NON-PERFORMING ASSETS OF INDIAN COMMERCIAL … · · 2016-07-28Gross NPA and Net NPA ... account...

International Journal of Applied Financial Management Perspectives Volume 1, Number 1, July -September 2012

© Pezzottaite Journals, Jammu & Kashmir, India. 1 | P a g e

NON-PERFORMING ASSETS OF INDIAN COMMERCIAL BANKS –

EMERGING ISSUES AND CHALLENGES

R. K. Uppal1 Amit Juneja2

ABSTRACT

Indian banking system is going through a transitional stage where winning the competition is the only key to success and

efficiency and any threat to this efficiency or success should be removed immediately. Non-performing assets of the banks are

just like a threat to the banking industry which can cause damage to the working and efficiency of a bank or bank group.

Keeping this thing in mind, a comparative study of different bank groups is done w.r.t. their NPAs of different sectors. For

that the entire banking industry is divided into four groups namely public sector banks, old private sector banks, new private

sector banks and foreign banks. The study finds that foreign banks and new private sector banks are most successful in

controlling their NPAs of different sectors during the study period of 2009 to 2011, whereas public sector banks are least

successful in this area.

KEYWORDS

Non-Performing Assets, Gross NPAs, Net NPAs, Priority Sector, and Non-Priority Sector etc.

I. INTRODUCTION

Banking in India has a long history and it has evolved over the years passing through various phases. The Indian banking system

has undergone noteworthy transformation following financial sector reforms, and at present it is passing through a decisive phase.

It is adopting international best practices in the area of regulation and supervision with a view to strengthening the banking sector.

According to Report on Currency and Finance (2006-08), with a view to create a strong, competitive and vibrant banking system,

several measures were initiated in the beginning of early 1990s. The banking system witnessed reforms such as introducing

prudential norms; allowing entry of new private sector banks and enhanced presence of foreign banks; permission to access the

capital market, operational flexibility and functional autonomy to public sector banks; strengthening of corporate governance

practices and disclosure standards. The initial mandate that banks were given was to expand their branch network, increase the

savings rate and extend credit to the rural and SSI sectors. This mandate has been achieved admirably. Since the early 90’s the

focus has shifted towards improving quality of assets and better risk management. The ‘directed’ lending approach has given way

to more market driven practices. The Narasimhan Committee has recommended prudential norms on income recognition, asset

classification and provisioning. In a change from the past, Income recognition is now not on an accrual basis but when it is

actually received. Past problems faced by banks were to a great extent attributable to this situation.

Banks have increasingly diversified into non-traditional activities, and as a result several conglomerates have emerged. Thus

deregulation has opened up new avenues for banks to augment income; it has also exposed the sector to greater risk of

nonperforming assets. It is obvious as a result of banking sector reforms the opportunities and challenges in the banking sector has

augmented. Classification of what an NPA is has changed with tightening of prudential norms. Currently an asset is “non-

performing” if interest or installments of principal due remain unpaid for more than 180 days.

Gross NPA and Net NPA

Gross NPA is advance which is considered irrecoverable, for bank has made provisions, and which is still held in banks' books of

account. Net NPA is obtained by deducting items like interest due but not recovered, part payment received and kept in suspense

account from Gross NPA.

The Reserve Bank of India states that, compared to other Asian countries and the US, the gross non-performing asset figures in

India seem more alarming than the net NPA figure. The problem of high gross NPAs is simply one of inheritance. Historically,

Indian public sector banks have been poor on credit recovery, mainly because of very little legal provision governing foreclosure

and bankruptcy, lengthy legal battles, sticky loans made to government public sector undertakings, loan waivers and priority

sector lending. Net NPAs are comparatively better on a global basis because of the stringent provisioning norms prescribed for

banks in 1991 by Narasimham Committee.

In India, even on security taken against loans, provision has to be created. Further, Indian Banks have to make a 100 per cent

provision on the amount not covered by the realizable Value of securities in case of ''doubtful'' advance, while in some countries;

it is 75 per cent or just 50 per cent. The ASSOCHAM Study titled ―Solvency Analysis of the Indian Banking sector reveals that

on an average 24 per cent rise in net non performing assets have been registered by 25 public sector and commercial banks during

the second quarter of the 2009 as against 2008. According to the RBI, "Reduction of NPAs in the Indian banking sector should be

treated as a national priority item to make the system stronger, resilient and geared to meet the challenges of globalization. It is

necessary that a public debate is started soon on the problem of NPAs and their resolution. "

1Principal Investigator & HOD, Department of Economics, D.A.V. College, Punjab, India, [email protected] 2Research Scholar, Department of Economics, Punjab University, Punjab,India, [email protected]

mailto:[email protected]




Nonperforming assets as a major issue and challenge for banking industry in India

Non-performing Assets are threatening the stability and demolishing bank„s profitability through a loss of interest income, write-

off of the principal loan amount itself. RBI issued guidelines in 1993 based on recommendations of the Narasimham Committee

that mandated identification and reduction of NPAs to be treated as a national priority because NPA direct toward credit risk that

bank faces and its efficiency in allocating resources. Profitability and earnings of banks are affected due to NPA numbers. In

recent years financial reform led by RBI has helped in reducing NPA numbers.

Table-1

Causes of Problem Mechanisms used to solve the problem

1. Legal impediments and time consuming nature of asset

disposal process.

1. Strengthening of Legal Norms.

2. Manipulation by the debtors using political influence has

been a cause for industrial bad debt being so high.

2. Aligning of prudential norms with international Standards.

3. Political tool - Directed Credit to SSI and Rural sectors. 3. Legal mechanisms including creation of ARCs and partial

disbanding of the BIFR.

The banking sector has been facing the serious problems of the rising NPAs. In fact PSBs are facing more problems than the

private sector banks and foreign banks. The NPAs in PSBs are growing in comparison to other banks due to external as well as

internal factors. One of the main causes of NPAs in the banking sector is the Directed loans system under which commercial

banks are required to supply 40% percentage of their credit to priority sectors. Most significant sources of NPAs are directed

loans supplied to the ―micro sector are problematic of recoveries especially when some of its units become sick or weak. PSBs 7

percent of net advances were directed to these units. Poverty elevation programs like Integrated Rural Development Program

(IRDP), Jawahar Rojgar Yojna (JRY), Prime Minister’s Rozgar Yojna (PMRY) etc., have failed miserably in meeting the

objectives. Due to Political interference, manipulation, misuse of fund by & and unreliable customer the amount issued these type

of schemes has become unrecoverable by and large.

The major cause for the NPA can be attributed to:

Improper selection of borrower’s activities;

Weak credit appraisal system;

Industrial problem;

Inefficiency in management of borrower;

Slackness in credit management & monitoring;

Lack of proper follow up by bank;

Recession in the market;

Due to natural calamities and other uncertainties.

SCHEME OF THE PAPER

The plan of research report has been framed under six sections:-

Section-I gives the introduction of the problem taken for study.

Section -II deals with review of related literature.

Section -III objectives, data base, statistical techniques and research methodology.

Section -IV deals with the analysis and interpretation of data.

Section-V deals with conclusions and implications of the study.

Section-VI deals with future areas of research.

II. REVIEW OF LITERATURE

Usha Arora, Bhavna Vashisht & Monica Bansal (2009) analyzed and compared the performance (in terms of loan disbursement

and non- performing assets) of credit schemes of selected banks for the last five years. This paper is divided into two parts. In the

first part, bank-wise as well as year-wise comparisons are done with the help of Compound Annual Growth Rate (CAGR), mean

and standard deviation; and in the second part, a positive relationship is found between total loan disbursement and total NPA O/S

of selected banks with the help of a correlation technique. The study found a positive relationship between total loan disbursement

and total Non-Performing Assets Outstanding (NPA O/S) of selected banks.

Thomas P. Ferguson (2007) found that asset securitization is a burgeoning trend in Russia as companies burdened by poor credit

ratings seek access to capital at lower costs than they would be allowed in traditional equity or debt markets. Study indicates that

securitization of these bad loans has not occurred in Russia at the levels one might expect. This has been due to both a relatively

small amount of loans that under-perform as well as legal and regulatory impediments that have discouraged investors and lenders

alike. The study has been conducted to examine the expansion of consumer credit in Russia and the circumstances under which it

is occurring indicate that the level of non-performing loans is due to rapidly increase and as the rationale for maintaining the

impediments that stand in the way of securitizing these loans is being re-examined, those impediments are being scaled back to



make way for market participants to engage in such securitizations. Thus, this article anticipates a significant rise in the level of

non-performing loans, which will be logically paired with an increased interest of Russian lenders in securitizing these assets.

Hu et al (2006) analyze the relationship between NPLs and ownership structure of commercial banks in Taiwan with a panel data

set covering the period 1996-1999. The study shows that banks with higher government ownership recorded lower non-

performing loans. The author also shows that bank size is negatively related to NPLs while diversification may not be a

determinant.

Fofack (2005) using a pseudo panel-based model for several Sub-Saharan African countries, finds evidence that economic

growth, real exchange rate appreciation, the real interest rate, net interest margins, and inter-bank loans are significant

determinants of NPLs in these countries. The author attributes the strong association between the macroeconomic factors and non-

performing loans to the undiversified nature of some African economies.

Isaac K. Otchere (2005) conducted a study on the performance of privatized banks in middle- and low-income countries shows

mixed results by “Competitive and Value Effects of Bank Privatization in Developed Countries”. The paper observed that private

banks in developed countries have experienced significant improvements in operating performance. The improvement in

performance remains significant after controlling for persistence in bank performance. A comparison of the performance of

privatized banks in developed and developing countries suggests that privatization has encouraged excessive risk taking among

privatized banks in developing countries, with the consequence that those banks carry large non-performing assets than their

counterparts in the developed countries. They also observe that consistent with the competitive effects hypothesis, investors view

privatization announcements as foreshadowing bad news for rival banks.

Jimenez and Saurina (2005) examine the Spanish banking sector from 1984 to 2003; they provide evidence that NPLs are

determined by GDP growth, high real interest rates and lenient credit terms. This study attributes the latter to disaster myopia,

herd behaviour and agency problems that may entice bank managers to lend excessively during boom periods.

Tamal Datta Chaudhuri (2005) indicates that declining capital adequacy adversely affects shareholder value and restricts the

ability of the bank/institution to access the capital market for additional equity to enhance capital adequacy. So, if a resolution

strategy for recovery of dues from NPAs is not put in place quickly and efficiently, these assets would deteriorate in value over

time and little value would be realized at the end, except may be its scrap value. The purpose of this paper is to indicate the

various considerations that one has to bear in mind before zeroing on a resolution strategy and provides a State - Resolution -

Mapping (SRM) framework. However, the paper has not specifically discussed about the various resolution strategies that could

be put in place for recovery from NPAs, and in particular, in which situation which type of strategy should be adopted.

Amitabh Joshi (2003) found that Profitability and Viability of Development Financial Institutions are directly affected by quality

and performance of advances. The basic element of Sound NPA Management System is quick identification of Non-performing

advances, their containment at minimum levels and ensuring that their impingement on the financials is at low level. Excessive

reliance on Collaterals has led Institutions to long drawn litigations and hence it should not be sole criteria for sanction. Banks

should manage their exposure limit to few borrower(s) and linkage should be placed with net owned funds for developing control

over high leverages of borrower level. Study also revealed that exchange of credit information among banks would be immense

help to them to avoid possible NPAs. Management Information system and Market intelligence should be utilized to their full

potential

Rajan and Dhal (2003) utilize panel regression analysis to report that favourable macroeconomic conditions (measured by GDP

growth) and financial factors such as maturity, cost and terms of credit, banks size, and credit orientation impact significantly on

the NPLs of commercial banks in India.

Bercoff et al (2002) examine the fragility of the Argentinean Banking system over the 1993-1996 period; they argue that NPLs

are affected by both bank specific factors and macroeconomic factors. To separate the impact of bank specific and

macroeconomic factors, the authors employ survival analysis.

Prashanth K. Reddy (2002) in his research paper examined the similarities and dissimilarities, remedial measures. Financial sector

reform in India has progressed rapidly on aspects like interest rate deregulation, reduction in reserve requirements, barriers to

entry, prudential norms and risk-based supervision. The study reveals that the sheltering of weak institutions while liberalizing

operational rules of the game is making implementation of operational changes difficult and ineffective. Changes required to

tackle the NPA problem would have to span the entire gamut of judiciary, polity and the bureaucracy to be truly effective. This

paper deals with the experiences of other Asian countries in handling of NPAs. It further looks into the effect of the reforms on

the level of NPAs and suggests mechanisms to handle the problem by drawing on experiences from other countries.

Rituparna Das (2002) performed a research on Managing the Risk of Non Performing Assets in the Small Scale Industries in

India. In this article the researcher tries to seek a solution to the problem of NPA in the small scale industries under the present

circumstances of banking and insurance working together under the same roof. What is stressed in this article is the pressing need

of the small-scale entrepreneur for becoming aware and educated in modern business management holding a professional attitude

toward rational decision making and banks have to facilitate that process as a part of the credit policy sold by them.



Salas and Saurina (2002) using a dynamic model and a panel data set covering the period 1985-1997 to investigate the

determinants of problem loans of Spanish commercial and saving banks, reveal that real growth in GDP, rapid credit expansion,

bank size, capital ratio and market power explain variation in NPLs.

Keeton (1999) uses data from 1982 to 1996 and a vector auto regression model to analyze the impact of credit growth and loan

delinquencies in the US. It reports evidence of a strong relationship between credit growth and impaired assets. Specifically, the

author shows that rapid credit growth, which was associated with lower credit standards, contributed to higher loan losses in

certain states in the US. In this study loan delinquency was defined as loans which are overdue for more than 90 days or does not

accrue interest.

Sinkey and Greenwalt (1991) investigate the loan loss-experience of large commercial banks in the US; they argue that both

internal and external factors explain the loan-loss rate (defined as net loan charge offs plus NPLs divided by total loans plus net

charge-offs) of these banks. These authors find a significant positive relationship between the loan-loss rate and internal factors

such as high interest rates, excessive lending, and volatile funds. They report that depressed regional economic conditions also

explain the loss-rate of the commercial banks. The study employs a simple log-linear regression model and data of large

commercial banks in the United States from 1984 to 1987.

Keeton and Morris (1987) present one of the earliest studies to examine the causes of loan losses. In the latter paper the authors

examined the losses by 2,470 insured commercial banks in the United States (US) over the 1979-85. Using NPLs net of charge-

offs as the primary measure of loan losses the authors shows that local economic conditions along with the poor performance of

certain sectors explain the variation in loan losses recorded by the banks. The study also reports that commercial banks with

greater risk appetite tend to record higher losses.

Research Gap

Banking system of any country is the backbone of the growth and development process of that country. Whenever the country

needs investments for speeding the rate of economic growth, the banking sector is always there to help the country. Banking

sector provides financial help to the country from its resources and these resources have come from its profits and profitability

that it earns from its loans and deposits. The banks are successfully meeting the challenge of providing service to its customers,

but the biggest challenge before them it management of NPAs. The soaring NPAs have adverse impact upon the progress of any

economy, and hence a matter of great concern for the Indian financial system. In this background, the present research paper

strives to examine the state of affair of the NPAs in the Indian banking system.

III. OBJECTIVES OF STUDY

To study and compare priority sector NPAs of different bank groups.

To study and compare non-priority sector NPAs of different bank groups.

To study and compare total NPAs of different bank groups.

Data Base

Secondary data had been used in the present study.

Report on Trend and Progress of Banking in India, RBI Publications, Mumbai 2009 to 2011.

Statistical Techniques

For the analysis of data arithmetic mean and percentage statistical techniques were used. Percentage increase was calculated by

using the following formula:

𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑌𝑒𝑎𝑟

𝐵𝑎𝑠𝑒 𝑌𝑒𝑎𝑟 X 100

RESEARCH METHODOLOGY

Research Design

A descriptive conclusion research design was used for the present study. A comparative study has been conducted about the

performance of various bank groups on their non performing assets during the study period to know up to what extent they are

successful in reducing their non performing assets.

Sample Design

The present study is concerned with Indian Banking Industry in whole and it is further divided into four bank groups to compare

their performance in terms of selected parameters.



SBI & its associates and Nationalized Banks known as Public Sector Banks- G-I.

Old Private Sector Banks- G-II.

New Private Sector Banks-G-III.

Foreign Banks-G-IV.

Time Period

Time period for the present study will be taken from 2009 to 2011. This time period is deliberately taken because with the entry of

new private sector banks and foreign banks in Indian banking industry, the banking business has become more competitive and

only those banks or bank groups are successful in this competitive environment which are able to decrease their non performing

assets. So, with this view, this study is taken in hand for the above said time period.

Parameters of the Study

In the present study, the analysis and comparison of the performance of various bank groups is done with reference to the

following parameters:

1. Priority sector NPAs of SCBs

i. of which Agriculture,

ii. of which Micro and Small Enterprises,

iii. of which Others.

2. Non-Priority Sector NPAs of SCBs

3. Total NPAs SCBs

IV. FINDINGS AND DISCUSSIONS

The major findings of this research and the analysis of data is shown the following tables:

Table-1: Comparison between different bank groups w.r.t. Priority Sector NPAs

(Amount in Crore)

Bank Group 2009 2010 2011 % Growth Rank

G-I 24318 30848 41245 0.69 3

G-II 1233 1613 1599 0.30 1

G-III 2407 3179 3224 0.34 2

2G-IV 649 1170 1141 0.76 4

Source: Report on Trends and Progress of banking in India: 2008-11.

Table1 shows comparison between different bank groups w.r.t. priority sector non-performing assets during the period 2009 to

2011. From the analysis of the table, it is clear that in case of the entire bank groups priority sector NPAs has increased during the

study period. But the priority sector NPAs of old private sector banks has increased to a least percent and priority sector NPAs of

Foreign banks has increased to a maximum percent as compared to other bank groups. Performance of New private sector banks

is also very close to Old Private sector banks as their priority sector NPAs increased to 0.34pc. Public sector bank’s performance

is very close to the performance of foreign banks as their priority sector NPAs increased to 0.69pc. But when we analyze the table

from another aspect, we find that there is continuous rise in priority sector NPAs in case of public sector banks and new private

sector banks. But in case of old private sector banks and foreign banks, their priority sector NPAs rises in 2010 as compared to

2009 and then fall in 2011, which is a good sign for them.

Table-2: Comparison between different bank groups w.r.t. Priority Sector NPAs to Agriculture

(Amount in Crore)


G-I 5708 8330 14487 1.54 3

G-II 263 269 417 0.59 2

G-III 1178 1754 1755 0.49 1

G-IV - - 0.1 - -


Table2 shows comparison between different bank groups w.r.t. priority sector NPAs especially to agriculture during the period

2009 to 2011. From the analysis of the table it is clear that NPAs of new private sector banks to agriculture have increased least as

compared to other bank groups where as these NPAs increased to a maximum in case of public sector banks. These NPAs of

foreign banks have just emerged in 2011 and are of Rs. 10 lacs. Increase in these NPAs of old private sector banks are close to

new private sector banks. The table concludes that public sector banks are more prone to this NPA problem in case of agriculture

as compared to other bank groups. Though in case of the entire bank groups there is an increase in these NPAS.



Table- 3: Comparison between different bank groups w.r.t. Priority Sector NPAs to Micro and Small Enterprises

(Amount in Crores)


G-I 6984 11537 10424 0.49 1

G-II 307 475 551 0.79 3

G-III 363 664 746 1.06 4

G-IV 220 299 352 .060 2


Table3 shows comparison between different bank groups w.r.t. priority sector NPAs especially to Micro and Small enterprises

during the study period of 2009 to 2011. From the analysis of the table it is clear that NPAs of public sector banks to priority

sector especially to micro and small enterprises have increased least during the study period whereas percentage rate of growth of

these NPAs during the study period is maximum in case of new private sector banks. Performance of foreign banks can also be

termed as satisfactory in this area. The table reveals that all the bank groups are not successful in decreasing these NPAs during

the study period, but public sector banks are successful in maintaining a decent rate of increase whereas new private sector banks

completely failed in this area.

Table-4: Comparison between different bank groups w.r.t. Priority Sector NPAs to others

(Amount in Crore)


G-I 11626 10981 12417 0.07 3

G-II 663 869 631 -0.05 2

G-III 866 760 722 -0.17 1

G-IV 429 871 789 0.84 4


Table4 shows comparison between different bank groups w.r.t. priority sector NPAs to other sectors during the study period 2009

to 2011. From the analysis of the table it is clear that old private sector banks and new private sector banks are the only two bank

groups which are successful in decreasing these NPAs and among these two bank groups, new private sector banks are more

successful as their rate of decrease is more than old private sector banks. Performance of public sector banks is also satisfactory.

Though these NPAs has increased of public sector banks during the study period, but their rate of increase is very less. Foreign

banks really disappoints in this area as they are not at all successful in controlling their NPAs. During the study period, these

NPAs has increased from 429 crore to 789 crore with a percentage growth rate of 0.84pc which is really high and shows their

inefficiency in controlling their NPAs.

Table-5: Comparison between different bank groups w.r.t. Non- Priority Sector NPAs

(Amount in Crores)


G-I 19251 25929 29803 0.55 4

G-II 1839 1999 2094 0.14 3

G-III 11334 10594 11053 -0.02 2

G-IV 6506 3956 3924 -0.40 1


Table5 shows comparison between different bank groups w.r.t. non-priority sector NPAs during the study period of 2009 to 2011.

From the analysis of the table, it is clear that foreign banks and new private sector banks are the only two bank groups which are

successful in decreasing their non-priority sector NPAs and between these two bank groups foreign banks are more successful as

their rate of decrease in these NPAs is more than new private sector banks. Old private sector banks and public sector banks are

not successful in controlling these NPAs and among these two bank groups the performance of public sector banks is most

disappointing as its non-priority sector NPAs as increased from 19251 crore to 29803 crore during the three years of study period.

Table-6: Comparison between different bank groups w.r.t. total NPAs

(Amount in Crore)


G-I 44042 57301 71047 0.61 4

G-II 3072 3612 3694 0.20 3

G-III 13815 13772 14277 0.03 2

G-IV 7155 7125 5065 -0.29 1


Table6 shows comparison between different bank group groups w.r.t. their total NPAs during the period 2009 to 2011. From the

analysis of the table it is clear that foreign bank’s group is the only bank group which is successful in decreasing their total NPAs.

Performance of new private sector banks is also very good. But old private sector banks and public sector banks are not successful

in controlling their NPAs and out of these two bank groups’ public sector bank’s performance is most disappointing as their total



NPAs has increased from 44042 crore o 71047 crore in just three years of the study period. This is a big alarming sign for the

survival of this bank group.

Table-7: Comparison between different Bank Groups w.r.t. their NPAs

Parameter Bank Group

G-I G-II G-III G-IV

1. Priority sector NPAs 3 1 2 4

2. Priority sector NPAs to Agriculture 3 2 1 -

3. Priority sector NPAs to Micro and Small Enterprises 1 3 4 2

4. Priority sector NPAs to others 3 2 1 4

5. Non-Priority Sector NPAs 4 3 2 1

6. Total NPAs 4 3 2 1

Total 1st Rank Achieved 1 1 2 2

Table7 shows comparison between different bank groups w.r.t. their NPAs to different areas. Table reveals that new private sector

banks and foreign banks are most successful in controlling their NPAs and there is also a tie between their performances. New

private sector banks top the chart in priority sector NPAs to agriculture and others where as foreign banks top the chart in non-

priority sector NPAs and total NPAs. Public sector banks are only successful in controlling their NPAs to micro and small

enterprises and old private sector banks in priority sector NPAs. So in nut shell, we can say that foreign banks and new private

sector banks are most successful bank groups in Indian banking industry as far as controlling of NPAs is concerned.

V. CONCLUSIONS AND IMPLICATIONS OF STUDY

Concluding Remarks

As far as the performance of public sector banks is concerned, it can be concluded that they are failed to perform well in

decreasing their NPAs rather all type of their NPAs have increased during the study period. In the field of NPAs to

other priority sector, their NPA percentage growth rate is minimum, but on all the other parameters they are completely

failed in managing and controlling their NPAs. Maximum percentage growth of their NPAs is in the field of agriculture

where their NPAs have increased from 5708 crore to 14487 crore in just three years with a percentage growth rate of

1.54pc. If these NPAs have increased in the near future at the same rate, it will adversely affect the functioning of the

public sector banks and render them inefficient.

As far as the performance of old private sector banks is concerned, it can be concluded that their performance is also not

very impressive as far as their NPA management is concerned. They are only successful in decreasing their NPAs to

other priority sector and on all the other sectors they are not successful in controlling their increasing NPAs. The

maximum percentage increase of NPAs is in the field of NPAs to micro and small enterprises where their NPAs have

increased fro 307 crore to 554 crore in just three years of the study period. If this bank really wants to gain profitability

and become efficient for its customers, it has to decrease its NPAs, so that it can make itself and its customers satisfied.

As far as the performance of new private sector banks is concerned, it can be concluded that this bank group does well

in decreasing its NPAs in two areas i.e. NPAs to other priority sector and non-priority sector NPAs and in case of total

NPAs its rate of increase is also very less and it some how successful in managing its NPAs. But the biggest

disappointment from this bank is in the area of priority sector NPAs to micro and small enterprises where its NPAs has

increased from 363 crore to 746 core in just three years of the study period. In other areas also it is performing only

satisfactory, though better than other bank groups, but it has to compete with itself to become more profitable and

efficient in its working and trying for decreasing their NPAs in these other fields also.

As far as the performance of foreign banks is concerned, it can be concluded that this bank group also does well on

some of the parameters like it is successful in decreasing its non-priority sector NPAs and total NPAs during the study

period which shows its efficient working. But on priority sector NPAs and other priority sector NPAs its performance is

not so good and there is an increase in NPAs in these areas. Priority sector NPAs to agriculture has just emerged in this

bank group. So, if this bank group continues to be more profitable and efficient for its customers and for itself also, it

has to make efforts in this field, so that it can make itself successful in decreasing its NPAs in various fields.

Conclusions

Survival of the fittest is an old saying. The same can be applied to banking industry. Profits and profitability are like health drinks

to the banking industry and non-performing assets are like a disease which can make banking industry sick. So, in order to

maintain the position and to grow, it is necessary for the banks to control their NPAs. From the above study, it can be concluded

that new private sector banks and foreign banks are the only two bank groups which are successful in fulfilling this condition of

their survival while old private sector banks and public sector banks are still struggling with this problem. So, in order to make

themselves efficient and profitable for the customers, it is necessary that they should properly take care this aspect of banking

otherwise it is impossible for them to survive in this competitive era.



Implications

The current study is mainly concerned with the analysis of comparative performance of the specific bank groups during the period

of 2009 to 2011 that reflects their performance in controlling their NPAs to different sectors. As the study reflects the number of

banks that have improved or declined their performance in respect of all the selected parameters, so provides important analysis to

judge the banks with poor performance which further help to make some policy measures to improve their performance. The

study will be more beneficial for the RBI, for the bankers and policy makers to make some important decisions and to make

policy measures to improve their performance. The study will be helpful to the academicians and researchers for further study in

this respect.

VI. FUTURE AREAS OF RESEARCH

Comparative study of individual banks for all selected parameters.

Performance evaluation of banks separately in domestic and abroad branches.

Comparative performance of segment-wise NPS in rural, semi-urban and metropolitan branches of each bank.

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4. Datta Chaudhuri, Tamal, (2005). “Resolution Strategies for Maximising Value of Non- Performing Assets (NPAs)”,

Available at SSRN: http://ssrn.com/abstract=871038.

5. Ferguson, Thomas P., (2007). “Observations on the Securitization of Non-Performing Loans in Russia”, Bucerius Law

Journal, Bucerius Law School, Hamburg, Germany, March 2008. Available at SSRN: http://ssrn.com/abstract=1017288.

6. Fofack, Hippolyte, (2005). “Non-performing loans in sub-Saharan Africa: Causal Analysis and Macroeconomic

Implications”, World Bank Policy Research Working Paper No. 3769, November.

7. Hu, Jin-Li; Yang Li, and Yung - Ho Chiu, (2006). “Ownership and Non-performing Loans: Evidence from Taiwan’s

Banks”, Developing Economies.

8. Jimenez, Gabriel, and Jesus Saurina, (2005). “Credit Cycles, Credit Risk, and Prudential Regulation”, Banco de Espana,

January.

9. Joshi, Amitabh, (2003). “Analysis of Non-Performing Assets of IFCI Ltd”, Available at SSRN:

http://ssrn.com/abstract=921860.

10. Keeton, William R., (1999). “Does Faster Loan Growth Lead to Higher Loan Losses?”, Federal Reserve Bank of

Kansas City, Economic Review, Second Quarter 1999.

11. Keeton, William, and Charles S. Morris, (1987). “Why Do Banks’ Loan Losses Differ?”, Federal Reserve Bank of

Kansas City, Economic Review, May, pp. 3-21.

12. Rajan, Rajiv, and Sarat C. Dhal., (2003). “Non-performing Loans and Terms of Credit of Public Sector Banks in India:

An Empirical Assessment”, Occasional Papers, 24:3, pp. 81-121, Reserve Bank of India.

13. Reddy, Prashanth K., (2002). “A comparative study of Non Performing Assets in India in the Global context -

similarities and dissimilarities, remedial measures”, Available at SSRN: http://ssrn.com/abstract=361322 or

doi:10.2139/ssrn.361322.

14. Report on Trend and Progress on Banking in India 2009-2011.

15. Salas, Vincente, and Jesus Saurina, (2002). “Credit Risk in Two Institutional Regimes: Spanish Commercial and

Savings Banks”, Journal of Financial Services Research, 22:3, pp. 203-224.

16. Sinkey, Joseph. F., and Mary B. Greenwalt, (1991). “Loan-Loss Experience and Risk-Taking Behvior at Large

Commercial Banks”, Journal of Financial Services Research, 5, pp.43-59.

*****

http://ssrn.com/abstract=1368624








TESTING OF RANDOM WALK BEHAVIOUR – AN EMPIRICAL STUDY WITH SPECIAL

REFERENCE TO SELECTED SECTORAL STOCKS IN BSE

M. S. Ramaratnam3 R. Jayaraman4 B. Balaji Srinivasan5

ABSTRACT

An evidence of random walk is tested with respect to Indian Stock market by selecting the script price belonging to three

major sectors namely banking, Information technology and pharma. It is normally believed that there is every possibility of

making superior return in the stock market by way of predicting the future price of the script on the basis of the past price and

thereby either in the means of buying or selling the script, the investor can earn abnormal return. But to contrast, some of the

research analysts firmly pose a faith on the market in such a way that when the market is said to be moving towards perfect,

future price cannot be predicted on the basis of historical movement of the script and that script movement is called as random

walk model. As far as Indian stock market is concerned, by unleashing several measures in the last one decade, a remarkable

achievement has been attained by way of faster settlement mechanism, transparency, script less trading etc., paved the way to

move towards efficiency. To test whether Indian stock follows random walk model or not, though a quiet number of research

has been done in this area over the period, the result of the analysis through the research was emerged with mixed reactions

showing at some point of time the market behaved as efficiency and many times the market showed inefficiency behaviour.

By taking into consideration of the above fact, this paper has made an attempt to test the market behaviour by taking selected

scripts of three major industries and apply run test and KS Test to verify the market behaviour during the period of 2006 to

2012.

KEYWORDS

Efficient Market Hypothesis, Share Price Behaviour, Random Walk Model, Run Test, and KS Test etc.

INTRODUCTION

Market efficiency is a puzz word in the context of any capital market. The efficiency of the market is attained if and only if the

price of the script moves independently without having influenced by the previous price. On the basis of the above statement, a

market can be classified into three ways in terms of efficiency as propounded by efficient market hypothesis (EMH). First of its

kind is weak form where the stock price move independently and the prediction of the future price is impossible on the basis of

past price and therefore the concept of technical analysis cannot be used as tool to predict the future. Second type is semi-strong

efficiency in which the price of the scripts reflects all publicly available information and the prices instantly change to reflect new

public information. The last type of efficiency is attributed to strong- form efficiency wherein the insider cannot influence the

price by way of getting insider information because the price additionally reflects the insider information and even in this situation

fundamental analysis too can’t be used to predict the future price of the script based on the fundamentals of the company. As long

as the market is so efficient, the investor cannot earn excess return in the long run by using investment strategies based on

historical stock prices or other historical data. In the efficient market the price of the script does not follow any trend or pattern. If

the market is not efficient, the investors will beat the market and attain maximum profits. Participants in an efficient market can

use various devices such as trading rules and statistical techniques to predict the movement of stock prices. The aspect of market

efficiency has become more interesting when the analysis is carried in the context of any national stock market in the eve of

globalization which has brought financial returns in the market. Since India has undergone many changes in the face of the stock

market during the last one decade. The financial analyst and researchers are interested in finding the efficiency of the market with

respect to Indian context.

REVIEWOF LITERATURE

A number of studies have been made to analyse the weak form of efficiency at different levels over the period of the years. If we

look at the result of the study, the study revealed mixed opinion with respect to the existence of randomness in the stock return

over various phases of the market. Few of them are given as citation.

Cooper (1982) took a sample of 36 countries and employed monthly, weekly and daily data of stock market to validate the

random walk model through correlation analysis and run test. It was found that except UK & US other markets showed a result in

consistent with random walk model.

Kendall (1953) & Fama (1965) found existence of low degree of correlation in terms of price behavioural change with respect to

weak form of efficiency.

Granger & Morgenstern (1970) analysed the sample of 5 stocks at various interval of time to test random walk theory. In that

short term stock prices witnessed a deviation from random walk model.

3Assistant Professor (SG), Faculty of Management Studies, SCSVMV University, Tamil Nadu, India, [email protected] 4Assistant Professor, Faculty of Management Studies, SCSVMV University, Tamil Nadu, India, [email protected] 5Head, Faculty of Management Studies, SCSVMV University, Tamil Nadu, India [email protected]






Harvey (1995) studied the return of market and the power of predictability with respect to six Latin American countries, Eight

Asian countries and Two African countries and it was found that strong correlation in the stock return series.

Dasgupta & Glen (1995) found that operational inefficiency reflected in price behavioural pattern so that the price inefficiency

was caused in nineteen emerging market, made the equity return of the market to be dependent.

With respect to the Indian market many number of researchers supported to witness the evidence of weak form efficiency, Barja

(1981, 1987), Rao & Mukerjee (1971), Sharma & Kennedy (1977), Gupta (1985), Ramachandran (1985) , Dhankar (1991) found

through their contribution that Indian capital market obeyed the rule of weak form efficiency.

Bhaumita (1997) took sensex as the barometer of the Indian Capital Market to prove stock prices closely represent a random

variable.

Mohanty (2001) documented the evidences in favour of random walk model and in his study he considered sensex, BSE 100 and

BSE 200 for the period of five years from 1996 to 2001. The result of the study supported the null hypothesis of Unit root test but

the variance ratio test was rejected.

Gupta & Basu (2007) tested two major equity markets in India for the period of 1991 to 2006 and found that the market series did

not follow the random walk model and there was an evidence of auto correlation in both markets rejecting the weak form of

efficiency.

Samantha (2004) studied BSE 100 for a time period of seven years from January 1993 to December 2001. Till July 1996 the

market showed remarkable inefficiency and high level of efficiency was shown during July 1996 to December 1999.

Verma & Rao (2007) examined the companies included in BSE 100 and applied serial correlation and unit root for a period of

three years from 1998 to 2001 and found that mixed results have been emerged in the market concerned.

Renuka Sharma & Ramesh Chander (2011) studied the market proxies of BSE such as Sensex, BSE 100 and BSE 500 to verify

whether the market proxies have followed random walk model and it was found that the result of unit root test and auto

correlation has differed during the different phases of the study.

OBJECTIVES OF STUDY

To test whether the successive script value changes are independent or not.

To test the stock market efficiency at weak form in the Indian stock market with special reference to select scripts.


The present study is based on the secondary data related to daily closing figures of various select scripts belonging to different

sectors over the period from 2nd January 2006 to 31st December 2011. The select script price has been taken on the basis of the

effective performance of the sectors viz., Banking, IT & Pharma. The data is taken from Yahoo Finance. The returns using the

closing prices are computed using the first differences of natural logs of prices.

Table-1.1: Details of Selected Sample Units for Testing the Market Efficiency

S. No. Sector Company script S. No. Sector Company script

1 Banking State bank of India 6 Pharma Dr Redy’s labs

2 Banking Bank of Baroda 7 Information technology TCS

3 Banking Punjab national bank 8 Information technology HCL

4 Pharma Piramal health care 9 Information technology Wipro

5 Pharma Cipla

Non-Parametric Tests

Run Test

Test is to detect statistical dependencies (randomness) which may not be detected by the autocorrects test. The null hypothesis is

that the observed series use random variable. The number of runs is computed as a sequence of the price changes of the same sign

(such as; + +,--, 00). When the expected number of runs is significantly different from the observed number of runs, the test

rejects the null hypothesis. A lower than expected number of runs indicates the market’s over-reaction to information,

subsequently reversed, while higher numbers of runs reflect a lagged response to information. Either situation would suggest an

opportunity to make excess returns. Under the null hypothesis the successive outcomes are independent and assuming that N1 >

10 and N2 > 10.



The number of runs is asymptotically normally distributed with:

Mean E(R) = (2N1N2/N) + 1,

Variance = 2N1N2 (2N1N2 – N) / (N^2) (N -1),

Where N = total number of observations,

N1 = number of ‘+’ symbols,

N2 = number of ‘–‘symbols,

R2 = number of runs.

The run test converts the total number of runs into a Z statistic. For large samples the Z statistic gives the probability of difference

between the actual and expected number of runs. If the Z value is greater than or equal to ±1.96, the null hypothesis will be

rejected at 5 percent level of significance.

Kolmogorov-Smirnov (K-S) Test

The K-S test was originally proposed in the 1930s [Kanji 1999]. K-S is one of the best known and most widely used goodness-of-

fit tests. It is based on the empirical distribution function and converges uniformly to the population cumulative distribution

function with probability measure one. The one sample K-S test procedure compares the observed cumulative distribution

function for a variable with a specified the theoretical distribution which may be normal, uniform, Poisson or exponential. The K-

S Z is computed form the largest difference (in absolute value) between the observed and theoretical cumulative distribution

functions. This goodness-of-fit test checks whether the observations could reasonable have come from the specified distribution.

Under null hypothesis that the sample comes from the hypothesized distribution F(x),

In distribution, where B(t) is the Brownian bridge.

If F is continuous then under the null hypothesis converges to the Kolmogorov distribution, which does not depend on F.

This result may also be known as the Kolmogorov theorem. The goodness-of-fit test or the Kolmogorov – Smirnov test is

constructed by using the critical values of the Kolmogorov distribution. The null hypothesis is rejected at level if:

Where Kα is found from:

The asymptotic power of this test is 1.

Empirical Analysis

The empirical results are presented as below:

Run Test

Table-1.2: Showing Run Test on Select Samples

Script

Name

Total number

of Runs

Z-Test Assy. Sig

(2-tailed)

Script

Name

Total number

of Runs

Z-Test Assy. Sig

(2-tailed)

SBI 741 -1.22 0.02 DR REDYS LABS 771 1.0 0.02

BOB 755 0.74 0.04 TCS 733 -0.90 0.03

PNB 740 -0.056 0.05 HCL 779 1.4 0.14

PHC 747 -0.99 0.03 WIPRO 769 0.27 0.17

CIPLA 808 3.4 0.01

Significant at 5 percent level of significance

Source: Computed Data.

The results of the run test are presented in Table 1.2. It is evident from the table that the Z-statistics, which have been computed to

test the significance of the difference between the number of actual runs and the expected runs, are found significant in case of the

script return of SBI, Bank of Baroda, Punjab National Bank, Piramal Health care, Cipla, Dr Redy’s labs, Wipro, HCL

Technologies and TCS. Since the number of actual runs and the expected runs are found to be significant in the selected samples,

it makes clear that the script prices are not moving independently and thereby the random walk model has not been found in

Indian stock market with special reference to select scripts.

http://en.wikipedia.org/wiki/Convergence_of_random_variables

http://en.wikipedia.org/wiki/Brownian_bridge

http://en.wikipedia.org/wiki/Statistical_power



Kolmogorov-Smirnov Test

The K-S Z-statistics has been used to test that the normality of frequency distribution of the underlying series. The results are

presented in Table 1.3. Thus, the K-S test confirms that the frequency distribution of the return series of the SBI, Bank of Baroda,

Punjab National Bank, Piramal Health care, Cipla, Dr Redy’s labs, Wipro, HCL Technologies and TCS scripts does not fit the

normal distribution. Thus, the findings of the K-S Z-statistics makes further clear that the selected scripts do not follow random

walk model as basic assumption of random walk model is that return series should be normal.

Table-1.3: Estimates of Kolmogorov-Smirnov Test

Script name Absolute Positive Negative K – S (Z) Significance

SBI 0.057 0.057 -0.049 2.247 0.000

BOB .271 0.255 -0.271 10.421 0.000

PNB .059 0.058 -0.059 2.273 0.000

PHC 0.081 0.081 -0.074 3.158 0.000

CIPLA .220 0.199 -0.220 8.46 0.000

DR REDYS LABS 0.117 0.108 -0.117 4.521 0.000

TCS 0.144 0.123 -0.149 5.58 0.000

HCL 0.101 0.088 -0.101 3.89 0.000

WIPRO 0.080 0.078 -0.80 3.12 0.000

Note: The null hypotheses of normal distribution of the underlying variables are rejected at 1 percent level of significance.

Source: Computed Data.

CONCLUSIONS

A study has been made to examine the stock return behaviour of select scripts belonging to different sectors in the context of

Indian stock market. The behaviour of stock market has been tested in the proxy form of taking select scripts with the help of

statistical tools like Run test and K-S test. The analysis of the share price behaviour indicated that the tests used in this study

reject the hypothesis of random walk for all the select scripts. This reflects that the Indian stock market did not have the reference

of random walk model despite the effects taken by SEBI towards improving the functioning and transparency of the stock market.

It seems that certain anomalies still exist in the Indian stock market which may cause the market to be inefficient.

REFERENCES

1. Cooper, J. C. B., (1982). “World Stock Market: Some Random Walk Tests”, Applied Economics, Vol.14, PP 515 – 531.

2. Fama, Eugene, (1965). “The Behaviour Of Stock Market Prices”, Journal of Business, 38, pp 34 – 105.

3. Garnger, C. W. I., and Oskar Morgenstern, (1970). “What the Random Walk Model Does Not Say”, Financial Analysis

Journal, Vol.26, No.3, pp 91 – 93.

4. Harvey, C. R., (1995). “Predictable Risk and Return in Emerging Markets”, Review of Financial Studies, Vol. 8, No.3,

pp 773 – 816.

5. Kendall, M., (1953). “The Analysis of Economic Time Series’, Journal of The Royal Statistical Society, Series A, 96,

pp 13 – 32.

6. Mohanty, Pitabas, (2001). “Efficiency of the Market for Small Stocks”, NSE Research paper No.1.

7. Ramachandran, J., (1985). “Behaviour of Stock Market Prices, Trading Rules, Information & Market Efficiency”,

Doctoral Dissertation, Indian Institute of Management, Ahmadabad.

8. Rao, K. N., and Mukherjee. K., (1971). “Random-Walk Hypothesis: An Empirical Study”, Arthaniti, Vol.14, No.142.

9. Sharma, Renuka, and Ramesh Chander, (2011). “Market Proxies at BSE and Weak Form Efficiency”, Indian Journal of

Finance, Vol.V.No.3, pp 18 – 27.

10. Sharma, J. L., and Kennedy, Robert E., (1977). “A Historical Analysis of Market Efficiency: Do Historical Returns

Follow a Random Walk”, Journal of Financial and Strategic Decisions, pp 10.

11. Ramaratnam, M. S., and R. Jayaraman, (2011). “A Study on Testing of Efficient Market Hypothesis with Special

Reference to Selective Indices in the Global Context: An Empirical Approach”, Researchers World – An International

Journal of Arts, Science And Commerce, Vol– II, Issue –1, pp 17 – 32.

12. Ramaratnam, M. S., & R. Jayaraman, and K. N. Ganapathy, (2011). “An Empirical Event Study On Possibilities To

Earn Superior Risk Adjusted Return By Trading On Earning Announcement With Special Reference To Selected

Stocks”, Indian Journal of Finance, Vol -V, Issue – 2.

*****



PERFORMANCE AND PROBLEMS OF URBAN CO-OPERATIVE BANKS IN INDIA

Shirish R. Kulkarni6 Chirag P. Surti7

ABSTRACT

Banking sector in India play a very important role for country’s economic growth. Banking companies include commercial

banks and co-operatvie banks. In banking sector major reforms have taken place particularly after 1991. Co-operative banks

are one which is facing the numbers of problems and such banks are unable to survive them against public sector, private

sector and foreign banks. The UCB’s are facing the problems of Non-performing assets since so many years which indicate

the performance of UCB’s in India. The numbers of UCB’s in India has declined is due to the increase in NPA and the RBI

directive imposed over UCB’s and large numbers of merger / amalgamation had taken place in the co-operative sector.

The NPA’s is also a parameter which shows the financial soundness of any banks. Here in this paper the authors try to analyze

the NPA’s of UCB’s and had taken the period from 2001 to 2011 for the study. However, RBI Report on trend and progress of

banking in India shows the data for gross NPA and Net NPA of UCB’s.

INTRODUCTION

The very word “CO-OPERATIVE” remaining in the design of Bank’s Structure, the entire fabric of Co-operative Banks carry in

the theme of co-operation. The meaning pregnant in word co-operative reflects the need for positive compromising strategy for

the activities of such co-operative organization. This base philosophy had assumed understandable prominence in the organic set

up of all co-operative sectors. Therefore only banking activities in co-operative sectors could not disassociate itself from said

theme of co-operation broadly meaning positive approach or compromise. Needless to mention that the financial sectors engaged

necessarily in utilization of money cannot afford positive attitude of an organic business strategy. While all stated here in above is

purely a philosophical expression.

It should not surprise anybody if the said theme of co-operation is revealed to be the root cause of sickness(NPA) in co-operative

Banks. These Banks since their inception had been enjoying legacy of management control in the select hands of either front line

community members or like minded group of people from inter related families. This situation coupled with the less regulated

conditions, unwanted political indulgences and such other evils had impacted the entire organizational set up of co-operative

Banks and many of them were thrown completely out of gear. This development that took place from 1970 to 2000 unfolded

several liquidations and crisis of confidence of depositors in the urban co-operative Banks.

By end large all the matters stated above rendered many lakhs of people from the middle class or lower middle class absolutely

help less in receiving back their hard earned and hard saved deposits from the UCB’s. Right from preposition of the assets

allocation by way of loans, advances, investment, creation of fixed assets, suspense accounts of other judicious expenditure till

actual deployments and realization everything is handled under the pretext of co-operation which actually aims at selfish motive

and accommodation and compromise on minimum fiscal discipline. Obviously, depositors money deployed like this is destined to

turn into potential NPA’s and regulatory looseness followed by liberal judiciary norms always induced master mind of certain

positions to milk the co-operative cows so vigorously till it runs out life and dies. This is evident from the under mentioned

statistical data which through light on the state of health of UCB’s.

Table-1: Total Amount of Gross NPA and NET NPA of UCB’s

6Professor, Department of Accounting & Financial Management, Faculty of Commerce, The M. S. University of Baroda, Gujarat,

India. 7Faculty Member, Department of Accounting & Financial Management, Faculty of Commerce, The M. S. University of Baroda,

Gujarat, India, [email protected]

End of

March

No of Reporting

UCB’s

Gross

NPA’s

Gross NPA’s as%

to Total Advances

Net

NPA’s

Net NPA’s as %

to Total Advances

2001 1942 9245 16.1 -

2002 1937 13706 21.9 -

2003 1941 12509 19.0 6428 13.0

2004 1926 15406 22.7 8242 12.1

2005 1872 15486 23.4 8257 12.5

2006 1853 13871 19.7 6718 9.6

2007 1813 14541 18.3 6235 8.8

2008 1770 14037 15.5 6083 7.7

2009 1721 13043 13.3 5318 6.1

2010 1674 11399 10.1 3821 3.9

2011 1645 11529 8.5 3130 2.5

Source: RBI Reports on Trend And Progress of Banking In India.




16.1

21.9

19

22.7 23.4

19.718.3

15.5

13.3

10.18.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

YEARS

GROSS NPA (%)

The above table indicative of growth of NPA’s both in quantity and as % to total credit between the years 2001 to 2011. It may be

noted that number of UCB’s has declined from 1942 to 1645 during this period. The amount of gross NPA’s has shot up from Rs.

9000 core to as high as Rs. 15000 core. In simple words, the growth in % terms is as high as 23% in the year 2005 but then after it

is continuously decline.

However, control and monitoring and compulsion of RBI to strict observance of provisions and norms resulted in the decline of

net NPA’s from 13% in the year 2003 to 2.5% in 2011. These facts clearly establish that co-operative banks have finally accepted

the need for making adequate profit through viable advances and still pursuing co-operative principles and when it comes to

recovery of advances and interest, they do not compromise on anything.

Graph-1

Therefore, better quality of appraisal of advances, strict monitoring and scrutiny, considered purely on merits improves the health

of their credit portfolio. These measures help them to make adequate profits and provide for and prevent advances / loans turning

as NPA. It is now hoped that financial discipline will help perfect co-operative banks as strong banks. RBI feels that merger of

weak banks is workable in theory and has produced very good result. This inspiration has been drawn from recent merged Global

Trust Banks with the Oriental Bank of Commerce and United Western Bank with IDBI.

All though these two are public and private sector banks, the salient features like ready network of branches with infrastructure,

broad customer base and ready to use experienced staff continued to remain common synergy even in co-operative banks.

Therefore merger and acquisition is the main theme played these days by RBI to revive health of many small weak co-operative

banks.

The cry of middle class people of having lost their hard saved money whenever co-operative banks turn sick or identified for

liquidation, created the need for more intense and critical audit of accounts, financial reporting system, norms for income

recognition and making provisions RBI came forward and ensured that depositors money remains well protected. This requires

observance of prudential norms and also more frequent audit and financial reporting. However, different legislatives and statutory

provisions of co-operative Act is still a big hurdle and become a handicap in raising standard of fiscal discipline in co-operative

banks.

It may be noted here that still the co-operative banks are not permitted to go to recovery tribunals under Securitization Act as they

do not have jurisdiction over co-operative banks. The above being position the option of merger and acquisition came in rescue of

efficiently managing sick and sinking co-operative banks. However, large numbers of weak co-operative banks are unable to find

adequate numbers of strong co-operative banks.

CONCLUSIONS

The merger and acquisition wave has finally hits the shores of co-operative banking. The RBI says the consolidation drive is

aimed at a non disruptive exit of weak UCB’s. But the problem is that more than 80% of the UCB’s are weak, while there are just

handfuls of potential acquirers.



Guidelines by RBI on Merger / Amalgamation

a) Reserve Bank of India may consider proposals for merger and amalgamation in the following circumstances:

(i) When the net worth of the acquired bank is positive and the acquirer bank assures to protect entire deposits of all the

depositors of the acquired bank.

(ii) When the net worth of acquired bank is negative and the acquirer bank on its own assures to protect deposits of all

the depositors of the acquired bank.

(iii) When the net worth of the acquired bank is negative and the acquirer bank assures to protect the deposits of all the

depositors of the acquired bank with financial support from the State Government extended upfront as part of the

process of merger.

b) In all cases of merger / amalgamation the financial parameters of the acquirer bank post merger should conform to the

prescribed minimum prudential and regulatory requirement for urban co-operative banks.

c) The realizable value of assets has to be assessed through a process of due diligence.

REFERENCES

1. Bhole, L. M., (2004). "Financial Institution and Markets", Tata Mc Graw Hill Publishing Company Ltd, New Delhi.

2. K. C. Mishra, “Regulatory and Supervisory Framework for UCB’s: Issues and Challenges”, Published in the CAB

Calling Special Issue vol. 29, N0 4 (October-December 2005).

3. www.nafcub.org

4. www.rbi.org.in

*****

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EVA - A STUDY OF SELECTED COMPANIES IN INDIA

Babli Dhiman8 Shipra Pruthi9

ABSTRACT

Economic Value Added (EVA) is an improved measure of checking the company performance so that the stakeholders can

decide that the company is generating or destroying their wealth. EVA is latest and modern measure technique to know the

efficiency of the companies that whether they are maximizing or reducing the value of shareholders wealth. Therefore this

paper is designed with an attempt to know that which companies is the wealth generator or destroyer for the shareholders. The

main objective of the study is to rank the companies on the basis of EVA.

Therefore a sample of fifty companies has been taken from NSE listed Companies for the period of 2005-2010 from Stock

Market and ranked according to EVA as wealth generating companies or wealth destroying companies as in our fifty sample

companies only three companies have disclosed Economic Value Added in their annual reports. This paper is mainly focuses

on the shareholders benefit so that they can take decision to continue their investment with the same companies or not.

KEYWORDS

Economic Value Added, Wealth Generator, Wealth Destroyer, and Rank etc.

INTRODUCTION

Maximization of Shareholder’s value has become the new corporate standard in India. Any company which gives less return to

their shareholder is considered as low standard company. In a country like India where capital is still costly, the corporate

management would try to get a maximum profit for every single buck of investment. EVA means economic value added, in

another words addition in the returns of shareholder. EVA is the difference between a company’s profit and the full cost of its

capital.

A company should not only seek to make a profit from its business – it should also make enough profit to cover the cost of its

capital, including equity invested by shareholders, which is very important for the survival of company. Idea of EVA has been

given by Stern Stewart & Co, a New York based global financial consultant. Most of the companies consider the returns but not

the entire cost they consider only the cost of debt and cost of preference shares, but they don’t consider the equity cost, whereas

equity share capital is also the cost which has to be considered. Most of the companies and shareholders believe in traditional

measures like return on equity and return on assets. But these methods are not capable enough to tell the true profitability of a

company because they don’t consider the equity as cost. Management considers Equity as a cost free capital. In this situation

shareholder returns are manipulative. Equity is a costly source of finance.

EVA: Evolution and Growth

EVA is not a new concept globally. It is based on residual concept that is calculated by deducting capital charges from the

operating profits. One variation between EVA and residual income is to know how to work out on return and cost to get

maximum return. Stern Stewart &Co. introduced this system in 1982.The list of some of the companies using EVA are Coca-

Cola, Eliy Lily Monsanto and others. Companies adopted EVA by number of ways the initial interest was introduced a few years

ago by a magazine article about EVA. Two senior executives came upon the article independently and sent it to each other. In

Mid 1996 Joel Stern was invited to give a full dress presentation on EVA to the top management, and later that year the company

signed up and formed an EVA steering Group for implementation EVA.

EVA in Indian Corporate Sector

Bennet Stewart and Joel Stern jointly founded Global Financial Consultancy firm based in New York under the name “Stern

Stewart Co”, This Company has a strong faith that EVA is a true economic measurement tool than any other financial

measurement tool. In a study till 2008 just 37 companies in India have disclosed EVA in their financial report. There are

irregularities in the study of EVA calculation so the companies avoid accepting EVA as performance measurement tool. EVA is

based on the concept that a successful firm should earn at least its cost of capital. Firms that earn higher returns than financing

costs enhance the wealth of shareholder.

The world is now a global economy and the size of business is increasing day by day. In order to compete in the business world

and become the best player, organization must have resources and complete skill to be best player in using these resources. Many

companies in the world are adapting EVA in these circumstances India is also not far behind for understanding and implementing

the concept.

8Associate Professor, Department of Management, Lovely Professional University, Punjab, India,[email protected] 9Lecturer, Department of Management, Continental Group of Institute, Punjab, India, [email protected]

http://www.economics-dictionary.com/definition/company.html

http://www.economics-dictionary.com/definition/profit.html

http://www.economics-dictionary.com/definition/cost.html

http://www.economics-dictionary.com/definition/capital.html

http://www.economics-dictionary.com/definition/equity.html





The corporate India should be fully equipped with solutions of ifs and buts of EVA not only for the reason of global competition

but also for their own long understanding and continues existence. Though the EVA technique is very simple to understand but it

is tricky to implement especially in a country where economic environment is in the process of alteration. Companies are trying to

implement EVA and are asked to incorporate many more changes to their present financial books in India. The equity capital is

not a free capital; rather it is expensive and risky. Any organization who is capable to generate profit for its investor can only

survive in this competitive world.

Limitations of EVA

Although EVA has benefited the shareholder more than the traditional measures still it has some limitations like EVA cycle does

not give any idea about the financial performance of companies which is affected by the business cycle. Possibility of error in

estimating WACC (Weighted Average Capital Cost) is an important aspect and the calculation of cost of equity is also very

difficult to compute.

With the help of NPV (Net Present Value) an individual project can be selected and rejected. It can happen that any project has

positive NPV and the company selects that project. But for the initial year the project can give negative NPV so the starting EVA

of the project can be negative so it can be a loss making project then it should not be taken but in future this project can give

higher positive cash flow. Fast moving goods are less capital intensive so these companies has more EVA than any other

company which is more capital intensive. This is the reason that inter-company comparison will get unrealistic.

REVIEW OF LITERATURE

Kroll (1997) depicted that a business can get success only when if it generates profit more than its cost of capital. A company

which implements economic value added shows a great improvement in its performance. Many companies have adopted

economic value added and improved its performance with the help of it. Economic value added also helps in acquisition of a

company because manager can know that what the value of a firm is?

Dodd & Chen (2000) said economic value added is the most important performance measure. EVA has important effect on stock

market. The study is about to prove the importance of EVA than any other traditional measures. Author used regression to prove

the relationship and find out that economic value added alone cannot be taken as performance measure. Every method has its own

importance for measuring the performance of the company.

Stewart (2003) expressed the implication of economic value added in Harsco Corporation. Stern has used four M for performance

measure under EVA system. The EVA Implementation at Harsco was structured using Stern Stewart’s Four Ms. Economic value

added used in this company for these four M which mean measurement, management, motivation and mindset. By using

economic value added Harsco started to perform well.

Beneda (2004) has worked about the company named Toll Brothers performance, which was in the home building industry. The

study shows that the performance of the company increases after applying the economic value added into practice. Here market

value added is also calculating with the help of difference between the book value of company’s assets in place and the overall

value of the firms operations. Economic value added and ROIC computes the changes in the company due to change in value of

operating invested assets in place.

Geyse &Hall (2004) found that there are many methods to check the performance of the company but the best method amongst

these methods are economic value added due to the performance value addition. These methods tell whether the company is

creating or destroying the wealth of the shareholder. Economic value added can be calculated with the help of net operating profit

after tax minus cost of capital. Here return on assets and return on equity has been taken as performance measures of economic

value added. The result shows that Economic value added can be destroyed if more debt invested. This paper states that the

traditional methods are not the good indicators of performance evaluation.

Russell (2005) found that there are several methods to measure the performance of the company but economic value added creates

its own space worldwide as a performance measurement. Economic value added calculates the true economic profit of the

company with the help of net operating profit after tax and cost of capital. If the profit is more than the cost of capital it means

that the company is creating the wealth for the shareholder.

Fraker (2006) said that economic value added is a tool which helps the bankers to measure the performance of the bank. EVA is

the invention of Stern Stewart & Co.EVA is equal to the net operating profit minus cost of equity. Cost of equity is equal to the

company’s equity capital (reported on its balance sheet) multiplied by a percentage return that the company’s shareholders require

on their investment. For the calculation of NOPAT Stern Stewart has given more then 120 adjustment. These adjustments should

be done in NOPAT to find out the economic profit.

Percentage return of the shareholders can be calculated with the help of risk free rate of return and beta value. Treasury bill can be

taken as risk free rate and beta is the volatility of the stock according to the market. The market risk premium is the risk associated

with investing in the stock market as a whole. The bank can consider EVA as their performance measure and can improve the

performance as in this study the given bank improved the performance after applying EVA as performance measure.



Mittal et al (2008) investigated a positive relationship between economic value added and corporate social responsibility.

Company with code of ethics generally generates good profit and also has a positive economic value added. For the calculation of

economic value added net operating profit and WACC is important. Corporate social responsibility and economic value added

have a positive relation which is shown by regression. The study proves strong evidence that there is a positive relationship

between economic value added and corporate social responsibility.

Holler (2008) expressed that Share holder value creation is an important part for modern business. If a firm has positive economic

value added the stock return is also high. For the calculation of economic value added regression, WACC and NOPAT has been

used. Economic value added gives a true profitability of the firm.EVA improves investor monitoring capacity, disclosure of return

on investment and EVA are important in annual reports for the benefit of the shareholder.

Zaima (2008) said that economic value has gained popularity now days. In the firm which has been taken as an example for the

calculation of economic value added, economic value added and market value added calculated in the firm and the firm is ranked

according to their performance. The result generates surprising outcome the first portfolio annual return are more volatile than

others.

Lin & Zhilin (2008) found that EVA performance measurement is more beneficial than any other performance metric. The study

is based on listed companies of China. For the calculation of economic value added there are necessary adjustments made in the

rules of accounting given by GAAP. EVA was developed by Stern Stewart & co. which can be calculated by the help of NOPAT

and WACC. Here EVA is used with the help of neutral network which is a computer based system and is used in management

field. Traditional methods have their own limitations but integrated economic value added proved its betterment than any other

method.

Reddy et al (2011) expressed that EVA is an attempt to measure whether the company is destroying or creating the wealth of the

shareholder. The traditional measures continue side by side. These traditional measures are unable to tell that the company is

creating or destroying the wealth of the shareholder. From the analysis it is found that the economic value added is the best

performance metrics than any other metrics.

Khairallah D. (2011) investigated that EVA is better measurement parameter over other performance measures. It can be use as

employee motivator as well. For the calculation of economic value added Net operating profit after tax has taken and cost of

capital deducted from it. They also highlighted that managers must take responsibility for decision-making, promote transparency

of information and knowledge, and be accountable for their performance.

OBJECTIVES OF STUDY

The focus of the study is on the following objectives:

a) To calculate the important metric of financial performance that is EVA for a sample of 50 NSE listed Companies for the

period of 2005-2010.

b) To rank the sample companies on the basis of EVA generated / lost.

c) To identify the companies which have cited the use of EVA in their annual reports for the financial year 2005-06 to

2009-10.


Sample Size

Sample size of fifty companies has been taken to compute the EVA. All these companies are NSE listed.

Methodology

According to Stewart, Eva is a residual return measure that subtracts the cost of invested capital from NOPAT. At it’s the simplest

form and can be calculated by the following equation:

EVA=NOPAT-(WACC*IC),

NOPAT=Net operating profit after tax,

WACC=Weighted Average Cost of Capital,

IC = Invested Capital (total assets).

EVA is positive if NOPAT exceed the cost of financing. The authors of EVA state that, in this case, the company has created

shareholder value. On the other hand when EVA is negative, the company is destroying the value of the shareholder.

To compute the NOPAT Stern Stewart has taken 160 adjustments. But in this study we are taking three adjustments to compute

the EVA. He did these adjustments so that traditional accounting can be closer to the “Economic Profit”.

NOPAT has been taken from the income statement of companies’ refers to capita line data base:



Adjustment in NOPAT

Interest Expenses

Goodwill written off

Research & Development expenses

The second step is to calculate WACC (weighted average cost of capital)

WACC=Ke*We+Kd*Wd+Kp*Wp

Cost of equity (ke) calculated by the CAPM model

Ke=Rf+Beta(Rm-Rf)

For calculation of cost of equity the beta has been calculated by following method:

Market return = Current day closing price of nifty-previous day closing price of nifty / Previous day closing price of nifty

Security return = Current day closing price of stock-previous day closing price of stock / Previous day closing price of stock

Use regression formula to check the dependency of security return on market return this is called beta.

Calculation of RM (Market return) or market premium

(Current day closing price of nifty-previous day closing price of nifty) * Number of trading days

Previous day closing price of nifty

Kd = Rate of interest rate paid by the company for debts

Calculation of cost of debt

Total interest expenses* (1-effective tax rate) / Average total borrowings*100

Calculation of cost of preference shares

Kp = (Preference dividend / Average Preference capital) *100

DATA ANALYSIS AND INTERPRETATION

It is depicted from the table 4.1 given below that average of economic value added created by the sample companies during the

last five year that is 2005 to 2009. The table shows that in out of fifty company, thirty seven companies have generated positive

economic value added and thirteen companies are destroying the wealth. The companies which are destroying the wealth are

Voltas Ltd, Tamil Nadu Petroleum Ltd, B. A. G. Telefilms and Media Ltd, Madras Fertilizer Ltd, Noida Toll Bridge Ltd, Cipla

Ltd, Tata Teleservices Maharashtra Ltd, Tata Chemical Ltd. Bharat Electronics Ltd. BHEL, NTPC and Tata Communications.

Reliance Industry is the top most company which has generated the wealth of the shareholder within the fifty companies followed

by ONGC, Grasim Industries Ltd, TCS,HPCL,IFCI Ltd. these are the top five wealth generated companies in this study.

Table-1: Eva Ranking of Selected Companies

Name of the company Average EVA

(in Crore)

Rank Name of the company Average EVA

(in Crore)

Rank

Reliance Industries Limited 18324.57201 1 TVS Motor Company Limited 144.2758776 26

Oil & Natural Gas Ltd. 17219.71506 2 Blue Star Limited 117.7686052 27

Tata Consultancy Services Limited 2856.551155 3 Godrej industries 98.81769009 28

Hindustan Petroleum Corporation

Limited

2378.196855

4 Bombay Dyeing & Mfg Company

Limited

82.38910491

29

Grasim Industries Limited 2544.330224 5 Tamil Nadu Newsprint & Papers

Limited

82.73401934

30

Tata motors 2110.716965 6 Surya Roshni Limited 61.22699332 31

IFCI Ltd. 1464.636551 7 Apollo Hospitals Enterprise

Limited

46.97118938

32

Hero Honda Motors Limited 1480.926111 8 CCL Products (India) Limited 49.57815037 33

UltraTech Cement Limited 1255.935676 9 Ashok Leyland Ltd. 26.58338228 34

Wipro Limited 969.0819805 10 NIIT Limited 14.75859292 35

India bulls Financial Services

Limited

918.0194752 11 Aegis Logistics Limited 7.92392652 36



Chennai Petroleum Corporation

Limited

829.6690377 12 Zodiac Clothing Company

Limited

3.832382319

37

Adani Enterprises Limited 532.415484 13 Voltas Ltd. -49.3683552 38

Maruti Suzuki India Limited 519.9510743 14 Tamilnadu Petro Products

Limited

-51.59997869

39

Asian Paints Ltd. 460.3958403 15 Madras Fertilizers Limited -101.777072 40

Bharat Forge Limited 351.9156171 16 B.A.G. telefilms and media -157.9604555 41

Tata Global Beverages Limited 342.5240333 17 Noida Toll Bridge Company

Limited

-188.2613802 42

Dabur india Ltd 312.7598334 18 Cipla Limited -227.3487506 43

Usha Martin Limited 274.0564035 19 Tata Teleservices (Maharashtra)

Limited

-771.2550348 44

Godrej consumer product ltd 245.7607877 20 Tata Chemicals Limited -1038.809517 45

Bharati Shipyard Limited 203.790116 21 Bharat Electronics Limited -1048.455781 46

Pidilite Industries Limited 216.0114564 22 Bharat Heavy Electricals Limited -1738.948682 47

GAIL (India) Limited 163.5656894 23 ITC Limited -2224.03887 48

Alembic Limited 168.5285691 24 NTPC Limited -4110.238722 49

Apollo Tyres Limited 155.7598593 25 TATA Communications -5088.577075 50

Five Wealth Generating Companies

Graph1 shows the position in economic value added of top five companies. Reliance industries are the top performer amongst

them, followed by ONGC Ltd, Grasim Industries Ltd, Hindustan Petroleum Corporation Ltd and Tata consultancy. On an average

reliance has generated the highest economic value added on average basis that is Rs.18324.57 Crores, which is a good indicator

for the future performance. It means that reliance industries has been investing its finance in good projects and generating more

profit than the cost of capital. The success is followed by ONGC which is a public sector company. It proves the assumption

wrong that the government sector is not an efficient one. ONGC is the second wealth creator among the fifty sample companies

which has been taken in this study. ONGC has never shown any loss in past five years, as the total cost of capital of ONGC is less

than their profits. Therefore ONGC is very efficient in giving true value for the shareholder wealth. Tata consultancy ltd, ranked

third wealth creator in top five wealth generated companies. Although it has negative economic value added in the year 2005 and

2009 but other years it gives very good performance in the form of economic value added which make it eligible as the part of top

five wealth creator in our study. HPCL ranked fourth in wealth generation in our study.

Graph-1: Top Five Wealth Generating Companies

HPCL has increasing performance trend in the form of economic value added till the year 2006. In year 2007 EVA is less than the

last two years. The reason is that the profit is less than in last two years, as per the cost this year WACC is more than other years

but less than year 2005. Again it has increasing trend in year 2008 but decreasing in year 2009 while comparing with year 2008,

WACC is more in this year than the year 2008. Grasim industry ranked fifth position in the best performer as shareholder wealth

creator. Grasim has trend of increasing EVA from the year 2005-2007 after that in 2008 the EVA is decreasing as the profit is less

then last two years then again it has decrease economic value added but it is because of the WACC profit which is less in 2008

then last four years.

Top Five Wealth Destroyer Companies

The below given Graph2 shows that Tata Communication is the top wealth destroyer in top five wealth destroyer companies. Till

2007 Tata Communications has shown negative wealth creation for the shareholders mainly in the year 2005 and 2006. In 2008 it

started to perform with positive economic value addition but again in 2009 it has negative economic value added so on an average

its performance is not good. It has a high rate of WACC in the starting years but there is a decreasing trend in WACC. No doubt

the profit is increasing but not so enough that it can convert the negative EVA into positive EVA. In the starting year the company

has high rate of WACC due to the high rate of cost of equity. After that WACC has decreasing trend because the cost of equity

has a decreasing trend. On an average the cost of capital is also high in Tata Communication which is one of the reasons to rank it

0

5000

10000

15000

20000

AVERAGE EVA

AVERAGE EVA



in the highest position of wealth destroyer. The second highest wealth destroyer in the graph2 is NTPC Ltd. NTPC has negative

EVA in 2005, 2007 and 2009. Although it has positive economic value added in the 2006 and 2008 but it is not to offset the

negative EVA of 2005, 2007 and 2009 so on an average it is the second highest wealth destroyer after Tata Communications

because of the high rate of cost of capital. The third position among the top five wealth destroyer is ITC Ltd.

Graph-2: Top Five Companies with Negative EVA

ITC has a positive trend in profits but its economic value added has negative trend except the 2006 and 2008. The reason of

negative economic value added is WACC which is on an average is high. The fourth highest wealth destroyer among top

destroyer is BHEL Ltd. BHEL Ltd. has trend of increasing profit but on an average WACC increased over the five year. So the

performance of the BHEL Ltd. is also not good. Fifth position among the top five wealth destroying is Bharat Electronics Ltd. It

has positive economic value added only in the year of 2008 the main reason to make it wealth destroyer is high rate of WACC.

FINDINGS OF THE STUDY

Economic value added is a true measure of financial performance and is gaining reputation globally. As a part of this research

work we find out that in India there are very less number of companies which are disclosing economic value added. In a sample of

fifty companies considered for the study, there are only three companies which have disclosed economic value added in their

annual reports. As per the data for the period under consideration (2005-06 to 2009-10), public sector is destroying the wealth of

the shareholders more than the private sector. Economic value added is successful to prove its betterment over other traditional

methods. The findings of the study are:

Amongst the top five wealth destroying companies four companies are from public sector so the overall public sector

performance is not really good. These companies are NTPC Ltd., ITC Ltd. Bharat Heavy Electronics Ltd. and Bharat

Electricals Ltd.

The companies which are destroying the wealth of the shareholder have a high equity cost and the profit is not enough

to cover the equity cost. It means these companies are investing its funds in less profitable projects.

Reliance Industries Ltd., are the top most performers in fifty sample companies. This means that Reliance Industries

Ltd. is investing its funds in efficient projects.

The names of top wealth creator companies are Reliance industries, ONGC Ltd., Tata consultancy, HPCL Ltd. and

Grasim industries.

In the list of top EVA destroying companies NTPC Ltd. and Tata Communications is the top wealth destroying

companies.

There are only three companies in our study which disclose the economic value added in their annual report. These are

TVS Motors Ltd., BHEL Ltd. Godrej Products Ltd.

SUGGESTIONS

The companies should disclose economic value added in their annual report and it should be mandatory for every company to

disclose economic value added. The main aim for every investment should be to create value for the shareholder from that

investment. The manager never considers their own profit while making investment. He should be a risk taker because higher

profit is attached with more risk. He should have capability to win over the risk and get more profit. He should not have the fear

of risk. Public Sector Company must have good management system. They must have professionals who can help them to

generate more profits. Incentive should be attached with economic value added generation.

Globalization has made it compulsory for every company to be transparent other wise capital will be very costly

because investor would invest in the company where more transparency is available. With the help of EVA, as cost of

equity is considered in its calculation, the company which is giving more profit after deducting the cost of equity is a

well performing company than any other.

For a general justice it is necessary to know whether any action of the company is benefitting the shareholders or not.

The main aim of the company should be the improvement of the shareholder wealth so the manger should not think

about the short term profit but also should think about the long term effects of the projects also.

-6000

-5000

-4000

-3000

-2000

-1000

0

Average EVA

Average EVA



The findings of the study show that the four companies in top five wealth destroyer are from public sector, like Bharat

electronics Ltd, BHEL Ltd, NTPC Ltd. and ITC Ltd. Thus the management of these PSU’s should take corrective

measures to improve their profits.

The research study recommends the disclosure of economic value added in companies ‘annual report on compulsory

basis as it is an important indicator of financial performance measures.

The capital structure should be optimum so that EVA can be generated. Equity is not a ‘free of cost’ capital so

unnecessarily equity should not be issued. Also whatever the capital has been generated it should be invested in a

project after proper appraisal of the same. The evaluation of the project profitability should be done before investment

so that the wealth of the shareholder may not get destroyed.

A pay program should be attached with the performance of the managers in terms of economic value added.

CONCLUSIONS

At last we can conclude that EVA is a financial measure based on operating income after tax. EVA is the most reliable source to

know the performance of the company in today era. A company should be a wealth creator not the wealth destroyer for the

shareholder. In the EVA system a company will be a wealth creator if its operations are as good as it can generate profit more than

the cost of capital which includes the cost of equity as well. In our study there are fourteen companies which are the wealth

destroyer companies although they have good profit but still they have negative EVA. It means they have high cost of equity

capital and this capital has not been invested in good projects.

The companies are considering the cost of debt and cost of preference shares, but the most important cost is not considered that is

cost of equity. So the calculation of EVA is very important for these companies so that they can take care of all the weakness and

try to generate more profit, so that the cost of equity can also be covered. In top five wealth destroying there is only one company

which is from private sector. There are some limitations of EVA as well this system does not control the size difference across

plants or divisions, A large plant has higher EVA instead of smaller size of plant. EVA is a computed number that relies on

financial statements which can give wrong results if manipulated by the managers for increased incentive motive. It restricts the

investment on the project which requires high investment but gives good result after a long time period because with the

investment in that project the cost of capital will raise and the current year EVA will decrease and the incentives of the managers

also may decrease. They will not be motivated to invest in that type of project. Although there are some limitations of EVA, still it

is a good performance indicator than any other method and globally it has reputation because it considers the cost of equity in the

form of shareholder expectations. In India there are very less number of companies which are adopting economic value added.

REFERENCES

1. Beneda, L. N., (2004). “Valuing Operating Assets in Place and Computing Economic Value Added”, The CPA Journal.

Vol 74, No.11, pp. 56-62.

2. Chen, L., and Zhilin, Q., (2008). “Empirical Study of Integrated EVA Performance Measurement in China”, Canadian

Social Science, Vol 4, No13, pp.41-49.

3. Fraker, T. G., (2006). “Using Economic Value Added (EVA) to Measure and Improve Bank Performance”, Arizona

Chapter Vol 13, No 3, pp. 1-6.

4. Holler, A., (2008). “Have Earning Lost Value-Revisiting Latest Evidence on EVA”, The Business Review, Vol 10,

No2, pp. 245-255.

5. Kroll, M. K., (1997). “EVA and Creating Value”, Industry Week, Vol. 246, No.7, pp.102-106.

6. Kaur, M., and Narang, S., (2009). “Shareholder Value Creation in India’s Most Valuable Companies”, Journal of

Management Research, Vol 8, No 8, pp.16-27.

7. Morris, F. V., (2001). “The EVA Challenge: Implementing Value - Added Change in an Organization / EVA and Value

Based Management Practical Guide to Implementation”, Financial Analysts Journal, Vol 57, No 6, pp.106-109.

8. Mittel, R. K., (2008). “An Analysis of Linkage between Economic Value Added and Corporate Social Responsibility”,

Management Decision, Vol 46, No 9, pp.1437-1440.

9. Reddy, (2011). “Valuation through EVA and Traditional Measures and Empirical Study”, International Journal of

Trade, Economics and Finance, Vol 2, No 1, pp.19-23.

10. Sharma, K. A., (2010). “Economic Value Added (EVA) - Literature Review and Relevant Issues”, International Journal

of Economics and Finance, Vol 2, No 2, pp.200-220.

11. Stewart, S., (2003). “Value Based Management Done Right”, Evaluation, Vol 5, No 1, pp.1-8.

12. Sahil, N. K., (2009). “Performance Measures: An Application of Economic Value Added”, International Journal of

business and Management, Vol 4, No 3, pp.169-177.

13. Zaima, K. J., (2008). “Portfolio Investing with EVA”, Journal of Portfolio Management, Vol 34, No 3, pp.34-42.

*****



INTERNATIONAL FINANCIAL REPORTING STANDARDS (IFRS) - AN OVERVIEW

Pawan Kumar10 Shalini Srivastav11

ABSTRACT

International Financial Reporting Standards (IFRS) are principles-based standards; interpretations and the framework adopted

by the International Accounting Standards Board Many of the standards forming part of IFRS are known by the older name of

International Accounting Standards (IAS). This article tries to explain an overview of IFRS to the readers. The adoption of

standards that require high-quality, transparent, and comparable information is welcomed by investors, creditors, financial

analysts, and other users of financial statements.

Without common standards, it is difficult to compare financial information prepared by entities located in different parts of the

world. In an increasingly global economy, the use of a single set of high-quality accounting standards facilitates investment

and other economic decisions across borders, increases market efficiency, and reduces the cost of raising capital. IFRS are

increasingly becoming the set of globally accepted accounting standards that meet the needs of the world’s increasingly

integrated global capital markets.

KEYWORDS

IFRS, Transition to IFRS, Roadmap to IFRS Convergence, IAS, and Challenges to IFRS etc.

INTRODUCTION

IFRS means International Financial Reporting Standards. As the name suggests, it means some standards which can be used

worldwide universally which would enable to standardize training among various nations. It is a single set of accounting standards

which would assure better quality and also permit international capital to flow more freely. The convergence with IFRS is set to

change the landscape for financial reporting in India. IFRS represents the most commonly accepted global accounting framework

and it has been adopted by more than 100 countries.

In April 2001, the International Accounting Standards Board (IASB) was founded to undertake the responsibilities of the

International Accounting Standards Committee (IASC) established in 1973. The IASB is made up of fourteen members

representing nine countries, including China, Japan, Australia, and the U.S., and is sponsored by a variety of financial institutions,

companies, banks, and accounting firms. In 2002, a year after their establishment, the IASB united with the Financial Accounting

Standards Board (FASB) to combine their knowledge and develop a set of high-quality accounting standards that would be

compatible with all countries in order to successfully carry out international business affairs and their accounting. This set of

global accounting standards is referred to as the International Financial Reporting Standards (IFRS).

IAS was issued between 1973 and 2001 by the Board of the International Accounting Standards Committee (IASC). On April 1,

2001, the new IASB took over from the IASC the responsibility for setting International Accounting Standards. During its first

meeting the new Board adopted existing IAS and Standing Interpretations Committee standards (SICs). The IASB has continued

to develop standards calling the new standards IFRS.

International Financial Reporting Standards refer to the standards and interpretations adopted by the IASB comprising of:

International Financial Reporting Standards.

International Accounting Standards.

Interpretation of IFRIC (International Financial Reporting Interpretations Committee).

Interpretation of SIC.

Need for IFRS

Different Countries employ different Accounting Standards while computing the Profits of a Company. It is expected that IFRS

adoption worldwide will be beneficial to investors and other users of financial statements, by reducing the Costs of Comparing

alternative Investments and Increasing the Quality of Information. The Companies are also expected to benefit, as investors will

be more willing to provide financing.

In recent Past, there have been cases where companies reporting under IFRS in Europe record a loss but when these same

companies re-state their accounts according to US GAAP they record a profit. International Financial Reporting Standards remove

some of the subjectivity from financial reporting and provide a consistent basis for recognition, measurement, presentation and

disclosure of transactions and events in financial statements. In other words we can say that this has lead to improved

transparency of financial reporting.

10Registrar & Assistant Professor, ACCMAN Institute of Management, Uttar Pradesh, India, [email protected] 11Assistant Professor, ACCMAN Institute of Management, Uttar Pradesh, India, [email protected]





Qualitative Characteristics

Understandability Financial information that is classified, characterized and presented in a clear and concise way is understandable.

Relevance Relevant financial information is capable of making a difference to the decision made by users. In order to make a

difference, financial information has predictive value, confirmatory value or both.

Reliability Financial information that faithfully represents economic phenomena has three characteristics: It is complete, it is

neutral, and it is free from error.

Comparability Comparability enables users to identify similarities and differences among items, both between different periods

within a set of financial statements and across different reporting entities.

Consistency Consistent application of methods to prepare financial statements helps to achieve comparability.

Figure-1

INTERNATIONAL FINANCIAL REPORTING STANDARDS-EFFECTIVE AS ON DATE

IFRS-1: First time adoption of IFRS

The objective of this IFRS is to ensure that an entity’s first IFRS financial statements, and its interim financial reports for part of

the period covered by those financial statements, contain high quality information.

It is transparent for users and comparable over all the periods presented.

Provides a suitable starting point for accounting under International Financial Reporting Standards (IFRS).

Can be generated at a cost that does not exceed the benefits to users.

IFRS-2: Share Based Payment

The objective of this IFRS is to specify the financial reporting by an entity when it undertakes a share-based payment transaction.

In particular, it requires an entity to reflect in its profit or loss and financial position the effects of share-based payment

transactions, including expenses associated with transactions in which share options are granted to employees.

IFRS-3: Business Combinations

The objective of the IFRS is to enhance the relevance, reliability and comparability of the information that an entity provides in its

financial statements about a business combination. It does that by establishing principles and requirements for how an acquirer:



Recognizes and measures in its financial statements the identifiable assets acquired, the liabilities assumed and any

non-controlling interest in the acquire,

Recognizes and measures the goodwill acquired in the business combination or a gain from a bargain purchase, and

Determines what information to disclose to enable users of the financial statements to evaluate the nature and

financial effects of the business combination.

IFRS-4: Insurance Contracts

The objective of this IFRS is to specify the financial reporting for insurance contracts by any entity that issues such contracts

(described in this IFRS as an insurer) until the Board completes the second phase of its project on insurance contracts. In

particular, this IFRS requires:

Limited improvements to accounting by insurers for insurance contracts.

Disclosure that identifies and explains the amounts in an insurer’s financial statements arising from insurance contracts

and helps users of those financial statements understand the amount, timing and uncertainty of future cash flows from

insurance contracts.

IFRS-5: Non-Current Assets Held For Sale and Discontinued Operations

The objective of this IFRS is to specify the accounting for assets held for sale, and the presentation and disclosure of discontinued

operations. In particular, the IFRS requires:

Assets that meet the criteria to be classified as held for sale to be measured at the lower carrying amount and fair value

less costs to sell, and depreciation on such assets to cease.

Assets that meet the criteria to be classified as held for sale to be presented separately in the statement of financial

position and the results of discontinued operations to be presented separately in the statement of comprehensive

income.

IFRS-6: Exploration & Evaluation of Mineral Resources

The objective of this IFRS is to specify the financial reporting for the exploration for and evaluation of mineral resources:

Exploration and evaluation expenditures are expenditures incurred by an entity in connection with the exploration for

and evaluation of mineral resources before the technical feasibility and commercial viability of extracting a mineral

resource are demonstrable.

Exploration for and evaluation of mineral resources is the search for mineral resources, including minerals, oil, natural

gas and similar non-regenerative resources after the entity has obtained legal rights to explore in a specific area, as well

as the determination of the technical feasibility and commercial viability of extracting the mineral resource.

IFRS-7: Financial Instruments – Disclosures

The objective of this IFRS is to require entities to provide disclosures in their financial statements that enable users to evaluate:

The significance of financial instruments for the entity’s financial position and performance.

The nature and extent of risks arising from financial instruments to which the entity is exposed during the period and at

the reporting date, and how the entity manages those risks. The qualitative disclosures describe management’s

objectives, policies and processes for managing those risks. The quantitative disclosures provide information about the

extent to which the entity is exposed to risk, based on information provided internally to the entity's key management

personnel. Together, these disclosures provide an overview of the entity's use of financial instruments and the

exposures to risks they create.

IFRS-8: Operating Segments

An entity shall disclose information to enable users of its financial statements to evaluate the nature and financial effects of the

business activities in which it engages and the economic environments in which it operates.

IFRS-9: Financial Statements

An entity shall disclose to all its users the uses of different financial statements and their application as required.

IFRS-10: Consolidated Financial Statements

It builds on existing principles by identifying the concept of control as the determining factor in whether an entity should be

included within the consolidated financial statements of the parent company. The standard provides additional guidance to assist

in the determination of control where this is difficult to assess.



IFRS-11: Joint Arrangements

It provides for a more realistic reflection of joint arrangements by focusing on the rights and obligations of the arrangement, rather

than its legal form (as is currently the case). The standard addresses inconsistencies in the reporting of joint arrangements by

requiring a single method to account for interests in jointly controlled entities.

IFRS-12: Disclosure of Interests in Other Entities

It is a new and comprehensive standard on disclosure requirements for all forms of interests in other entities, including joint

arrangements, associates, special purpose vehicles and other off balance sheet vehicles.

IFRS - 13 Fair Value Measurements

Fair Value measurement completes a major project of the boards’ joint work to improve IFRSs and US GAAP and to bring about

their convergence.

Roll IFRS out from April 2012

Despite issuing the IFRS-converged standards in February 2011, Indian regulators did not implement the new standard from April

1, 2011. Thus, it is possible that all regulatory steps required implementing Indian AS can be completed over the next several

months whereby Indian AS can become mandatory from April 1, 2012.

OBJECTIVES OF STUDY

According to the Preface to IFRS issued by the IASB; the main objectives of IFRS are:

1. To develop in public interest, a single set of high quality, understandable & enforceable global accounting standards

that require high quality, transparent & comparable information in financial statements & other financial reporting to

help participants in the various capital markets of the world & other users of the information to make economic

decisions.

2. To promote the use & rigorous application of those standards.

3. In fulfilling the objectives associated above to take account of, as appropriate, the special needs of small & medium-

sized entities & emerging economies.

4. To bring about convergence of national accounting standards & IFRSs to high quality solutions.

5. To encourage international investing & thereby increase in foreign capital inflow.

6. To benefit the economy by increased international business.

7. To provide more relevant, reliable, timely & comparable information to investors.

8. Better understanding of financial statements would benefit investors who wish to invest outside the country.

9. Capital at lesser cost from foreign market.

10. To reduced accounting requirements prevailing in various countries & hence reduced cost of compliance.

11. Professional opportunity to serve international clients.

12. Increased mobility to work in different parts of the world in industry or practice.

THE DEATH OF LIFO

Few differences between IFRS and U.S. GAAP loom larger than accounting for inventories, particularly the disallowance of the

last-in, first-out (LIFO) method in IFRS. The proposed shift of U.S. public companies to IFRS could affect many companies

currently using LIFO for both financial reporting and taxation. This is because the conformity rule of IRC 472(c) requires

taxpayers who apply LIFO for tax purposes to also apply it for income measurement in financial reporting, and IFRS does not

permit LIFO for book accounting.

Therefore, CPA may be called upon to help manage inventory method changes. Companies using LIFO would have to switch to

FIFO or average cost. The change would place companies in violation of the conformity requirement. Absent relief from the

Treasury Department, it would require them to change their tax method of inventory reporting. Thusa typical change in inventory

method, such as from average cost to FIFO, is treated retrospectively. The entity reflects a change from LIFO to FIFO in the same

manner. The result is:

An increase in inventory.

An increase in current income taxes resulting from the effective increase in income.

An adjustment to retained earnings for the effect of the increase in net income.



The entity may need to show a deferred tax liability for the temporary difference between the accounting and tax bases for the

inventory change if it were to remain, for example, on average cost for tax purposes yet switch from average cost to FIFO for

book purposes. A change from LIFO will normally have a significant positive income effect because the accumulation of prior

years’ costs in beginning inventory will replace cost of goods sold valued at current costs. Assuming that the inventory turns over,

income for the year of change would increase by the entire amount of the LIFO reserve.

CHALLENGES TO IFRS

Convergence to IFRS has created understandable nervousness both in industry and in profession. Without underestimating; the

problems involved, it is perhaps true to say that much of this nervousness arises out of a fear of unknown. Thus, it is necessary to

have a clear understanding of the issues involved and the steps necessary in this connection.

What is required is the awareness of the manner in which Indian Accounting Standards are formulated?

The differences between International standards and Indian standards fall in three broad groups namely:

Different requirements of the companies act and Schedule V1.

Differences in language and additional guidance provided in Indian Standards.

Differences in substance.

Action has already been taken to identify the conflicting requirements in the Companies Act and Schedule V1and steps to make

necessary amendments have already been taken. What therefore, needs to be addressed are the differences in substance. This is

somewhat complicated by 2 factors:

1. The international standards on which the Indian Standards are based have themselves been the subject matter of

extensive revision.

2. In the revisions made by IASB and the new standards which have been issued, recognition has been given to new

concepts which have fundamentally altered the basis of recognition and measurement.

Principally; there are three new concepts which need to be addressed:

A) Concept of Face Value: The face value defines as the price that would be received to sell an asset, or paid to transfer a

liability in an orderly transaction between market participants at the measurement date. It emphasizes:

An exit price.

Existence of an active market.

Transaction in the active market.

b) Concept of Time Value of Money: This requires that where the expected date of recovery of an asset or payment of a liability

is deferred, it is recorded not at its face value but at its discounted value using an effective rate of interest.

c) Concept of Comprehensive Income: A further change is needed in the said concept contained in IAS 1- Presentation of

Financial Statements. Until this standard was issued, items of income and expense were recognized in the profit and loss account

and items not co recognized were presented in a statement of changes in equity.

However, other comprehensive income has to be separately disclosed. Besides these, some other areas to be taken care of which

are as follows:

Increase in cost due to dual reporting requirement till full convergence is achieved.

Changes are required in various regulatory requirements such as Companies Act, Income Tax Act, SEBI, RBI, etc.

Training may be required to all stakeholders such as employees, auditors, to understand IFRS thoroughly.

Additional cost towards modification in IT systems & Procedures.

Difference between Indian GAAP & IFRS may impact business decision & financial performance.

Limited pool of trained resource & persons having expert knowledge on IFRS’s.

Transition to IFRS

Although 2014 may seem a long way off, it is not too early to prepare for IFRS conversion. Here are some key activities that will

contribute to a successful conversion:

Establish a structured methodology.

Identify the areas other than financial reporting that will be affected.

Develop an IT strategy that goes beyond conversion.

Implement effective training.

Benefit from European conversion experiences.

Establish a robust communications plan.



Impact of IFRS on Production and Exploration Companies

It is the Oil and Gas industry which is one of the major drivers for any economy to grow. It goes without saying that for India

which is still dependent on 70% of its crude oil requirements through imports, the application of OFRS on production and

exploration companies would not be an easy task. It’s strange but true that under IFRS standards there is no specific guidance

provided under the IFRS 6 which deals with only exploration activities. Other industry specific transactions like depletion,

exploration costs to be written off, types of reserves, etc for which no specific guidance is provided under IFRS.

Road Map for IFRS

In July 2007, the Institute of Chartered Accountants of India accounted the move towards convergence with IFRS with effect

from 1st April, 2011 and issued a detailed concept paper on Convergence with IFRS in India. In early 2010, the Ministry of

corporate Affairs issued various press releases on IFRS roadmap and convergence plan for India, reaffirming the convergence

date to be 1st April, 2011 through 2014 for select Indian companies:

Select companies from 1st April, 2011 onwards,

Insurance companies from 1st April 2012 onwards,

Banks and NBFC from 1st April 2013.

Conversion is much more than a technical accounting issue. IFRS may significantly affect any-number of a company’s day-to-day

operations and may even impact the reported profitability of the business itself. Conversion brings a one-time opportunity to

comprehensively reassess financial reporting and take “a clean sheet of paper” approach to financial policies and processes.

IFRS Proposed Roadmap for India

Figure-2: Opening Balance sheet as at April 1, 2012 using IFRS-Converged Accounting Standards.

Companies not covered in the above chart will apply ‘Existing Indian Accounting Standards’ OR voluntarily opt to apply the

‘IFRS-converged accounting standards’.

Note 1: These exclude insurance companies, banks and non-banking finance companies (NBFCs).

*If the financial year of the companies commence on a date other than April 1, then opening balance sheet need to be prepared

from the beginning of the new financial year of the companies.

Who gets affected by the change?

A country’s intention to adopt IFRS or converge with IFRS is highly admirable and to be applauded. However, the accounting

profession, governments, regulators, national accounting standard setters, and other constituents must continue to work together to

eliminate differences between national and international standards. The principal actions needed to support convergence are

outlined below:

The Accounting Profession needs to assist governments and standard setters in formulating and enacting convergence

plans, provide IFRS training and education and support the preparation of national language translations of IFRS.

Governments must establish formal convergence plans that include target dates for implementation and address

impediments to convergence, for example the link between financial accounting and lax legislation.

2011• NSE - Nifty 50 companies,

• BSE - Sensex 30companies,

• Companies whose sharesor other securities listedoutside India;

• Companies listed or not,having a net worthinexcess of Rs. 1,000 crores[Note 1]

2012

• AllInsuranceCompanies

2013• Companies listed or not, having

a net worth between Rs. 500crores and Rs. 1000 crores [Note1]

• All shedule commercial banks

• Urban co-operative banks havinga net worht in excess of Rs. 300crores

• NBFC-Nifty 50 or Sensex 30

• NBFC listed or not, having a networth> Rs. 1,000 crores

2014• Listed companies having a

net worth of less than Rs.500 crores [Note 1]

• Urban co-operative banksaving net worth betweenRs. 200 to Rs. 300 crores

• NBFC (all other Listed)

• NBFC (other Unlisted)having net worth betweenRs. 500 to Rs. 1,000 crores



Regulators should set up efficient and effective enforcement mechanisms to increase the consistency and quality of

application of IFRS as well as support the International Financial Reporting Interpretations Committee (IFRIC) and the

IASB as the sole clearing house for interpretation of IFRS.

National Standard Setters must decide on a strategy and timetable for achieving convergence and develop an active

standard setting agenda aimed at eliminating existing differences with IFRS.

The IASB is required to address concerns about the complexity and operational practicality of IFRS, prioritize the SME

project as an agenda item and oversee and authorize translations of IFRS in various languages.

The Preparers of financial statements must actively participate in the standard setting process, in particular to identify

practical application concerns, as well as providing IFRS training for staff and managers, including those in non-

financial roles.

Universities need to include IFRS in the core accounting curriculum.

Analysts and Investors are required to promote convergence of national accounting standards with IFRS. They should

also actively participate in the IASB’s standard setting process, in particular to identify users’ needs, and educate their

staff regarding the IFRS reporting model.

CONCLUSIONS

The adoption of new accounting standards in line with International Financial Reporting Standards, or IFRS, may increase the

cost of raising debt through instruments such as debentures, preference shares and foreign currency convertible bonds (FCCBs) as

the rules get tighter. IFRS are balance sheet driven standards which are more than likely to create volatility in the year-to-year

income statements and that is a hard reality which the Indian companies will be confronting with convergence. In the case of

debentures, under Ind-AS, the redemption premium plus coupon interest are amortized to the profit and loss account over the term

of these instruments based on the Effective Interest Rate (EIR) method.

Transitioning to IFRS would allow companies to compete for capital in other countries, while reducing cost and complexity for

companies operating internationally; we also think that embracing a single set of global accounting standards would contribute to

a higher degree of investor understanding and confidence. Also IFRS is very important for US Investors as they own 2/3 rd of

securities issued by foreign companies. Because of IFRS there will be greater comparability and greater confidence in the

transparency of financial reporting for the US investors.

REFERENCES

1. Barry, J. Epstein, and Eva. K. Jermakowicz, (WILEY) “Interpretation and Application of International Financial

Reporting”, Published by John Wiley & Sons Inc, Hoboken, New Jersey, ISBN No: 978-0470-45322 – 3.

2. Nandakumar, Kalpesh; J. Mehta; T. P. Ghosh, and Yass. A. Alkafaji, “Understanding IFRS Fundamentals:

International Financial Reporting Standards”, Published by John Wiley & Sons Inc, Hoboken, New Jersey, ISBN No:

978-0-470-39914-9.

3. The Chartered Accountant Journal –Vol. 59/No.1/July 2010.

4. The Chartered accountant Journal - Vol. 59/No.4/October 2010.

5. Journal of Accountancy, January 2008 – Robert Bloom & William. J. Cenker.

6. http://www.ifrs.org/NR/rdonlyres/9E3D1C87-8FB8-48DF-9A66-3CD6CDC4B5B7/0/MoreAnnualImpMar11.pdf

7. http://www.articlesbase.com/accounting-articles/what-is-ifrs-and-why-are-global-economics-looking-at-one-

accounting-standard-573896.html#ixzz1MrhfYzD1

8. www.pwc.com

9. http://thegaap.net/articles/AccountingfornewFinancialStatementsIFRS.html

10. http://www.ifrsbox.com/blog/post/how-to-learn-ifrs-part-1

11. http://www.ey.com/Publication/vwLUAssets/Supplement_86_GL_IFRS/$File/Supplement_86_GL_IFRS.pdf

12. http://www.charteredclub.com/what-is-ifrs/

13. http://www.ifrs.org/Home.htm

14. http://wircicai.org/wirc_referencer/Acconting%20&%20Auditing/Comparison%20of%20IFRS%20and%20Indian%20

Accounting%20Standards.htm

15. http://articles.economictimes.indiatimes.com/2011-07-7/news/29820849_1_implementation-indian-companies-

accounting-advisory-services

*****

http://www.ifrs.org/NR/rdonlyres/9E3D1C87-8FB8-48DF-9A66-3CD6CDC4B5B7/0/MoreAnnualImpMar11.pdf

http://www.articlesbase.com/accounting-articles/what-is-ifrs-and-why-are-global-economics-looking-at-one-accounting-standard-573896.html#ixzz1MrhfYzD1

http://www.articlesbase.com/accounting-articles/what-is-ifrs-and-why-are-global-economics-looking-at-one-accounting-standard-573896.html#ixzz1MrhfYzD1

http://www.pwc.com/

http://thegaap.net/articles/AccountingfornewFinancialStatementsIFRS.html

http://www.ifrsbox.com/blog/post/how-to-learn-ifrs-part-1

http://www.ey.com/Publication/vwLUAssets/Supplement_86_GL_IFRS/$File/Supplement_86_GL_IFRS.pdf

http://www.charteredclub.com/what-is-ifrs/

http://www.ifrs.org/Home.htm

http://wircicai.org/wirc_referencer/Acconting%20&%20Auditing/Comparison%20of%20IFRS%20and%20Indian%20Accounting%20Standards.htm

http://wircicai.org/wirc_referencer/Acconting%20&%20Auditing/Comparison%20of%20IFRS%20and%20Indian%20Accounting%20Standards.htm

http://articles.economictimes.indiatimes.com/2011-07-7/news/29820849_1_implementation-indian-companies-accounting-advisory-services

http://articles.economictimes.indiatimes.com/2011-07-7/news/29820849_1_implementation-indian-companies-accounting-advisory-services



PRIVATE EQUITY - IS THERE NEED FOR REGULATORY FRAMEWORK?

Preeti Singh12 Richa Arora13

ABSTRACT

Private Equity (PE) funds purchase equity shares and become part owners of companies in which they invest. The concept of

Private Equity in India is very recent as compared to USA, UK and Europe but it has played a very important role in an Indian

business since 1991 during the liberalization and globalization phase. Real estate, information technology, software and

service sectors have attracted a significant amount of Private Equity funding. This paper discusses the working of a Private

Equity fund in India and compares it with the differences in its working in other countries like UK, China and USA. There are

several challenges facing Private Equity investment options in India. Some of these challenges are regarding independent

directors to take a decisive role in the regular working of the company, technical and advisory services and requirement of

funds at the time of growth through international partners, employee resistance to exit options of a Private Equity fund. This

paper is an exploratory attempt to find out the state of existing regulations of Private Equity in India and also seeks to find out

if there is any need for regulatory framework in the present context in India.

KEYWORDS

Private Equity, Regulatory framework, and Challenges etc.

HISTORICAL BACKGROUND

The seeds of Indian Private Equity industry were laid in the mid 1980’s, where the first generation venture capital funds, which

can be looked at as a subset of private equity funds were launched by financial institutions like ICICI and IFCI. This was followed

by Commercial banks like Canara Bank and thereafter various private sector funds also came into being with their own venture

capital funds. Between 1995-2000, several foreign private equity firms like Baring PE partners, CDC Capital, Draper

International also started coming in. Moreover, during mid-1990’s, laws for venture capital funds formally started taking shape.

The Securities and Exchange Board of India issued the SEBI (Venture Capital Funds) Regulations 1996. The regulations were

further amended in 2000 on the recommendations of K.B. Chandrasekhar Committee. The PE industry slowed down between

2001-03 after technology boom burst in US in 2000. The investment activity revived in 2004 with the upward trend in domestic

stock market. However, despite a long history, the penetration of PE capital into India remains at a very miniscule extent.

ORGANIZATION OF THE PAPER

This paper has been organized in five sections. Section 1 discusses the definitions, nature followed by working and rationale of

Private Equity fund. Section 2 brings into light the review of past studies, thereby bringing the need for present study in the

concerned area. In Section 3, the evolving status of Private equity funds in various international markets has been carried out.

Section 4 makes a comparison between Indian and western developed Private Equity funds. Section 5 brings out the regulations in

India and the need for further reforms in regulations. Section 6 focuses on the challenges in the Private Equity sector and Section

7 brings out the summary and conclusions.

INTRODUCTION

Some Definitions

There is no universally agreed definition of private equity, as different academic studies have given different private equity

definitions.

As per Lerner(1999), Private equity are partnerships specializing in venture capital, leveraged buyouts, mezzanine investments,

distressed buyouts and other related investments. On the other hand, Ljungqvist and Richardson (2003) describes private equity as

an illiquid investment since there is no active secondary market for such investments, investors have little control over how

capital is invested and the investment profile covers a long horizon.

It is also called ‘Patient Capital’ as it seeks to profit from long term capital gains rather than short term regular reimbursements.

Private Equity are investment companies that take an initiative in the private companies by purchasing their equity shares and

becoming partial owners of the company in which they invest. They also invest in private placements of securities from public

limited companies. They prefer not to hold publicly-traded securities.

12 Associate Professor, Department of Commerce, Jesus & Mary College, University of Delhi, New Delhi, India,

[email protected] 13 Assistant Professor, Department of Commerce, Sri Ram College of Commerce, University of Delhi, New Delhi, India, [email protected]





Nature of Private Equity Fund

Private Equity is a high risk, high return oriented investment for investors. Private Equity generally refers to large investments in

more organized and matured companies at their expansion or later stages. As investors in companies, they are like partners of

such companies and play a major role in decision making of the company in which they invest. Virtually, all private equity firms

are organized as limited partnerships where private equity serves as general partners and large institutional investors and high net

worth individuals serve as limited partners.

Working and Rationale of Private Equity Fund

Private Equity funds are formed through a structure called an investment vehicle. An investment manager known as sponsor,

sources, acquires and also manages the investment of the fund. The sponsor is the face of the organization and in the background

there are a large number of investors who commit their surplus funds as an investment to earn a return in the future. The sponsor

being the chief person responsible for the business receives fees for his services and also a participation in the profits of the fund’s

investments. The investments of a trust are managed by an Asset Management Company (AMC) that charges the fund an annual

operating cost, which usually varies from 2-3 percent per annum, depending on the strategy, scheme, life cycle and investment

plan. The fund invests in number of companies, where some of the investments fail, some will break-even, some will do well and

some will give extremely high returns. At the end of life cycle of fund the profits are distributed to the investors. If the returns are

higher than the expected returns to investor, then the surplus is shared between the AMC and investors in a certain determined

ratio.

Presently, there are many factors that make a strong case for Private Equity, even for listed entities, as an alternative source of

growth capital compared to traditional means of fund-raising, particularly public markets and availability of bank finance. In line

with the global downturn, recent volatility in stock markets has brought down valuations of companies, but provided attractive

investment opportunities for Private Equity firms to invest in listed enterprises. Simultaneously, for listed companies, low public

market valuations have been a deterrent to raise money from the capital markets. Instead, they are attracted towards Private Equity

investors, who may tend to value the companies on underlying growth prospects rather than short term market sentiments.

Moreover, liquidity issues and lack of FII inflows have led to delays or withdrawal of the IPO plans of number of companies,

further supporting the case for PE as a stable source of growth capital for private companies aspiring to list in the stock market.


It is important to review the work done by experts on private equity.

Scott W. Naidech, Chadbourne & Parke LLP (2011), have provided a complete understanding of the economics of the Private

Equity structure by providing a comprehensive overview of the different kinds of fund vehicles. In particular, they have analyzed

the American situation and explained the different kind of documents required under the USA regulatory system.

A study by Barend de Beer & Zeph Nhleko (2009), is based on the experiences of Private Equity funds in South Africa. They

have compared the working of Private Equity funds in 20 countries. According to them in 2006-07, Private Equity funds have

been very active but developed countries have had more deals. USA and Canada have the highest Private Equity market in the

world. They found that South Africa has gained from Private Equity funds. South Africa does not have any specific laws to

regulate the Private Equity industries. However, there are institutional sector legislation and regulations that are applicable to all

the participants.

Cumming and Johan (2007) have discussed that the Dutch Private Equity market has been affected by a lack of proper

regulations. According to them it is important that there should be disclosure standards and regulations for governing and

monitoring private equity investment. According to their analysis, lack of regulations will not bring about participation in private

equity investment. They ranked the importance of regulations on a scale of importance from 1 to 5 giving 1 to lowest importance

and 5 to the greatest importance of regulations. The study found that private investment is facilitated upto 30% with proper

regulations.

Chandrasekhar (2007) stated that in Asia and more specifically in India, there would be an increase in foreign acquisitions

because a large number of unlisted companies required funding. Private sources appeared to be useful in this context. According

to this study, this type of funding has long term involvement where funds are provided with the objective of performance of

companies through involvement and acquisition of controlling stake undergoing various types of risks & uncertainty in the

process.

According to Prowse (1998), the growth in private equity has started since 1998 and has brought about organisational innovation

with regulatory and tax structures. Taking the example of USA, Japan and Germany that favoured bank finance deregulated their

financial markets as they found merit in private funding for small firms. Microsoft, Dell Computer, and Genentech had benefited

from Private Equity funding. Large public companies like Safeway, RJR Nabisco, and Beatrice also received private funding. In

his opinion, it was important to have more academic research on Private Equity.

R. K. Jain and Indrani Manna (2009), have researched on Private Equity by comparing the practices in various foreign countries

as well as in the Indian context. It analyzes the PE industry from its inception and discusses its performance and regulatory issues



and some implications in the Indian context. In India, the industry is self-regulatory but it is also controlled under foreign direct

investment norms.

Hence, this present paper is an exploratory attempt to discuss regulatory issues in India as the Private Equity funding has brought

about measures for growth in industry. It also seeks to answer whether there’s a requirement for regulatory framework?

STATUS OF PRIVATE EQUITY FUNDS IN DIFFERENT COUNTRIES

United Kingdom (UK)

Till 1980’s, the growth of UK PE industry was constrained by many factors, such as political environment, cultural impediments,

lack of liquid stock exchange for small and mid-sized businesses. It was only in the mid 1980’s that the state took progressive

steps to promote venture capital industry. The UK regulatory perimeter for PE industry is set by the Regulated Activities Order

(RAO). Although regulations in RAO only define ‘Venture Capital Firm’ and the term ‘Private Equity’ is not mentioned in such

regulations. Hence, any private equity based activity within the scope of ‘venture capital business’ needs authorization from

Financial Services Authority. Private equity firms have to comply with the obligations contained in the Money Laundering

Regulations, 2003 and Code of Business Requirements.

USA

The US market is today the biggest and most developed private equity market in the world. The first formal PE firm, ARD was

established after World War II in 1946 in the US. However since then PE industry has experienced rapid growth where such funds

were raised specifically as venture capital funds. The number of US based private equity based deals rose from 12 transactions in

1970 to 2474 deals in 2007. The regulatory environment for private equity firms has also evolved over a period of time, where

the primary requirement consists of registration under US Securities Act of 1933, Investment Company act of 1940 and Employee

Retirement Income Security Act of 1974.Under US Consumer Privacy legislation, private equity funds organized within US must

disclose the firm’s policies and practices with respect to disclosure of non-public personal information.

China

The extent of private equity activity in China is restricted to venture capital. Moreover the foreign investment policies of China

are designed to create a protective environment to encourage development of indigenous industries. In pursuit of controlling

foreign acquisitions of Chinese brands, State administration of Foreign Exchange (SAFE), Ministry of Commerce, the China

Securities Regulatory Commission and State Administration of Taxation in 2006 jointly issued regulations on mergers and

acquisitions of domestic firms by foreign enterprises. Also private equity investors have been directed a long-term investment

approach in China.

INDIAN AND WESTERN DEVELOPED PRIVATE EQUITY FIRMS: A COMPARISON

The strategy and behavior of Indian Private Equity firms and western developed Private Equity firms are very different in the

following modes:

Focus

Indian Private Equity firms focuses on minority investment, while Western developed Private Equity firm’s focuses on controlling

majority stake.

Nature of Companies

Indian Private Equity firms majorly invest in companies that have scope for further growth and expansion, while Western

developed Private Equity firms provide funds for matured companies.

Type of Investments

Indian Private Equity firms make limited use of transactions like leverage buyouts, while Western developed Private Equity firms

on a very frequent basis undertake transactions like leverage buyouts.

Exit Options

Private Equity investing in Indian markets faces the difficulty of exit as compared to Western developed Private Equity firms who

have been largely linked to those of market for Initial Public Offerings (IPO’s).

Nature of Ownership

The Indian Private Equity market is also different from Western developed market in the sense that small family-owned and

family managed businesses account for high proportion of the market and therefore better investment opportunities.



PRIVATE EQUITY REGULATIONS IN INDIA

Private Equity is not a regulated activity, per se, in India, though domestic and foreign venture capital funds are regulated by

SEBI (Venture Capital Funds) Regulations, 1996 and Foreign Venture Capital Regulations, 2000.This section is an attempt to find

out the existing regulations of Private Equity funds in India.

The Securities and Exchange board of India (SEBI) issued its regulations for Venture Capital in 1996, thus establishing agency’s

authority over the funds. There are no regulatory differences between venture capital and private equity firms. Foreign venture

capital funds have been permitted to operate in India since 1995. They may either hold their shares of unlisted companies directly

or route their investments through domestic venture capital funds.

Between 1995 and 2000 new simplified regulations were issued by SEBI for foreign investors related to exit pricing and

repatriation procedures. The Private Equity industry was regulated through the foreign direct investment regulation of the

government under the Indo-Mauritius Double Taxation Avoidance Treaty. There was minimum restriction in exit pricing and

repatriation of capital and these were regulated by Reserve Bank of India.

In 2000, government permitted equity funds with a ‘pass through status’ which means that the fund gets the advantage of no tax

to be levied on the distributed or undistributed income of the funds. To avoid double taxation a Private Equity funds income

would be taxed only once and in the hands of investors.

Based on Chandrasekhar committee on venture capital in January 2000, SEBI announced new guidelines which were amended in

April 2004 which are as follows:

Foreign venture capital investors can invest in India without the need of approval from Foreign Investment Promotion

Board if they register with SEBI.

According to the new rules the minimum investment of a single investor would be Rs. 5,00,000 and a fund’s minimum

corpus should be Rs. 50 million.

The maximum investment by a fund in one company is 25% of the fund’s corpus and the fund is not permitted to invest

in associated companies of ventures that it finances.

In 2004, the fund’s investment level was reduced from 75% to 66.67% of the investable funds in unlisted equity or

equity linked instruments and 33.3% could be invested in initial public offerings or in debt instruments of companies

that have already made an equity investment.

The lock-in period of one year for IPOs was also removed but preferential allotments of equity shares and equity linked

instruments of a listed company required a one year lock up in the company which was financially weak.

The Private Equity industry had a positive impact as they were able to have an exit mechanism within 3 years of listing

if it did not earn profits during that time. This particularly pertained to technology companies.

In April 2004, SEBI removed and simplified restrictions and permitted venture funds to invest in real estate companies,

gold-financing companies, equipment leasing and hire purchase companies registered with the RBI.

CHALLENGES IN PRIVATE EQUITY SECTOR

The following challenges have been observed in Private Equity sector in India, which needs addressal for easy functioning of such

funds:

Exit Options

The problem in India has been that the Private Equity Funds after generating the desired returns wants to exercise exit option from

the market thereby, bringing an adverse effect on the stock market. Such difficulty in exit options also leads to problem of

liquidity, which is another crucial point of debate with Private Equity based companies. Some of the examples where the exercise

of exit options by Private Equity had an adverse impact includes, where Temasek on reduction of their stake to half of their

original investment in ICICI bank led to sharp reduction in the price of shares. Similarly, Carlyle Group reduced their stake in

HDFC to 3.9% by selling 20 million shares of the company and made an exit.

Requirement of Professional Management

Since in India, most of the Private Equity firms are family owned and managed, therefore it is important on part of these firms to

recognize the importance of finding external directors who can provide the knowledge, experience and expertise necessary to help

steer a company through its next stage of growth.

Advisory Leadership Services

Identification of candidates who meet the criteria for critical leadership positions and conduct rigorous due diligence on them is

another challenge they are faced with.



Complex Regulatory Issues

Another challenge faced by Private Equity investment in India is the applicability of complex regulatory issues, as to whether they

should be made universally applicable to banks, hedge funds and private equity funds, given the fact of their diverse investment

objectives.

SUMMARY & CONCLUSIONS

Post recent financial meltdown, there have been widespread concerns about the unregulated nature of Private Equity funds, which

were seen as lacking transparency. In Indian context, Private Equity cover variety of investment options including leveraged

buyouts, distressed debt buyouts, mezzanine buyouts being some examples, but absence of proper regulation has given rise to

some of the interpretational issues, sometimes confusing the public perception, where Private Equity is treated synonymous with

Venture Capital. Though Private Equity stands to assume a bigger role as growth enablers, but regulatory obstacles remain. For

instance, Private Equity funds are impeded by ambiguities about their treatment in Indian securities and tax laws. Moreover, in a

recent announcement by Economic Affairs Secretary, the Private Equity funds which have been unregulated till date have been

proposed to be brought under SEBI Regulations. Also, at present there does not exists any provision that enables reporting of

sources and investments of private equity funds. Hence, there’s a need for regulatory status which requires the disclosure of

unknown identities investing in Indian private equity market.

Thus, in light of aforesaid role of Private Equity in a developing country like India, it may be concluded that “there’s a strong

need of regulatory framework which not only brings information, transparency and disclosure in the Indian Industry but also helps

in raising corporate governance standards, improves liquidity, enhances valuations and improves organizational capability”.

REFERENCES

1. Chandrashekhar, C. P., (2007). “Private Equity a New Role for Finance”, Economic and Political Weekly, Vol. 42 No.

13, March 3.

2. Srinivasan, Kritika, (2006). “Private and Voluntary Agencies in Sold Based Management”, Economic and Political

Weekly, Vol. 41 No. 22, June 3.

3. Mohan, T. T. Ram, (2005). “Taking Stock of Foreign Institutional Investors”, Economic and Political Weekly, Vol. 40

No. 24, June 11.

4. Dutta, Amrita, (2009). “Public Private Partnerships in India - A Case for Reform?”, Economic and Political Weekly,

Vol. 44, No. 33, Aug 15.

5. Rastogi, Sameer, (2008). “Venture Capital and Private Equity: An Impetus to Indian Economy”, Halsbury Law, May.

6. Uttamsingh, Vikram, (2008). “Private Equity Investing in India - A survery of Private Equity Investors and their

Portfolio Companies”, Report by KPMG India.

7. Bain & Company IVCA (2011). India Private Equity Report.

8. Berwin, S. J., (2004). “Legal and Regulatory Aspects of Marketing and Promoting Private Equity Funds Across

Europe”, Special Paper, European Private Equity & Venture Capital Association (EVCA), June.

9. Private Equity Council (2007). “Public Value: A Primer on Private Equity”.

10. Jonathan, Rotolo T., (2004) and Adjunct. (2003). “Centre for Private Equity and Entrepreneurship”, Note On Private

Equity Careers.

11. Kaplan, Steven N., and Per Stro¨mberg, (2008). “Leveraged Buyouts and Private Equity”, Journal of Economic

Perspectives, Vol. 22, No. 4.

12. Wright, Mike; Kevin Amess; Charlie Weir, and Sourafel Girma, (2009). “Private Equity and Corporate Governance:

Retrospect and Prospect”, Corporate Governance - An international Review.

13. Prowse, S. D., (1998). “The Economics of the Private Equity Market”, Federal Reserve Bank of Dallas Economic

Review, Third quarter. Dallas: Federal Reserve Bank.

14. Barend, de Beer, and Zeph Nhleko, (2009). “Measuring the Economic Impact of Private Equity Funds: The South

African Experience”.

15. Jain, R. K., and Indrani Manna, (2009). “Evolution of Global Private Equity Market: Lessons, Implications and

Prospects for India”, Reserve Bank of India Occasional Papers Vol. 30, No. 1.

*****



ELEVATING PRIORITY SECTOR THROUGH BANK CREDIT

S. Rajamohan14 D. Durairaj15

ABSTRACT

Agriculture plays an important role in the economic development of India. They depend purely on the agricultural and allied

activities. They require money to carry out the farming activities. They spend money during cultivation period and earn money

during harvest seasons. In earlier days, for finance, the farmers purely relied on the local money lenders, indigenous bankers,

creditors, commission agents etc. and thereafter cooperative banks were introduced and farmers enjoyed out of them. In 1969

the banks were nationalized and the banks were extended their services to the rural population, in order to uplift the farming

communities.

The banks also introduced lot of financial assistance programmes like PMRY loan, Crop loan, fertilizer loan, crop insurance

and the like. The RBI also directs all bankers to provide credit to agriculture liberally for purpose of upliftment of their living

standard. With this background, this paper has prepared to provide the ways to promote credit facilities to agriculture

community and add strength to the priority sector.

KEYWORDS

Credit, Priority Sector, and Economic Growth etc.

INTRODUCTION

Agriculture plays an important role in the economic development of India. Agriculture is the source of livelihood of more than 70

per cent of the population in India. To meet the requirement of the growing population and rapid developing economy, agriculture

has to grow fast and get modernized. This requires the use of high pay off inputs, adoption of high yielding varieties, fertilizers,

plant protection chemicals, modernized equipment and machineries which need huge investment. The rural agricultural sector of

the Indian economy is labour abundant, land poor and capital scarce. So it would be very difficult to get the benefits of

modernization of agriculture without adequate credit to the farmers at reasonable interest.

The small farmers are the most hapless object of the private money lenders who are free to recover their loans by high handed and

attachment of the crop of the poor farmers as well as their personal belongings, land and living residences. Available resource

base and the capacity to generate sufficient levels of financial resource within the rural sector particularly in agricultural sector

are, however limited at present. Institutional financing viewed from this angle as a principal resource of external finance to

support in a planned manner. Institutional credit enables the farmer to procure the necessary money of production and creates

contributing to climate for enhanced output. Since institutional credit exerts a push effect and has a catalytic role in development

process, provision of adequate, timely and liberal credit to the farmer has become an integral part of the agricultural development

policy in India.

As a result, agricultural credit service in the country is provided through three main channels, namely, commercial banks, regional

rural banks and co-operative banks. Through this source agriculturist can avail credit and mark use into agro business. So in this

term credit is help as a ladder to agriculture sector.

In this paper highlights the credit is help to agriculture sector, connotation of priority sector, impression of priority lending,

Resolution of priority sector, priority sector segments, effect of lending on bank management.

CONNOTATION OF PRIORITY SECTOR LENDING

Through the priority sector advance a plenty of connotation we can say, therefore here mentioned some of the significance of

priority sector lending:

Priority sector lending support the economy of the country.

It improves the particular area and promotes employment.

It assists the government in reducing unemployment.

Helps to improve the living standards of the nation.

Provide the financial assistance to weaker section.

Promote education by providing financial add to institute.

It improves and promote agriculture sector which is the back bone of the economy.

It creates a balance between assets and liabilities.

The overall impact on the bank management is very pleasant.

14Professor, Alagappa Institute of Management, Alagappa University, Tamilnadu, India, [email protected] 15Research Scholar, Alagappa Institute of Management, Alagappa University, Tamilnadu, India, [email protected]





CREDIT IS LADDER TO AGRICULTURE SECTOR

Credit is an important one for cultivation and it is needed from cultivation to harvest. In between, the farmers require finance for

various purposes such as purchasing the fertilizers, pesticides, manures and other allied things. For such kind of activities, in those

days, the farmers were depending on the local money lenders, through which they were in trouble and some of them committed

suicide due to the higher interest rate and unable to repay the amount.

Followed by our Indian government has taken steps to increase the agricultural credit. Its objectives have been to replace

moneylenders, to relieve the farmers from indebtedness, and to achieve higher levels of agricultural credit, investment, and

output. India's success in replacing moneylenders has been outstanding. Between 1951 and 1971 their share of rural credit appears

to have dropped from more than 80 per cent to 36 per cent. Still, institutional credit is far from reaching all farmers. Only about a

quarter of cultivators borrow, and not more than 2 per cent take out long-term loans.

SYNOPSIS OF PRIORITY SECTOR

At a meeting of the national credit council held in July 1968, it was emphasized that commercial banks should increase their

involvement in the financing of priority sectors like agriculture and allied activities. The description of the priority sector was

later in 1972 on the basis of this report submitted by the informal study group on statistics relating to advance to the priority

sectors constituted by the reserve bank in May 1971. On the basis of this report, the reserve bank prescribed a modified return for

reporting priority sector advances and certain guidelines were issued in this connection of priority sector.

Although initially there was no specific target fixed in respect of priority sector lending, in November 1974 the banks were

advised to raise the share of these sector in their aggregate advances to the level of 33 1/2 per cent by March 1979. At a meeting

of the union finance minister with the chief executive officers of public sector banks held in March 1980, it was agreed that banks

should aim at raising the proportion of their advances to priority sector to 40 per cent by March 1985.

Subsequently on the basis of the recommendation of the working group on the modalities of implementation of priority sector

lending and the twenty points economic programmed by banks, all commercial banks were advised to achieve the target of

priority sector lending at 40 per cent of aggregate bank advances by 1985. Sub targets were also specified for lending to

agriculture and the weaker sections within the priority sector.

Since then, there have been several changes in the scope of priority sector lending and the target and sub targets applicable to

various bank groups. On the basis of the recommendations of the internal working group, set up in reserve bank to examine,

review and recommend changes, if any, in the existing policy on priority sector lending including the segments constituting the

priority sector, target and sub- targets, etc., and the comments/suggestions received thereon from banks, financial institutions,

public and the Indian banks association (IBA), it has been decided to include only those sectors that impact large segments of

population and the weaker sections, and which are employment- intensive, as part of the priority sector.

PURPOSE OF PRIORITY SECTOR LENDING

The banks are issue the lending on priority sector in the following purpose:

Improve the economic condition of the country.

Facilitate the community by the direct and indirect finance to agriculture people.

Upliftment of living standard of rural and agricultural people.

The setup the more small industry in the rural area.

Generation employment in the rural people.

Reduction of mandatory credit to a large number of sector and sections, including marginal farmers, cottage industries,

small trade and services and low income housing incentives.

Improve credit flow to small scale industries and food crop agriculture as well as temporary credit.

Assure credit to new industries and new professions by the non- poor section.

Improve the life quality of weaker section.

SEGMENTS ON PRIORITY SECTOR LENDING

Priority Sectors

The Reserve Bank of India classified in the following categories is priority sector namely, Agricultural and allied activities:

Development of Dairy and Animal Husbandry.

Development of Fisheries.

Development of Poultry, Piggery.

Development and maintenance of Stud farms, Beekeeping, Sericulture, etc. However, breeding of race horses cannot be

classified under this head.

Purchase of Bullock Carts, Camel Carts, Pack Animals etc.

Distribution of inputs for allied activities such as poultry feed, cattle feed, etc.



Small Scale Industrial Units, Equipment/System for development of new and renewable source of energy, etc.

Small Scale and Ancillary Industries such as:

Flaying and tanning.

Leather goods.

Pottery.

Hand pounding of paddy and cereals.

Rice Mills, including flour mills and bakeries.

Canning of fruits and vegetables.

Manufacturing and processing of agricultural & marine products, and forest produce including beverage

industries.

Other village industries such as carpentry and black-smithy, bee-keeping and honey and honey products.

New and renewable sources of energy:

Flat plate solar collectors.

Concentrating and pipe type solar collectors.

Solar cookers.

Cottage industries, Khadi & Village Industries.

Tiny industries with an investment in plant & machinery up to Rs. 25 lakh irrespective of location of the unit.

Small Scale Service & Business (Industry Related) Enterprises (SSSBEs).

Food and agro based processing and forestry.

Loans and advances by primary (urban) co-operative banks.

Loans and advances by scheduled primary (urban) co-operative banks.

Leasing & Hire Purchase Finance by Scheduled PCBs.

Advances to Small Road and Water Transport Operators.

Retail Traders.

Small Business Enterprises.

Agents selling goods on commission basis.

Booking, clearing and forwarding agents.

Estate agents.

Press cum publishing houses, etc.

BANK CREDIT TO PRIORITY SECTOR

In India, table below shows the total amount spend by commercial banks to agriculture and allied activities in the years from 2002

to 2011, in this amount utilized by farmers in various agriculture activities such as buy a seeds, fertilizers, pesticides, cultivation

equipment’s etc.

Table-1: The Banks Credit To Priority Sector (Amount in Crore)

Sl. No Years Amount Percentage Sl. No Years Amount Percentage

1 2002 404354 - 6 2007 406404 - 2.78

2 2003 399494 1.20 7 2008 411816 -1.33

3 2004 402222 - 0.68 8 2009 421316 -2.30

4 2005 396394 1.44 9 2010 438129 -3.99

5 2006 395386 0.25 10 2011 438340 -0.04

Source: RBI bulletin.

Table1 renders credit issued by the scheduled commercial banks in India during one decade. All the years’ credit issuing capacity

was increased year by year in banks, except 2005 and 2006. In those years alone decreased credit facility, remaining all the years

going ladder from 2002 to 2011. Afore said all segments of priority sectors, the bankers are issuing the credit, in order to make

use of it all advances by rural people.

OUTCOME PRIORITY SECTOR PRODUCTS IN INDIA

Farmers need credit for buying all agricultural inputs such as seeds, fertilizers etc. in order to all commercial banks are issued

advance to an individual for agriculture cultivation. Through the credit utilized by the farmers following outcomes of agriculture

product, is shown in Table2.

The table also enlightens that, the outcome of some items of agriculture production past 10 years. In which a selected agricultural

commodities are taking into consideration.



Table- 2: Few Items of Priority Sector Product - Outcome from 2002 to 2011 (Million Tons)

S. No Agriculture Production 2002-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11

1 Food grain 174.8 213.2 198.4 208.6 217.3 230.8 234.5 218.2 229.7

2 Rice 71.8 88.5 83.1 91.8 93.4 96.7 99.2 89.1 90.0

3 Wheat 65.8 72.2 68.6 69.4 75.8 78.6 80.7 80.7 81.7

4 cereals 163.6 198.3 185.2 195.2 203.1 216.0 219.9 203.6 215.9

5 pulses 11.1 14.9 13.1 13.4 14.2 14.8 14.6 14.6 16.0

6 Kharif Food grain 87.2 117.0 103.3 109.9 110.6 121.0 118.1 103.8 NA

7 Rabi food grains 87.5 96.2 95.1 98.7 106.7 109.8 116.3 114.4 NA

8 Oilseeds 14.8 25.2 24.4 28.0 24.3 29.8 27.7 24.9 28.2

9 Sugarcane 287.4 233.9 237.1 281.2 355.5 348.2 285.0 277.8 321.0

Source: Survey of Indian Industry 2011.

IMPACT OF BANKS ON AGRICULTURE LENDING

The agricultural credit that has been pursued for three decades and has clearly benefit current borrowers and farm households

formerly indebted to moneylenders. It has also encouraged fertilizer use and investment in agriculture. It has been less successful

in generating viable institutions and has failed to generate agricultural employment. The policy's costs to India's government have

been high as portfolio losses associated with poor repayment ultimately have to be borne by the government or one of its

institutions under optimistic assumptions.

The benefits of the agricultural income are at the best not more than 13 per cent higher than the cost to the government of the

extra agricultural credit. If assumptions about the cost of supplying the credit and about repayment rates are less optimistic, the

social costs and the costs to the government of providing the credit would have exceeded the benefits in agricultural income. The

expansion of commercial banks to rural areas paid off in farm growth, employment, and rural wages.

CONCLUSIONS

This paper concluded that the banks are done by wondering job. Priority sector lending has a great impact on bank management.

This is the powerful tool to support the economy of the country and the nation as well. Despite of the banks should increase the

area of lending performance, as it is the effective way to support the economy and to reduce the unemployment, and promote

industry. And the amount for lending should be increase so that the maximum community can be supported to individual in

setting their business as well as priority sector activities.

REFERENCES

1. Abate, Adinew, (2000). “Agricultural Credit Policy and Institutional Financing in Karnataka, India – An Economic

Analysis”, M.Sc. (Agri.) Thesis, Uni. of Agricultural Science, Bangalore.

2. Ajjan, N., (1994). “An Analysis of Performance of Cooperative Credit Institutions in Tamil Nadu”, Indian Journal

of Agricultural Economy.

3. Anonymous, 2006, Dharwad District at a Glance. Districts Statistical Office, Dharwad.

4. Anonymous, 2007, “Retail Boom to Fuel Stepped Up Farm Loan By Banks”, Economic Times Finance, Daily,

March, 27.

5. Barman, K. Kandarpa, (1994). “Financial Sector Reforms – Some Implications on Rural Credit”, Financing

Agriculture, 26(4).

6. Survey of Indian Industry, 2011.

*****

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[email protected],[email protected]





KINETICS OF INDIVIDUAL INVESTORS’ BEHAVIOUR – A CONCEPTUAL VIEW

Mahabaleswara Bhatta H. S.16

ABSTRACT

The paper attempt has been made to understand the conceptual frame work of behavioral finance. Though, no study has been

successful in exactly predicting the investors’ behavior, it is increasingly realized that the “Efficient Market Hypothesis” is no

longer valid. The Capital Asset Pricing Model is, at best, validated as an Expectational Model”. The Fama –French empirical

study has proved that the stock’s historical beta has no relationship to the level of its historical return. As a step further in the

study of financial markets, the behavioral finance attempts to better understand and explain how emotions and cognitive errors

influence investors and in turn, their decision making process.

This paper, finally, provides for a discussion on the dynamics of behavioral finance to throw light on the efficiency of the

financial markets as well as explain many stock market anomalies, market bubbles and crashes.

The paper also suggests for a proper time bound program to educate and counsel the investors about the wisdom required in

trading and the possible threats from the shady dealings of the sharks in the ocean of stock market.

KEYWORDS

Kinetics, Behavioral Finance, Expectational Model, Cognitive Errors, Market Bubbles & Crashes, and Sharks etc.

INTRODUCTION

Gone are the days when investors used to invest in shares with the primary purpose of getting dividend. The expectation that the

dividend yield is much more than the other forms of returns, implicitly covered the risk of either getting lesser returns or loosing

the capital itself. The wide spread small investors, mostly without much insight about the insider and information about the

trading, depended on the declaration of the dividend. Higher profit and thereby, the higher dividend, meant a good intrinsic health

of the company. Lack of accounting knowledge, asymmetric information, window dressing, unethical practices (Barrings Bank

Case), had obscured the illusion of profit. Investors at loss, would blame the luck and odds.(Tversky 1979).

But today’s investor is highly knowledgeable, because of the well developed communication technology. Powered by the

analytical tools and sound theoretical knowledge, the investor is in a far better position to take wiser decisions. Much of economic

and financial theory is based on the notion that individual investors act rationally and consider all available information in the

decision making process.

INSTITUTIONAL INVESTORS AND INDIVIDUAL INVESTORS

Institutional investors like Life Insurance companies, Mutual Funds have a major chunk of share in the investment process. The

fund managers are professionals. The individual investors lack professionalism, but retail investors play an important role to make

stock market to function in a healthy manner over a long term and to give a reasonably good return.

FACTORS GUIDE THESE SMALL INVESTORS TO ACTIVELY PARTICIPATE IN THE INVESTMENT PROCESS

Uncertainty and Risk

Uncertainty is the driving force for trading tricks. Knowledge base is utilized to beat the uncertainty, but it has always been

elusive. The basic risk behaviors are; a) risk averse, b) risk –indifferent, and c) Risk seeking.

Risk adverse investors require higher expected returns to compensate them for taking greater risk. Risk seekers, theoretically,

enjoy risk taking and hence, they are willing to give up some return to take more risk. This behavior does not help the investor

much to maximize return. For Risk –indifferent investors, the required return does not change as risk increases. This attitude is

nonsensical in almost any business context.

The Bailard, Biehl & Kaiser Five-Way Model divides investors into five categories: a) "Adventurers" are risk takers and are

particularly difficult to advise, b) "Celebrities" like to be where the action is and make easy prey for fast-talking brokers, c)

"Individualists" tend to avoid extreme risk, do their own research, and act rationally, d) "Guardians" are typically older, more

careful, and more risk averse, and e) “Straight Arrows" fall in between the other four and are typically very balanced.

16Principal & Director, Global Institute of Management Sciences, Karnataka, India, [email protected]




Efficient Market Hypothesis

The Capital Asset Pricing Model was developed to explain the behavior of security prices and provide a mechanism whereby

investors could assess the impact of their portfolio’s overall risk and return. Efficient market with the characteristics such as: a)

many small investors, b) all having the same information and expectations with respect to securities, c) no restriction on

investment, d) no taxes, and e) no transaction costs. Hypothesis that all investors are rational and view securities similarly. They

are risk averse, preferring higher returns and lower risk. The security market line is best used to measure the changing preferences

of investors – risk premiums increase with increasing risk avoidance.

Against the Gods

Contrary to the general belief that knowledgeable investors act rationally, the researchers have evidenced that this is frequently

not the case. Irrational behavior and repeated errors have been documented in academic studies.

Peter L. Bernstein in his “Against the Gods: The remarkable story of risk”, states that the evidence reveals repeated patterns of

irrationality, inconsistency, and incompetence in the ways human beings arrive at decisions and choices when faced with

uncertainty.

Prospect Theory

Contrary to the expected utility theory of Von Neumann-Morgenstern, Tversky and Kahneman have found that people place

different weights on gains and losses and on different ranges of probability. Investors are much more distressed by prospective

losses than they are happy by equivalent gains. Investors will respond differently to equivalent situations depending on whether it

is presented in the context of losses or gains. They are willing to take more risks to avoid losses than to realize gains. Faced with

sure gain, most investors are risk-averse, but faced with sure loss, investors become risk-takers.

Logic and Investment Behavior

The risk of loosing goes on increasing as one continues trading in stocks. Let us take an example. X invests Rs 1, 00,000 in stock

M (IPO) with a face value of Rs 10 each. In five years, if the share value goes up to Rs 50 and if he sells all the shares, he will be

netting Rs. 5, 00,000. Now, if he wants to invest in the same type of shares, he will have to invest entire Rs 5, 00,000 without

pocketing any gain. This in reality results in no gain. On the other hand, if he pockets Rs 1, 00,000 and invests Rs 4, 00,000 in a

low rated share at Rs 40 each, he runs the risk of loosing because, there may not be any gain for such low rated shares in future.

If after two years, the value per share comes down to Rs 30 and if he sells such shares (fearing further decline) the loss will be Rs

1,00,000,resulting in a net gain of only Rs 3, 00,000 over the initial investment. On the other hand, if the value goes up to Rs 50,

the gain will be Rs 1, 00,000 resulting in a overall gain of Rs 5, 00,000. The net difference is therefore, limited to Original

Investment + Net realization +/- gain or loss in each trading. The investor has to continue to take risk every time trading takes

place in order to maximize the returns.

Fear of Regret (Empathy gap)

Meir Statman stated that investors tend to feel sorrow and grief after having made an error in judgment. Investors deciding

whether to sell a security are typically emotionally affected by whether the security was bought for more or less than the current

price. According to this approach, investors avoid selling stocks that have gone down in order to avoid the pain and regret of

having made a bad investment. The embarrassment of loosing prevents them selling such investments.

Trend Influence (Impact Bias)

Investors typically give too much weight to the recent experience and extrapolate recent trends with the help of long run averages

and statistical tools. They tend to become more optimistic when the market goes up and more pessimistic when the market goes

down. Robert J. Shiller found that at the peak of the Japanese market, 14% of Japanese investors expected a crash, but after it did

crash, 32% expected a crash.

Contrary to this, when high percentages of investors become overtly optimistic or pessimistic about the future, it is a signal that

the opposite scenario will occur.

Overconfidence

Overconfidence may in part stem from two other biases, self-attribution bias and hindsight bias. Self-attribution bias refers to

people’s tendency to ascribe any success they have in some activity to their own talents, while blaming failure on bad luck, rather

than on their ineptitude.

Doing this repeatedly will lead people pleasing but erroneous conclusion is that they are very talented. For example: investors

might become overconfident after several quarters of investing success [Gervais and Odean (2001)].



Hindsight bias is the tendency of people to believe, after an event has occurred, that they predicted it before it happened. If people

think they predicted the past better than they actually did, they may also believe that they can predict the future better than they

actually can.

They are over confident in areas where they have some knowledge. However, increasing levels of confidence frequently show no

correlation with greater success. Investors are consistently overconfident in their ability to outperform the market however, most

fail to do so. They see other investors’ decisions as the result of disposition but they see their own choices as rational. They

frequently trade on the information they believe to be superior and relevant, when in fact it is not and is fully discounted by the

market. This results in frequent trading and consistently high volumes in financial markets.

Vicinity Bias

Gur Huberman of Columbia University recently found that investors strongly favor investing in local companies that they are

familiar with. The study provides evidence that investors prefer local or familiar stocks even though there may be no rational

reason to prefer the local stock over other comparable stocks that the investor is unfamiliar with.

Human Trait

The basic human trait is the tendency to gamble and assume unnecessary risks. Entertainment and ego appear to be some of the

motivations for investors' tendency to speculate. They tend to remember successes, but not their failures, thereby unjustifiably

increasing their confidence. As John Allen Paulos states in his book “Innumeracy”, "There is a strong general tendency to filter

out the bad and the failed and to focus on the good and the successful".

Herd Behavior

Many investors follow what others have done. They feel it comfortable and safe to go along with others on whom they have

confidence. Researchers have theorized that investors follow the crowd and conventional wisdom to avoid the possibility of

feeling regret in the event that their decisions prove to be incorrect. Many investors find it easier to buy a popular stock and

rationalize it going down since everyone else owned it and thought so highly of it. Even though they loose, they feel happy

because others have also lost! .Justification is easy when a bad event occurs to others also. Buying a stock with a bad image is

harder to rationalize if it goes down. The “Crowd effects” have resulted in nasty turbulences in the stock market.

Gender Difference

In India most of the individual investors are men. Over recent years more and more women have started participating in the

investment process. Dominance of men has its roots to history, tradition, culture and chauvinism. Psychographics describe

psychological characteristics of people and are particularly relevant to each individual investors strategy and risk tolerance. The

investors background and past experience plays a significant role in the decision making process. Researchers have found that

women tend to be more risk averse than men and passive investors have typically became wealthy without much risk while active

investors have typically become wealthy by earning it themselves.

Market Extremity

In recent years it has been experienced that the market frequently mispriced the stocks. This is most often caused by human

emotions of fear and greed. At the height of optimism, greed moved the stocks beyond their intrinsic value, creating an overpriced

market. At other times, fear moved prices below intrinsic value, creating an undervalued market. Thus, as William Gross stated,

markets invariably move to undervalued and overvalued extremes because human nature falls victim to greed and/or fear.

Investors trade for both cognitive and emotional reasons. They trade because they think they have information when they have

nothing but noise, and they trade because trading can bring the joy of pride. Trading brings pride when decisions turn out well, but

it brings regret when decisions do not turn out well. Investors try to avoid the pain of regret by avoiding the realization of losses,

employing investment advisors as scapegoats, and avoiding stocks of companies with low reputations.

In recent years we find heavy ups and downs in the share price indices. Market bubbles and crashes are common, inspite of

advanced level information flow and technological assistance. Side by side, we also witness stock market scandals to the utter

dismay of the investors. Often, the governments and regulatory bodies intervene to rescue the stock market. Since trading has

been the hallmark of investment, it is necessary that the interests of the investors are properly safeguarded.

CONCLUSIONS

There is a paradigm shift of attention from the Efficient Market Hypothesis to Behavioral Finance. Though there is no concrete

solution to the unpredictable human behavior, several studies by the behaviorists have time and again thrown light on the

consequences of wrong judgments. These studies have helped to provide a broader framework within which the investors can

critically inspect their investing decisions. A set of behavioral principles would certainly guide the investors to be in the race

continually and yet make reasonable gains in trading.



In India, Securities Exchange Board has realized the importance of the presence of individual investors for a healthy growth of

stock market. To this end, there is a need for the concerned to educate and counsel the investors from time to time about the

wisdom required in the stock trading and also about the unethical and tactical practices of brokers, shady dealings of the

companies and the insider trading.

Behaviorists need to be involved in developing suitable programs by the regulatory authorities that reach the individual investors

and enlighten them about the tenets of sound and safe trading and the possible threat of shady dealings from the sharks in the

ocean of stock market. Investors can identify some of the biases and apply that knowledge to their own investment decision

making process and if need be take corrective action to hopefully make their future financial decisions a bit more rational and lot

more lucrative as well. There is enough scope to undertake research studies on the behavioral aspects of the investors from

different income groups, age groups, geographical and educational backgrounds.

REFERENCES

1. Kahneman, Daniel, and Mark Riepe, (1998). “Aspects of Investor Psychology”, The Journal of Portfolio Management.

2. Kahneman, Daniel, and Amos Tversky, (1979). “Prospect Theory: An Analysis of Decision Making Under Risk”,

Econometrica.

3. Joshua, D. Coval; David A. Hirshleifer, and Tyler Shumway, (2005). “Can Individual Investor Beat The Market?”,

Harvard Bsiness School Working Paper 02-45, HBS Finance Working Paper 04-025.

4. Jawaharlal, (1992). “Understanding Indian Investors”, ed1, Global Business Press, New Delhi.

5. Lawrence, J.Gitman, (2005). “Principles of Managerial Finance”, 10th ed. Pearson.

6. Statman, Meir, (1988). “Investor Psychology and Market Inefficiencies”, Equity Markets and Valuation Methods, The

Institute of Chartered Financial Analysts.

7. Terrance, Odean, (1999). “Why Do Investors Trade too Much?”, American Economic Review, 89(5),1279-1298 .

8. Terrance, Odean, (1988a). “Are Investors Reluctant to Realize their Losses?”, Journal of Finance, 53,1775-1798.

9. Richard, H. Thaler, and Nicholas Berberis, (2002). “A Survey of Behavioral Finance”, National Bureau of Economic

Research, Working Paper 7716, Yale School of Management.

10. Robert, J. Shiller, (2003). “From Efficient Market Theory to Behavioral Finance”, Yale University - Cowles, Discussion

Paper 1385.

11. Showry, Mendemu, and Tabassum Sultana, (2007). “Investors’ Behavior”, PES Business Review, Vol2, Issue1, pp 43-

49.

12. Social Science Research Network (SSRN), Abstract Data Base, Search Results.

13. www.investorhome.com

14. www.proquest.com

15. http://digitalcommons.pace.edu/honorscollege theses/44

*****

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Editor-In-Chief


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EXAMINING THE RELATIONSHIP BETWEEN STOCK PRICES AND MACROECONOMIC

VARIABLES - EMPIRICAL EVIDENCE FROM INDIA

Kamini Tandon17 Nidhi Malhotra18

ABSTRACT

In this paper attempt has been made to navigate the correlation and causal relation between stock market and real economy in

the context of Indian economy. The research derives its results from the study of empirical data for five years from January

2006 to December 2010. These findings point towards the developing phase that Indian economy is going through over the

last six decades. This entails that stock market in India still cannot be symbolized as the “indicator” of financial health of the

country.

The study clearly indicates that since real economic variables are not affecting the stock market index hence certainly some

exogenous variables do exist having an impact on the stock prices.

INTRODUCTION

The movement of stock indices is highly sensitive to the changes in fundamentals of the economy and to the changes in

expectations about future prospects. Expectations are influenced by the micro and macro fundamentals which may be formed

either rationally or adaptively on economic fundamentals, as well as by many subjective factors which are unpredictable and also

non quantifiable.

It is assumed that domestic economic fundamentals play determining role in the performance of stock market. However, in the

globally integrated economy, domestic economic variables are also subject to change due to the policies adopted and expected to

be adopted by other countries or some global events. The common external factors influencing the stock return would be stock

prices in global economy, the interest rate and the exchange rate.

Recently, it is observed that contagion from the US sub-prime crisis has played significant movement in the capital markets across

the world as foreign hedge funds unwind their positions in various markets. The modern financial theory focuses upon systematic

factors as sources of risk and contemplates that the long run return on an individual asset must reflect the changes in such

systematic factors. This implies that securities market must have a significant relationship with real and financial sectors of the

economy. This relationship generally viewed in two ways.

The first relationship views the stock market as the leading indicator of the economic activity in the country, whereas the second

focuses on the possible impact the stock market may have on aggregate demand, particularly through aggregate consumption and

investment. The former case implies that stock market leads economic activity, whereas the latter suggests that it lags economic

activity. Knowledge of the sensitivity of stock market to macroeconomic behaviour of key variables and vice-versa is important in

many areas of investments and finance. This research may be useful to understand this relationship.


Varying evidences of causal links of stock returns and macro variables have been found in the literature using various asset

pricing specifications. In the literature, widely popular CAPM has been severely challenged since returns can be predicted from

other financial factors. This has led to the development and testing of various alternative asset pricing specifications, such as the

Arbitrage Pricing Theory (APT) and Present Value Model (PVM). In the context of macro dynamics of stock returns, APT

assumes that returns are generated by a number of macroeconomic factors. It allows multiple risk factors to explain asset returns.

Chen, Roll and Ross (1986) have argued that stock returns should be affected by any factor that influences future cash flows or

the discount rate of those cash flows.

In an empirical investigation they found that the yield spread between long and short term government bonds, expected inflation,

unexpected inflation, nominal industrial production growth and the yield spread between corporate high and low grade bonds

significantly explain stock market returns. An alternative way of linking macroeconomic variables and stock prices is the

discounted cash flow or present value model (PVM). This model relates the stock price to future expected cash flows and the

future discount rate of the cash flows. Again, all macroeconomic factors that influence future expected cash flows or the discount

rate by which the cash flows are discounted should have an influence on the stock price. The advantage of the PVM model is that

it can be used to focus on the long run relationship between the stock market and macroeconomic variables.

Also various theoretical reasons have been explained linking behaviour of stock prices and key macro economic variables. For

instance, Friedman (1988) suggests wealth effect and substitution effect as the possible channels through which stock prices might

directly effect money demands in the economy. Friedman (1988) expected that the wealth effect will dominate and thus the

17Assistant Professor, Banarsidas Chandiwala Institute of Professional Studies, New Delhi, India, [email protected] 18Assistant Professor, Banarsidas Chandiwala Institute of Professional Studies, New Delhi, India, [email protected]





demand for money and stock prices to be positively related. The theoretical basis to examine the link between stock prices and the

real variables are well established in economic literature, e.g., in Baumol (1965) and Bosworth (1975).

The relationship between stock prices and real consumption expenditures, for instance, is based on the life cycle theory,

developed by Ando and Modigliani (1963), which states that individuals base their consumption decision on their expected life

time wealth. Part of their wealth may be held in the form of stocks linking stock price changes to changes in consumption

expenditure. The impact of real sector macro variables on equity returns has been much more difficult to establish. Mukherjee and

Naka (1995) reveal that co-integration relation existed and positive relationship was found between the Japanese industrial

production and stock return. However, Cutler, Poterba, and Summers [1989] (CPS) find that Industrial Production growth is

significantly positively correlated with real stock returns over the period 1926-1986, but not in the 1946- 85 sub-period.

In Indian context, Bhattacharya and Mukherjee (2002) studied the nature of the causal relationship between stock prices and

macro aggregates for the period of 1992-93 to 2000-2001.Their results show that there is no causal relationship between stock

price and macro economic variables like money supply, national income and interest rate but there exists a two way causation

between stock price and rate of inflation. Their results also indicate index of industrial production lead the stock price. As

discussed above, literature reveals differential causal pattern between key macro economic variables and stock prices. This

relationship varies in a number of different stock markets and time horizons in the literature.

This paper will add to the existing literature by providing robust result, which is based on correlation and regression analysis

between NIFTY 50 Index which is considered as dependent variables while four economic variables which are randomly chosen

.i.e. Indian rupee exchange rate in us dollar, broad money (M3), Inflation rate and index for industrial production are four

independent variable. Data are taken on monthly basis for five years i.e. January 2006 to December 2010.

OBJECTIVES OF STUDY

To study and analyze the relationship between macro economic variables -exchange rate INR/USD, inflation rate, M3 and IIP and

stock market Index NIFTY 50.


The modern financial theory focuses upon systematic factors as sources of risk and contemplates that the long run return on an

individual asset must reflect the changes in such systematic factors. This implies that securities market must have a significant

relationship with real and financial sectors of the economy. Knowledge of the sensitivity of stock market to macro-economic

behavior of key variables and vice-versa is important in many areas of investments and finance. This research may be useful to

understand this relationship.

The research derives its results from the study of empirical data for five years from January 2006 to December 2010. NIFTY 50

represents the stock market index. Four macroeconomic variables have been randomly chosen for the study which includes:

Exchange Rate INR/USD.

Inflation Rate (Based on WPI).

Broad Money (M3).

Index for Industrial Production (IIP).

To understand the relationship between NIFTY 50 & macroeconomic variable the following dataset variable table is created and

multiple regression model applied so as to understand whether stock performance is significantly dependent on economic

variables or not.

Table-1: The Dataset Variable

NIFTY 50 return Dependent variable

Indian rupee exchange rate in us $ Independent variable 1

Broad money (M3) Independent variable 2

IIP Independent variable 3

Inflation Rate Independent variable 4

Secondary data has been collected through various sources like official website of NSE India, Ministry of statistical and

programme implementation (MOSPI), Ministry of finance, Economic survey, RBI website etc.

The dataset so collected has been analyzed using descriptive statistics like standard deviation, coefficient of determination, mean,

etc. to show the nature and basic characteristics of the variables used in the analysis. Correlation Analysis & Multiple Regression

Analysis have been carried out to understand the causal relationship between stock prices and macroeconomic variables. Also

scatter diagrams have been created to plot the dependent variable against each independent variable in order to visualize the

relationships between the variables and indicate such attributes as strength, direction etc.



DATA ANALYSIS & INTERPRETATION

Table-2: Descriptive Statistics

Table-3: Correlation Matrix

Graph-1: Relation between NIFTY 50 and Indian Rupee Exchange Rate in US$

The coefficient of correlation between the market returns of NIFTY 50 and percentage change in Indian Rupee Exchange Rate In

US $ is -0.2642 which shows a low negative correlation.

Graph-2: Relation between NIFTY 50 and Broad Money (M3)

The coefficient of correlation between the market returns of NIFTY 50 and percentage change in Broad Money (M3) works out to

be 0.0020 which shows low positive correlation.

-40.00

-20.00

0.00

20.00

40.00

60.00

May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12

NIFTY

INDIAN RUPEEEXCHANGE RATE (US $)

-40.00

-20.00

0.00

20.00

40.00

60.00


NIFTY

BROAD MONEY M3

Column1 NIFTY 50 Exchange Rate M3 IIP Inflation Rate

Mean 1.723524182 0.177594764 1.47313833 0.8707 -24.20517994

Standard Error 1.291522607 0.679095826 0.09913069 0.788449 23.86632996

Median 2.776643434 0.152434338 1.46042183 0.669584 0.101112235

Mode 6.075483788 #N/A #N/A #N/A #N/A

Standard Deviation 9.920373381 5.216234019 0.7614373 6.056189 183.3207589

Sample Variance 98.41380801 27.20909734 0.57978676 36.67743 33606.50063

Kurtosis 4.453180504 8.523648535 0.2528386 0.174592 41.2118764

Skewness 0.091509077 -1.060895116 0.69048372 -0.11815 -6.023675514

Range 1.60198996 4.23588086 -0.8225791 -11.2758 -12.70053476

Minimum -30.29079241 -23.60063323 0.17635747 -13.2736 -1292.307692

Maximum 39.19810366 17.67866335 3.7007025 14.62379 256.7164179

Sum 101.6879268 10.47809106 86.9151612 51.37132 -1428.105616

Count 59 59 59 59 59

NIFTY 50 Exchange Rate (Us $) Broad Money M3 IIP Inflation Rate

Nifty 50 1.0000

Exchange Rate (Us$) -0.2642 1.0000

Broad Money M3 0.0020 -0.1744 1.0000

IIP -0.0044 0.2267 -0.1444 1.0000

Inflation Rate -0.1644 -0.1668 0.2024 -0.0896 1.0000



Graph-3: Relation between NIFTY 50 and IIP

The coefficient of correlation between the market returns of NIFTY 50 and percentage change IIP works out to be -0.0044 which

shows very low negative correlation.

Graph-4: Relation between NIFTY 50 and Inflation Rate

The coefficient of correlation between the market returns of NIFTY 50 and percentage change in Inflation rate works out to be -

0.01644 which shows low negative correlation.

Regression equation used in the analysis is as follows: Y = a + B1X1 + B2X2 + B3X3 + B4X4+... + BtXt + u

Where:

a= the intercept.

Y= NIFTY 50 return.

X1= % change in Indian Rupee Exchange Rate in US $.

B1= the slope of Indian Rupee Exchange Rate in US $.

X2 = % change in Broad money (M3).

B2 = the slope of broad money (M3).

X3 =% change in IIP.

B3= the slope of IIP.

X4 = % change in inflation rate.

B4 = the slope of inflation rate.

u= the regression residual.

Table-4: Regression Statistics

Multiple R 0.34144

R Square 0.11658

Adjusted R Square 0.05114

Standard Error 9.66336

Observations 59.00000

Table-5

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 1.5353 2.8996 0.5295 0.5986 -4.2781 7.3486

X Variable 1

( Exchange Rate INR/USD) -0.5905 0.2545 -2.3197 0.0242 -1.1008 -0.0801

X Variable 2

(Broad money -M3) -0.0342 1.7289 -0.0198 0.9843 -3.5004 3.4320

X Variable 3 ( IIP) 0.0764 0.2165 0.3526 0.7257 -0.3578 0.5105

X Variable 4

(Inflation rate) -0.0114 0.0071 -1.6033 0.1147 -0.0258 0.0029

-40.00

-20.00

0.00

20.00

40.00

60.00


NIFTY

IIP

-1500.00

-1000.00

-500.00

0.00

500.00


NIFTY

INFLATION RATE



The degree of association is low as the value of coefficient of determination R2 is quite low i.e. 0.01165 which reveals that the

interdependence between the NIFTY 50 & four specified macroeconomic variables is quite low. The findings indicate that only

11.65 % relationship can be established between the two while the rest is driven by some other factor.

CONCLUSIONS

The integration and interrelation between the stock market and real economy is very evidently witnessed in the developed

economies due to the basic reason that the common masses are very aware and educated about the stock markets and their these

small initiatives of investment collectively help the industries and in due course a country to grow and develop into a prosperous

and powerful economy. In this study, research has been done to navigate the correlation and causal relation between stock market

and real economy in the context of Indian economy.

These findings point towards the developing phase that Indian economy is going through over the last six decades. This entails

that stock market in India still cannot be symbolized as the “indicator” of financial health of the country. The study clearly

indicates that since real economic variables are not affecting the stock market index hence certainly some exogenous variables do

exist having an impact on the stock prices.

NIFTY 50 shows an inverse relationship due to negative correlation with the inflation rate and index for industrial production.

Rise in inflation in the economy leads to a slowdown in economic activity. When economic activity dips, it negatively affects the

future corporate profits and hence, stock prices. Indian rupee exchange rate in term of US$ shows high degree of negative

correlation (-0.2642) while Broad money ( M3) shows positive correlation with NIFTY 50 return as it is the money supply only

which provides liquidity to the economy and increases the purchasing power of the people thus providing an impetus to the

economy to grow further.

However, the relationship between stock returns and macroeconomic variables such as money supply, inflation, industrial

production and exchange rate are statistically insignificant. Finally, the results of the study are not consistently stable with the

results of the previous studies due to differences between the macroeconomic factors used, the period covered, the research

methodology employed and the countries examined.

REFERENCES

1. Abdalla, I. S. A., and V. Murinde (1997). “Exchange Rate and Stock Price Interactions in Emerging Financial Markets:

Evidence on India, Korea, Pakistan, and Philippines”, Applied Financial Economics 7, 25-35.

2. Agrawalla, R. K., (2008). “Share Prices and Macroeconomic Variables in India: An Approach to Investigate the

Relationship between Stock Markets and Economic Growth”, IGIDR Money & Finance Conference, 2008.

3. Ahmed, Shahid, (2008). “Aggregate Economic Variables and Stock Markets in India”, International Research Journal of

Finance & Economics, Issue 14, pp. 141-164.

4. Ando, A., and F. Modigliani (1963). “The Life Cycle Hypothesis of Saving: Aggregate Implications and Tests”,

American Economic Review, Vol. 53, No. 1.

5. Ajayi, R. A., and M. Mougoue, (1996). “On the Dynamic Relation between Stock Prices and Exchange Rates”, Journal

of Financial Research, 19, 193-207.

6. Bahmani-Oskooee, M., and A. Sohrabian, (1992). “Stock Prices and the Effective Exchange Rate of the

Dollar”, Applied Economics, 24, 4, 459-464.

7. Baumol, W., (1965). Stock Market and Economic Efficiency, Fordham University Press, New York.

8. Bhattacharya, B., and Mukherjee J., (2002). “Causal Relationship Between Stock Market and Exchange Rate, Foreign

Exchange Reserves and Value of Trade Balance: A Case Study For India”, www.igidr.ac.in

9. Bosworth, B., (1975). "The Stock Market and Economy", Brookings Papers on Economic Activity, Vol. 2.

10. Chen, N.; R. Roll, and S. Ross, (1986). “Economic Forces and the Stock Market”, Journal of Business, 59, 383-403.

11. Friedman, M., (1988). “Money and the Stock Market”, Journal of Political Economy, 96, 2,221-245.

12. Mukherjee, T. K., and A. Naka, (1995). “Dynamic Relations between Macroeconomic Variables and the Japanese Stock

Market: An Application of Avector Error Correction Model”, The Journal of Financial Research, 2, 223-237.

*****

http://www.igidr.ac.in/



FORECASTING INDIA’S FOOD PRICE INDEX USING ARIMA MODEL

Rahul Ranjan19 Priyanka Varma20 Abhishek Kumar Chintu21

ABSTRACT

The Commodity Food Price Index plays an important role in evaluating the macroeconomic policies of the Government.

Forecasting price indices requires an in depth knowledge of various forecasting techniques which in fact fail at times because

forecasting is more of an art than science and not one thumb of rule can be applied to any phenomenon.

The paper is an attempt to model the monthly Commodity Food Price Index from the year 1991 onwards using a time series

analysis in Stata10. The Box-Jenkins approach has been used to model the data as an ARIMA (2, 1, 1). The model has been

validated by the generation of white noise residual which depicts no serial autocorrelation and also, has been used to forecast

12 month Price Index.

Overall, the forecasting error is small on comparison of the actual price index and the forecasted price index and the

forecasted values follow the trend line of actual Price Index.

KEYWORDS

Commodity, Food Price Index, and Forecasting etc.

INTRODUCTION

Over the last few decades, many different forecasting techniques have been developed in a number of different application areas,

including economics. Generally the time-series approach is the most appropriate and the most accurate approach to generate the

large number of short-term forecasts (James et al, 2007). Time series forecasting is an important area of forecasting in which past

observations of the same variable are collected and analyzed to develop a model describing the underlying relationship.

An industry-oriented forecasting method should meet a number of requirements: it should be easy to implement with conventional

software; it should give quick price forecasts without much of an analytical input; and it ought not to require more information

than is contained in the series being forecast.

Accuracy-wise, the methods involved are quite different in their handling of the data-generating process. As its name suggests,

ARMA is to work with the ARMA component. While it is moderately robust to noisy data, its capabilities in terms of modelling

seasonality and cyclical patterns are limited to seasonal coefficients. Spectral decomposition, on the contrary, can extract a

periodic signal of complex form from the data. However, it cannot handle autoregressive processes and therefore may produce

very poor results for short-run forecasts.

Exponential smoothing encompasses some kinds of ARMA process, can account for a simple seasonal pattern and trends, and is

capable of producing satisfactory predictions in the presence of a considerable amount of noise. It can be seen therefore that, if it

so happens that a component of the data-generating process dominates the others for a majority of series, then the method which

is the most suitable to deal with that component will be, overall, the best performing one. Otherwise, a more broadly applicable

method should be preferred.

Inflation forecasts are routinely used by professional economists for predicting monetary policy and the path of short-term interest

rates. A common descriptive model of monetary policy states that the Federal Reserve increases or decreases short term interest

rates in response to changes in the rate of consumer price inflation and a measure of real activity such as deviations in

unemployment from its natural rate. Thus, forecasts of inflation are important statistics for anyone interested in monetary policy.

Since higher commodity prices are often thought to presage higher rates of inflation, the evolution of commodity prices in turn is

intensively scrutinized by professional economists.

The inflation rate is often seen as an important indicator for the performance of a central bank. Inflation forecasts are therefore an

important element in the set of variables on which forward looking monetary policy decisions are based. Apart from the role of

inflation forecasts as an input to monetary policy deliberations there is also an additional role for inflation forecasts in the national

macroeconomic policy debate. By informing the public about likely trends in inflation the forecast can influence inflationary

expectations and therefore can serve as a nominal anchor for example in the wage bargaining process or for other nominally fixed

contracts like housing rents, interest rates.

Some countries employ core inflation (i.e., excluding items such as food and energy which exhibit high short-run price volatility

resulting from external shocks) while others focus on total consumer price index (CPI). Using total CPI may be more appropriate

in developing countries with inflation targeting regimes for at least two reasons. First, the share of food expenditures in household

19Student, Department of Economics, Faculty of Social Sciences, BHU, Uttar Pradesh, India, [email protected] 20Student, Department of Economics, Faculty of Social Sciences, BHU, Uttar Pradesh, India. 21Research Scholar, Department of Economics, Jai Prakash University, Bihar, India, [email protected]





budgets in developing countries is much higher than in developed countries. Second, food prices in developing countries tend to

be more volatile than in their developed counterparts because they have (1) higher prevalence of fresh over processed foods, (2)

lower share of food away from home in total food budget and (3) higher market imperfections.

Consequently the high share of food in household budget as well as its high price volatility results in substantial impacts on total

CPI. Many developing countries using inflation targeting set inflationary goals based on total CPI. This makes sense for them

since generally their share of food in household expenditures is high and because most of the contracts are indexed to total CPI.

Thus, total CPI is more representative of the loss of the purchasing power of money than any measure of core inflation.

Under inflation targeting, central banks usually employ two types of models to evaluate the effects of monetary policy decisions.

One type of models focuses on estimating short-run forecasts (one to twelve-month horizons), assumes constant interest rates (i.e.

ignoring the transmission mechanism of monetary policy), and includes various types of time series models. The second type

consists of a set of recursive equations describing the transmission mechanism to simulate the impact of monetary policy decision.

These models are complementary in the sense that the short-run forecast models provide initial values for the transmission

mechanism model.

Some food price-forecasting models use a time series approach such as the Autoregressive Integrated Moving-Average (ARIMA)

model in Box and Jenkins (1970). The time series model, which depicts the historical movement of time series data observations,

is a convenient approach because it uses mainly its own price variable to predict food prices. Because none of the time series

models incorporates economic rationale, however, the models’ forecasts may be unreliable when there is a change in economic

conditions.

Price fluctuations are a common feature of well-functioning agricultural product markets. But when these become large and

unexpected – volatile – they can have a negative impact on the food security of consumers, farmers and entire countries. Since

2007, world markets have seen a series of dramatic swings in commodity prices. Food prices reached their highest levels for 30

years during the summer of 2008, collapsing the following winter, before rapidly rising again in the months that followed.

The main factor which motivated me to carry this subject of modelling was its current and extreme importance in our lives.

Everyone eats and is affected by food price changes. Also, its fluctuations render millions of people below the poverty line into

hunger and malnutrition. In this concern, I read several articles by IMF, FAO and Agricultural Market Information system. In

future, I would like to delve deeper into the agricultural commodity future markets and look into factors driving the market.


Forecasting Techniques Coming of Age

On the empirical plane, several stylised facts and time series properties of alternative inflation indicators could be exploited for

policy analysis. First, for measuring inflation expectation and risks, a mute question here is how economic agents form

expectations about future inflation condition. Before formal models such as adaptive expectation (Friedman, 1968) and rational

expectation (Lucas, 1972) models were developed, economists believed in agents forming expectations based on the historical

mean of the inflation rate observed over a sample period of medium-longer time horizon consistent with the maturity of

benchmark financial instrument such as the ten-year treasury bond or the length of a typical business cycle. In recent years,

economists use a variety of time series models to measure time varying inflation expectations in line with the alternative

expectation models. Illustratively, the auto regressive integrated moving average (ARIMA) model is used widely to measure

adaptive expectation component of the inflation rate.

Second, as regards the measure of inflation risk, a commonly adopted measure of inflation risk is the unconditional standard

deviation of inflation rate over a sample period preferably over medium-longer horizon. Since economic agents engage in

dynamic financial risk pricing in a modern financial environment, the conditional or time varying inflation risks based on

generalised auto regressive conditional heteroscedacity (GARCH) models for the inflation rate are becoming popular. Third, for

gauging the inflation persistence, empirical studies have adopted various approaches such as lagged correlation of inflation, the

correlation of inflation with the growth rate of monetary variables, Box-Jenkins ARIMA model and Granger’s spectral density

analysis.

Numerous studies have investigated the relative accuracy of alternative inflation forecasting models. One approach has been to

compare the accuracy of survey respondents’ inflation forecasts relative to univariate time-series models. Another approach is the

methodology associated with the work of Fama (1975, 1977) and recently extended by Fama and Gibbons (1982, 1984). This

approach extracts from observed nominal interest rates the market’s inherent expectation of inflation. Based on a univariate time-

series modelling of the real interest rate, Fama and Gibbons (1984)9 find that the interest-rate model yields inflation forecasts with

a lower error variance than a univariate model, and that the interest-rate model’s forecasts dominate those calculated from the

Livingston survey.

Aidan Meyler, Geoff Kenny and Terry Quinn10 (1998) outlined autoregressive integrated moving average (ARIMA) time series

models for forecasting Irish inflation. It considered two alternative approaches to the issue of identifying ARIMA models - the

Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance, which suggests that

ARIMA forecast has outperformed.



Geoff Kenny, Aidan Meyler and Terry Quinn (1998) focused on the development of multiple time series models for forecasting

Irish Inflation. The Bayesian approach to the estimation of vector 10 autoregressive (VAR) models is employed. This allows the

estimated models combine the evidence in the data with any prior information, which may also be available. A large selection of

inflation indicators is assessed as potential candidates for inclusion in a VAR. The results confirm the significant improvement in

forecasting performance, which can be obtained by the use of Bayesian techniques.

Toshitaka Sekine11 (2001) estimated an inflation function and forecasts one-year ahead inflation for Japan. He found that mark-up

relationships, excess money and the output gap are particularly relevant long-run determinants for an equilibrium correction

model (Eq CM) of inflation.

Tim Callen and Dongkoo Chang12 [1999] found that the Reserve Bank of India (RBI) has moved away from a broad money target

toward a “multiple indicators” approach to the conduct of monetary policy. In adopting such a framework, it is necessary to know

which of the many potential indicators provide the most reliable and timely information on future developments in the target

variable(s).

This paper assesses which indicators provide the most useful information about future inflationary trends. While the broad money

target has been de emphasized, developments in the monetary aggregates remain an important indicator of future inflation. The

exchange rate and import prices are also relevant, particularly for inflation in the manufacturing sector. Maintaining a reasonable

degree of price stability while ensuring an adequate expansion of credit to assist economic growth have been the primary goals of

monetary policy in India (Rangarajan, 1998).

The concern with inflation emanates not only from the need to maintain overall macroeconomic stability, but also from the fact

that inflation hits the poor particularly hard as they do not possess effective inflation hedges. One may say that Inflation is the

single biggest enemy of the poor. Consequently, maintaining low inflation is seen as a necessary part of an effective anti-poverty

strategy. By the standards of many developing countries, India has been reasonably successful in maintaining an acceptable rate

of inflation. Since the early 1980s inflation has not exceeded 17 percent (measured by the year-one-year change in the monthly

WPI and has averaged about 8 percent. While this is only on par with other countries in the Asian region, Francisco Nadal-De

Simone (2000) estimated two time-varying parameter models of Chilean inflation Box-Jenkins models outperform the two models

for short-term out-of-sample forecasts; their superiority deteriorates in longer forecasts.

Aidan Meyler, Geoff Kenny and Terry Quinn (1998) have considered autoregressive integrated moving average (ARIMA)

forecasting. ARIMA models are theoretically justified and can be surprisingly robust with respect to alternative (multivariate)

modeling approaches. Indeed, Stockton and Glassman13 (1987, pg. 117) upon finding similar results for the United States

commented that, “it seems somewhat distressing that a simple ARIMA model of inflation should turn in such a respectable

forecast performance relative to the theoretically based specifications.”

Ling and Li14 (1997) considered fractionally integrated autoregressive moving-average time series models with conditional

heteroscedasticity, which combined the popular generalized autoregressive conditional heteroscedacity (GARCH) and the

fractional (ARMA) models. Drost and Klaassen (1997) said that it is well-known that financial data sets exhibit conditional

heteroscedasticity. GARCH-type models are often used to model this phenomenon. They constructed adaptive and hence efficient

estimators in a general GARCH in mean-type context including integrated GARCH models.

In a short summary of the above studies, we can come to the conclusion that the ARIMA models in use are much more advanced

and produce accurate forecasts than any model-based analysis and forecasting. Studies here have focussed mainly on the overall

inflation and the results have been robust and surprisingly more in tune with actual values.

The other approach to forecasting (model-based) which is still used by many researchers involves just too many

restrictions/assumptions to be applied in real even though they have gone a long way in predicting future values of the variable in

question. Attempts are being made to develop more the forecasting techniques such as GARCH and Neutral Networking to come

up with fine, accurate and reliable results.

STATEMENT OF PROBLEM

Alternative Model (ARIMA) for India’s Inflation Forecast

The studies so far on inflation/price index forecasting have based their predictions either on a structural model involving multi-

variable equation or a pure econometric technique (ARIMA) involving mainly the past values of the variable in question. The

model based predictions require some assumptions which are too idealistic to be prevailing in the real world scenario. Because of

the restrictions we impose on models, we deviate from the path of reality and tread the path of simplicity in order to come up with

results satisfying the economic theory, but real life conditions are not that uncomplicated and economics as a discipline continues

to change and adapt to new changes.

So, a model based on pure econometric technique would be best suited for forecasting price index which can then be used for

policy decision making. Not much work has been done on forecasting based on pure econometric modeling using nothing but the

trend of the variable in question. Also, for price index, the models have focused on estimating inflation in general and nothing

much has been done on Food Price Index in great detail in India and this is what will be taken up in this project report.



OBJECTIVES OF STUDY

The purpose of the study is to do away with the model-based predictions and focus on time series forecasting based on trend that

the variable follows. The econometric technique, called Box Jenkins Approach to Forecasting, does not require any axioms to

base its predictions upon. It has been accorded the best approach available for forecasting purposes. A lot many sectors have been

employing this technique for various purposes. In this report, we see how this method of forecasting can be applied in case of

food price index using data from January 1991 onwards till January 2012 and how accurate the final results match with the

original data.

Keeping in view the above studies and literature, an attempt has been made to outline the practical steps which need to be

undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Food Price Index for

India. A framework for ARIMA forecasting has been drawn up.

On the basis of in-sample and out-of-sample forecast, it has concluded that the model has sufficient predictive powers and the

findings are well in line with those of other studies. This study follows simple ARIMA methodology and exclusively focuses on

India and further, the main focus is to forecast the monthly food price index on short-term basis, for this purpose, different

ARIMA models have used and the candid model has proposed. On the basis of various diagnostic and selection & evaluation

criteria the best model is selected for the short term forecasting of Food Price Index.

A food price-forecasting model is needed to provide information for use by agricultural policy decision makers to evaluate the

effects of changes in farm products due to farm programs, economic conditions, and weather on food prices. The objective of this

report is to develop a price-forecasting model that can be easily implemented for timely outlook and situation analyses.

INDIA’S FOOD PRICE INDEX: KEY FACTS

Commodity Food Price Index

“Everyone eats. As a result, everyone is affected to some degree by food price changes.” The Commodity Food Price Index is a

measure of the monthly change in international prices of a basket of food commodities. It consists of the average of five

commodity group price indices (representing 55 quotations), weighted with the average export shares of each of the groups for

2002-2004. Commodity Food Price Index includes Cereal, Vegetable Oils, Meat, Seafood, Sugar, Bananas, and Oranges Price

Indices.

Economic Rationale behind Changes in Household Behavior Due To Changing Food Price Index

For households with low disposable income levels where food expenditures are a large share of the budget, rising food prices

result in greater responsiveness and may force more difficult budgetary tradeoffs than in higher-income households with smaller

food budget shares. The opposite effect is true during periods of falling prices. However, each household’s price and income

effects also are influenced by its particular set of noneconomic characteristics.

Recent Trends in Commodity Food Price Index

By mid-2008, international food prices had skyrocketed to their highest level in 30 years. This, coupled with the global economic

downturn, pushed millions more people into poverty and hunger. In December 2010, the food price index rose above its 2008

peak.

The index dropped to an 11-month low in October 2011, but food prices still remain generally higher than last year and very

volatile. Efforts need to be scaled up at all levels to strengthen the resilience of small farmers to future shocks and to improve

food and nutrition security over the long term.

The major factors for concern which still remain despite improvement in overall supply are:

Global stocks still remain alarmingly low. The low stock environment has created a situation in which even small

shortfalls in yields can have amplified effects on prices.

The prices of specific commodities, such as petroleum products, rice and sugar, will also have an important bearing on

overall food price movements in the coming months.

While global prices have remained at high levels, domestic food prices continue to fluctuate widely.

Because of the fluctuating nature of this price index, attempts are being made to forecast the price index accurately and

help the policy makers in decision making and evaluation of economy’s progress. Time Series provides us with one

such technique which has found great usage in empirical work across countries. Time Series econometric technique

called Box Jenkins Approach to forecasting is by far the best method which has been in use for forecasting purposes. It

consists of four steps: Identification, Estimation, Diagnosis and Forecasting. Different tests are involved in each phase



which qualify the given time series for further analysis. The details of each step along with the relevant tests are given

in the end under Section 10.

The fact that it involves analysis of time series using mainly the past values of the variable in question and can also

includes other factors in the model if the situation demands; it is preferable to use this method.

DATA DESCRIPTION

The data which has been studied is the Commodity Food Price Index which includes Cereal, Vegetable Oils, Meat, Seafood,

Sugar, Bananas, and Oranges Price Indices .The period taken into consideration is January 1991 to January 2012.The source of

the data is IMF with the year 2005 set to base category. The following is a plot of data for each month from January 1991

onwards. We can see the wide fluctuations in Food Price Index in the year 2007 owing to the financial downturn that was

witnessed by the world economy which had an impact on all the sectors.

Graph-1

Source: IMF.

The next plot depicts the yearly mean price index from the year 1991 onwards. It can be seen that the mean food price Index has

been rising from the year 2000 onwards. The number of observations on the horizontal axis is in terms of number of months from

1991 onwards. The fluctuating mean yearly price index from 204th observation (2007) is reflective of the global financial crisis

which saw fluctuating prices in all sectors.

Graph-2

Source: IMF.


The models in use since the inception of forecasting have shown a great degree of difference in methodology. Some have used

purely theoretical model having a large number of variables affecting the main variable in question and others have dependent

purely on the time trend the variable in question follows keeping the factors affecting the main variable as control variables.



The latter approach has been accorded the best forecasting technique and the results have shown tremendous improvement

outperforming the results of former approach (axioms/restrictions on the factors making the former approach unrealistic). In India,

Forecast of price indices have followed an old approach and hence in this report, an attempt has been made to carry out the

forecasting using the time trend only keeping other variables as control variables.

In this paper, the Box-Jenkins Methodology for univariate time-series has been used. To obtain the results STATA10 was used.

The final results are given in the next section. In this section we restrict ourselves to the procedure that was followed.

Many forecasters are persuaded by the benefits of parsimony, or using as few parameters as possible. More the parameters, higher

will be the chances of going wrong. Larger models tend to fit the data very well but perform very poorly when used for out-of-

sample forecasts. Instead, smaller values of p and q in the case of univariate ARMA processes give better forecasts which are

more robust.

An ARMA (p, q) process is given by the following equation:

There are three primary phases in building a Box-Jenkins Time Series Model (Source: Hamilton):

Phase I-Identification

Data Preparation

Transform data to stabilize variance.

Difference data to obtain stationary series.

Model Selection

Use ACF and PACF to identify appropriate models.

Phase II-Estimation and Testing

Estimation

Derive MLE parameter estimates for each model.

Use model Selection Criteria to choose the best model.

Diagnostics

Check ACF/PACF of residuals.

Do portmanteau and other test of residuals.

Are residuals white noise?

Phase III-Forecasting

Use models to forecast.

Phase I-Box-Jenkins Model Identification

The identification stage is the most important stage and helps us to determine the adequate model from ARIMA family models.

The phase is found by the study of autocorrelation and partial autocorrelation. The first step requires us to check for stationarity of

the given time series because Box Jenkins Methodology is applicable only on stationary time series. In case the series is non-

stationary, we can make it stationary by differencing the time series.

Checking for Stationarity

The first step in developing a Box-Jenkins model is to determine if the series is stationary and if there is any significant

seasonality that needs to be modeled. The Box-Jenkins model assumes that the time series is stationary. A stationary series has: 1)

Constant Mean, 2) Constant Variance, and 3) Constant Autocorrelation Structure.

For this, we use the autocorrelation function (ACF), Augmented Dickey-Fuller test (ADF) at different lags and Phillips -

Perron Unit Root Test.

Due to the autocorrelation (ACF) starts high and declines slowly, leads to series as nonstationary, and should be differenced.



Graph-3: ACF of the Price Index Time Series

We have analyzed the data series stationarity by using Augmented Dickey-Fuller (ADF) test at lags 0, 1 and 2 which reveals the

fact that the zero hypothesis is accepted, the series has a root unit and it is non-stationary. Also, the Phillips-Perron test reveals

that the zero hypothesis (which states that the series has a unit root) is accepted. The results are tabulated below:

Table-1: Check for Stationarity of Price Index Series

Source: Hamilton.

The test statistic at the chosen lags is coming out to be less than the critical values given by the MacKinnon Table at 1%, 5% and

10% significance level. Therefore, we accept the null hypothesis that there exists a unit root and hence, the series is non

stationary. It becomes stationary by first order differences. Doing so produces an ARIMA model, with "I" standing for

“Integrated”. Since its first difference - Yt = Yt – Yt-1 = ut, is stationary, it is integrated of order 1. The time series graph of the

first order differenced Price Index is given below:

Graph-4: Time Series Plot of Differenced Price Index



The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of the first differenced time series is given

below and it can be seen from the graphs that there no longer exists non-stationarity as there is no exponentially rising or falling

trend with increase in lag length.

Graph-5: ACF of Differenced Price Index

Graph-6: PACF of Differenced Price Index

By applying the ADF test and the Philips-Perron Test for the series of the first order differences one can observe that the series

becomes stationary, so the initial series of the monthly Commodity Food Price Index is integrated by first order. The results are

tabulated below:

Table-2

As can be seen from the table, the absolute value of the test statistic at different lags is more than the critical values given by the

table at 1%, 5% and 10% significance level and hence we reject the null hypothesis of non-stationarity.



Checking for Seasonality

Also, at the model identification stage, our goal is to detect seasonality, if it exists, and to identify the order for the seasonal

autoregressive and seasonal moving average terms. Seasonality of order s is revealed by "spikes” at s, 2s, 3s, lags of the partial

autocorrelation function. However, we observe no such pattern in PACF of the original Price Index series. Hence, our data depicts

no seasonal pattern.

Graph-7: PACF of Original Price Index Time Series

Once stationarity and seasonality have been addressed, the next step is to identify the order (the p and q) of the autoregressive and

moving average terms. The primary tools for doing this are the autocorrelation plot and the partial autocorrelation plot. We apply

the Box-Jenkins procedure on the stationary data series as we want to identify the corresponding ARIMA (p, q) process. The

series correlogram has allowed us to choose appropriate p and q for the data series. Since the series ACF shows alternative

positive and negative spikes decaying to zero the process is an ARIMA (p,1,q) process. Also, the PACF of the first differenced

price index series indicates spikes at 1 and 5 and ACF of differenced price index shows spikes at 1, 2 and 7 so the order can be

hypothesized accordingly.

Phase 2 - Model Estimation

The main approaches to fitting Box-Jenkins model is the maximum likelihood estimation on the stationary series. I tried

maximum likelihood estimation on 35 models (Shown in Appendix) for different combinations of p and q where p and q

ranged from 1 to 6.

Out of 35 models, 7 models showed significant probability for both AR and MA order at all lags. I computed both AIC

and SBC for the 7 prospective models for the Price Index Time Series and hence selected the model with the minimum

AIC/SBC i.e. ARIMA (2, 1, 1).

The AIC/SBC criteria of models with significant lags are tabulated below:

Table-3: AIC/SBC of Models with Significance at all Lags

*Probability value is indicated only for one lag (the particular p or q) but in actual the significance is seen at all lags of the

converted stationary series. Although 7 models showed significant probability lag values, model with the minimum AIC/SBC

criteria is selected. The time series is proposed to follow ARIMA (2, 1, 1), the model will be validated if the residuals follows

White Noise process.



Phase 3 - Model Validation & Forecasting

If the Box-Jenkins Model is a good model for the data, the residuals should satisfy certain assumptions. The error term is assumed

to follow the assumptions of a stationary unvaried process. The residuals should be white noise (or independent when their

distributions are normal) drawn from a fixed distribution with a constant mean and variance. If these assumptions are not satisfied,

we need to fit a more appropriate model. That is, we go back to the model identification step and try to develop a better model. To

analyze the residuals are white noise or not, we follow the Portmanteau Q Test for the White Noise. The results are as follows:

Table-4: Portmanteau Test for White Noise

It can be seen that the residuals accept the null hypothesis (which stated that there is no serial correlation between the residuals) as

the p value exceeds the critical value of 0.05. The residual is white noise. Any term is not outside the confidence intervals

(illustrated by ACFs and PACFs of the residual term).

Graph-8: ACF of the residual of the Price Index Series

RESULTS AND INTERPRETATION

After estimating ARIMA (2, 1,1) Model, the following results were obtained:

Table-5



In the above model, all the coefficients of this model are significant since the values of the test statistic is coming out to be larger

than the critical values and, therefore, we reject the null hypothesis that coefficients are equal to zero. The series was forecasted

twelve periods ahead and values are given in the table below:

Table-6

The plot of the graph is given below and it can be seen from the graph that the forecasted food price index almost overlaps with

the actual food price index but shows a bit deviation from the actual towards the end.

Graph-9: Forecasting Error-Difference between Original Price Index and Forecasted Price Index

The higher than expected food price deceleration in October 2011 was caused largely by the seasonal decline in vegetable prices.

Consistent with the Reserve Bank’s earlier projections, inflation is likely to decelerate further to 7 per cent by March 2012. Food

inflation has moderated more than anticipated because of a sharp drop in vegetable prices. Going by past trends, the extent of

decline in vegetable prices seen in December this year is usually observed in the winter season (December-February).

As such, the decline in food inflation is likely to be limited in coming months. Beyond this, inflation in respect of protein-based

items remains high. In the absence of appropriate supply responses of those commodities where there are structural imbalances,

particularly protein-based items, risks to food inflation will continue to be on the upside. Significantly, there has been reduction in

rabi acreage for pulses, which may have an adverse impact on prices.

A significant downgrade in the growth projection would normally have been accompanied by a downward revision in the inflation

projection. However, in the current circumstances, two factors have prevented this from happening. First, rupee depreciation has



been feeding into core inflation, delaying the adjustment of inflation to slower growth. Second, very importantly, suppressed

inflation in petroleum product and coal prices remains quite significant.

CONCLUSIONS

From this entire exercise we have observed that the forecasted values, however are not very close to the observed value, but tend

to follow the trend of the observed price index. As it can be seen in the table given above, the forecasted price index values are

more or less rising (or falling) as the original price index is rising (or falling). For the values very far away from the mean, there is

a greater difference between the forecasted values and the observed values.

The volatility seems to have disappeared in the forecast which was present in the original series. This could be due to the fact that

there is a problem of hetero scedasticity that we have not taken into account and might require to use ARCH/GARCH models to

take care of this problem. This problem can be taken up in the future work.

We can see from the above analysis of forecasting that the model used (ARIMA) is by far the best model used for the purpose of

forecasting requiring mainly the past values of the variable whose forecast is to be made. The forecast of Food Price Index goes in

tune with the actual Index values for the period Jan 2011 to Jan 2012.

The slight divergence in the end months is because of the decline in prices of seasonal vegetables and also because of the

depreciation of rupee which could have actually caused an increase in price index but because of the slow adjustment process, it

could not feed into the price index.

Hence, we see an unexpected drop in the graph. Still the fact remains that our forecast did not go in the opposite direction of the

actual trend. It is still following the trend of actual price index. Even though there is no economic rationale behind the formation

of time series econometric model based only on past values of the variable, we do get reliable and accurate forecast and can be

compared with other models and still have higher predictive powers.

REFERENCES

1. Hamilton, J. D., (1994). Time Series Analysis, Princeton University Press, New Jersey.

2. Worldbank.org-“International Poverty-Food Price Watch”.

3. IMF.

4. Congress Research Service.

5. FAO. “World Food Situation (Food Prices Index)”.

6. Agricultural Market Information System.

7. “Trends in World Food Prices and Linkages with Food Crisis”-UNICEF.

8. “Box Jenkins Analysis”-Alexander dobre et al.

9. Comparison of Inflation Forecasts; Authors: Fama Eugene F, Gibbons Michael R, Volume: 13, 1984, Pg 327-348.

10. Forecasting Irish inflation using ARIMA models; Authors: Aidan Meyler, Geoff Kenny and Terry Quinn, December

1998.

11. Modeling and Forecasting Inflation in Japan (IMF working Paper 01/82); Author: Toshitaka Sekine, 2001.

12. Modeling and forecasting Inflation in India, Tim Callen and Dongkoo Chang, Volume 99-119 of IMF Working Paper,

1999.

13. The Forecast Performance of Alternative Models of Inflation, Authors: Stockton and Glossman, 1987.

14. Recent Theoretical Results for Tome Series Models with GARCH Errors; Author: WK Li, Shiqing Ling, 1997.

15. Forecasting Sugarcane Production in India with ARIMA model; Author: B. N. Mandal.

*****



EXTENSIBLE BUSINESS REPORTING LANGUAGE (XBRL) – A MODERN AGE BUSINESS

LANGUAGE SYSTEM AND ITS DEVELOPMENTS SCENARIO WITHIN INDIA & ABROAD

T. Ravikumar22

ABSTRACT

The last few years have seen dramatic and significant changes in corporate and global world. The numerous corporate

governance failures across the globe have resulted in demand for complete transparency in financial reports as well as related

financial reporting and corporate governance processes. The extension of the communication is the meaningful analysis of the

data on the recipient system.

Extensible Business Reporting Language (XBRL), in recent times have been introduced and mandated by Ministry of

Corporate Affairs (MCA) because of its immense use and support to all types of business organization in filing their returns

and for other purposes. XBRL can change the way an organization’s performance information is defined, produced,

exchanged, and communicated. It’s not just a transport mechanism, it’s also a way to define, constrain, and relate information.

In the current environment XBRL seems to have evolved as the key to unlock the myriad challenges faced by the

organizations and regulators alike. But, XBRL has not yet made a major impact on financial and business reporting in India.

This article makes an attempt to study about potential benefits, business application, enhanced presentation and issues of

XBRL. Further, this article also studies about world wide development of XBRL and its implementation in India.

KEYWORDS

XBRL, Business Reporting, Regulators, Taxanomy, and India etc.

INTRODUCTION

XBRL (Extensible Business Reporting Language) is an open, freely available electronic language to report and present financial

information. It is one of the important issues in the present reporting practices of financial information through the globe. XBRL

is a sub-set of XML (Extensible Markup Language). As per IASB (International Accounting Standards Board), XBRL is a

language for the electronic communication of business and financial data which is set to revolutionize business reporting around

the world. It is a platform which is independent, expandable and is a standardized method of exchanging information. XBRL

helps to obtain more accurate and transparent data in a faster way as it provides a format which enables greater reuse of reported

information. Thus XBRL enhances the credibility of financial statements by improving the transparency of reported information.

Using XBRL, information can be disseminated in a flexible and usable format on an almost real time basis. Again cost-effective

financial reporting is possible under the XBRL system. As XBRL provides some unique opportunities to different parties, more

and more organizations (including accounting organizations) from around the world are increasingly participating in XBRL

initiatives. Now more than 250 world's leading organizations including Morgan Stanley, Microsoft, and the major accounting

institutes take XBRL and support it to develop the common language for financial reporting. Again, the US SEC has announced

that it will permit public companies to submit their mandatory filings in XBRL format. XBRL is a reporting format which can be

used to publish financial reporting digitally. It improves the consistency, accuracy and quality of financial reporting as it can be

used independently or incorporated into other computer applications which require flexible informational sharing.

As a universal standard XBRL will allow companies to integrate and improve information sharing with regulators, investors,

accounting institutions and bodies, government agencies and other external entities. It benefits all members of the business

information supply chain.

XBRL is a language for electronic communication of various business data. Each and every item is tagged with information about

various attributes. XBRL tagged data can be read by any software that includes an XBRL processor and thus can be easily

transferred between computers. Tagging data in XBRL format improves transparency without additional disclosures and again

make more information available to everyone.

XBRL is a language for the electronic communication of business and financial data which is set to change business reporting

around the world. It provides major benefits in the preparation, analysis and communication of business information.

XBRL is a technique that can be applied to the conceptualization of financial statements data, other reporting of business and

financial information and further process, distribute, report and analyze the information more effectively and professionally. It is

an application of XML which uses tags to describe any data making it more interactive and reusable.

22Assistant Professor, Department of Management Studies, Christ University, Karnataka, India, [email protected]




XBRL tags both numbers and textual information that is both Financial Statements as well as information in Notes & Schedules.

It helps to significantly reduce this effort involved. Once a set of financial statements are converted into an XBRL ‘Instance’

document, it can be compared with several other such XBRL ‘Instance’ documents What would have been done in days, weeks

and months can now be achieved in minutes using this new XBRL technology.

XBRL allows for the exchange and analysis of business reporting data by encoding information in a meaningful way. Computer

applications can use XBRL data “intelligent” - recognizing the information in an XBRL document– and then applications can

select, analyze, store and exchange XBRL data with other computers and present it in a variety of ways to users. XBRL is the tool

that the companies can use to make their financial fillings more interactive and which can help the regulators, to do more

meaningful analysis and data mining.

WORLDWIDE DEVELOPMENT OF XBRL

XBRL was introduced in 1999 by the XBRL International Inc. It was patronized by the AICPA (American Institute of Certified

Public Accountants). It has active chapters in many countries including India. More than 250 of the world's leading organizations

have accepted it. Major applications of XBRL are developing in Australia, Japan, Germany and the UK. Besides AICPA, several

professional accountancy bodies from around the world like, Canadian Institute of Chartered Accountants (CICA), Institute of

Chartered Accountants of New Zealand, Institute of Chartered Accountants in Australia, Institute of Chartered Accountants in

England & Wales (ICAEW), Institute of Chartered Accountants in Ireland, Institute of Management Accountants (IMA), Institute

of Certified Public Accountants in Singapore, International Accounting Standards Board (IASB), and International Federation of

Accountants (IFAC) are members of the XBRL International.

Taxonomy (a classification system for business and financial reporting data elements, again an extensible dictionary or

vocabulary of financial and business terms) for financial reporting has also been published by IASB for XBRL. This taxonomy is

an XML-based specification for the 'Commercial and Industrial' sector. It permits users and suppliers of financial information to

exchange financial statements across all software, technologies (including the web), and the geographies. Regulatory bodies in the

US, Australia, Germany, Japan, China, Denmark, Korea, the UK and the Netherlands are implementing XBRL.

Due to XBRL implementation, foreign capital investment has increased significantly in the Korean Stock Exchange. Argentina,

Belgium, France, Spain, Hong Kong, Italy and Ireland are among other countries, which are also using XBRL or have recently

announced projects to introduce it. The major markets across Asia, Americas and EU have started to adopt new reporting

standards including XBRL.

The software vendors are also delivering XBRL enabled application to the market around the world. Followings are some of the

worldwide developments of XBRL:

Reuters released and published its 2001 full year end financial results on the internet using XBRL. It was the first publicly

listed company in the world to take this initiative.

Microsoft has become the first high tech company to publish its financial statements on the internet in XBRL format.

Bank of America and Deutsche Bank announced to use XBRL in their different banking operations.

A draft of the new XBRL US GAAP Taxonomies was released to the public on 5th December 2007.

In May 2008, SEC (Securities and Exchange Commission), US announced its plans to require companies to file their

financial results using XBRL computer tags. Under this proposal about 500 of the largest public companies would begin

filing their financial data in XBRL, in early 2009. SEC expected that most of the remaining companies would also comply

within the following two years.

So, XBRL is already in use internationally and as a new global open standard for formatting financial information, it is being

embraced worldwide by the business community.

PROCESS OF XBRL

XBRL is a XML based digital language. It is a computer programming add-on that tags on each segment of computerized

business information with an identification code or marker. XBRL is similar to a bar code for financial statements. An

electronically readable tag (bar code) is put on each financial statement element, which provides additional context. The XBRL

technology would allow electronic tagging of each individual item of data (e.g. revenue, depreciation, fixed assets etc.), instead of

treating financial information as a block of text; so that computers can work on the information using a set of rules.

The electronic tags are standardized and contain taxonomies which are essentially the dictionaries used by XBRL. Taxonomies,

which have been developed and are maintained on the XBRL web site, define the specific tags for individual items of data. The

tags are applied using a tagging tool that retrieves the tags from the standard taxonomies and applies them to whatever format the

financial statements are created, such as Microsoft Word or Excel. In most cases, accounting software will insert the tags

automatically. Again tagged data can be automatically retrieved and analyzed.



Tagged data can also be easily transferred between computers. In traditional financial reporting system like spreadsheet

representation, the significance of the value depends on its position. But in XBRL, the tagged data item say "Payroll" would be

attached to the value. The XBRL tagged data item indicates that it is independent of its location in a spreadsheet, database, or any

other application. This is so because XBRL enabled software can locate and assemble the required business data (textual,

quantitative or monetary) into a spreadsheet by executing a simple command.

Under XBRL system, business information is described irrespective of the operating systems and computing systems involved in

the information supply chain and in XBRL environment, information can be exchanged with greater speed, higher efficiency, and

enhanced reliability. XBRL can gather and analyze business information as per the needs of the users. This is possible in XBRL

with the help of two instruments: (1) an instance document (an electronic version of the XBRL tagged business report) and (2)

XBRL taxonomy (an extensible dictionary or vocabulary of financial and business terms, as mentioned earlier).

XBRL- BENEFITS AND PROBLEMS

XBRL can be used as a cost effective reporting system. It can save time and money and facilitate information analysis. On the

other hand, XBRL itself does not guarantee quality financial reporting as its reporting quality depends on quality information. If

information is not generated at all or it is inadequate or misleading then XBRL will not correct that problem. So benefits and

problems both are associated with XBRL.

BENEFITS OF XBRL

XBRL system has a great opportunity and potential to increase the quality and usability of financial reports and financial reporting

information. All users in the financial information supply chain (e.g. companies, the accounting bodies, regulators, analysts,

capital markets, the government etc.) can be benefitted from XBRL. Followings are the important benefits:

I. It is a cost effective system. Cost effectiveness can be achieved by improving the quality financial reports and

financial information and by reduction in cost of processing and disseminating business information. Again under

XBRL system, once financial data and information are created and formatted for the first time, it never has to be

keyed in a second time or reformatted for even special presentations. Some studies found that the application of

XBRL can lead to a reduction of more than 50% of the costs of traditional reporting practices. Again, the Gartner

and Forrester analyst reports suggested that the cost of producing reports can be reduced by as much as 70%.

II. XBRL significantly increases the credibility of financial statements by improving the transparency of reported

information. It is the ideal tool to facilitate faster, more transparent and more accessible reporting.

III. XBRL provides more complete, accurate and timely information in the hands of management. Thus it facilitates

better decision making.

IV. XBRL is a reporting format which publishes digital financial reports as XBRL is based on internet technologies. So

searching of specific data and information related to a particular type of analysis is also possible.

V. Under XBRL system, the speed of handling of financial information can be increased and the chance of error (by

permitting automatic checking of data) can be reduced. This system can easily handle data in different languages

and accounting standards.

VI. XBRL can help the financial service providers to collect and update information about borrowers, automate reports

to regulators and distribute or collect necessary information related to loan portfolio, sales and purchases.

VII. XBRL will help CAs (Chartered Accountants) and CFAs (Chartered Financial Analysts) to provide valuable

information and knowledge on a real time basis. Accountants and analysts can expand their professional

opportunities and value in the market place by using XBRL. In this way XBRL will help the clients also.

VIII. VIII.XBRL reduces inconsistencies and thus enhances investors' confidence.

IX. XBRL allows regulators to validate reported information prior to its receipt and analysis.

X. As proper assessment is possible on real time basis, risk is also reduced under XBRL system.

XI. Mergers and acquisitions: In the scoping and due diligence phases of M&A activity.

XBRL can be used as a uniform means to gather and compare information on target companies, allowing

management to make better-informed decisions and project pro forma performance of the combined enterprise.

Following the closing of the transaction and as integration takes place, management and advisers can use XBRL as

an information standard to improve access to data held in different accounting packages and elsewhere across the



new enterprise and to enable more streamlined communication with outside parties such as regulators, tax

authorities and investors. Sarbanes-Oxley and similar regulatory compliance - adhering to the increasingly complex

rules and regulations for public companies - can be facilitated by using XBRL to move internal controls closer to the

actual data they are designed to test.

Also, using one core set of reusable data that can be repurposed for multiple reporting audiences reduces the costs of

reporting. XBRL can reduce or eliminate the manual processes of entering company results into financial models.

This frees up analysts to ask more searching questions on earnings calls.

XII. Lower preparation cost.

XIII. Reduced preparation time.

XIV. Broader information availability.

XV. Adaptability to changing reporting requirements.

XVI. Enhanced analytical capabilities.

XBRL benefits in automation, cost savings, enhanced accuracy, greater efficiency and reliability. It further helps in deeper

analysis, better quality of the information and decision-making. It is a flexible language, which is intended to support all current

aspects of reporting in different countries and industries.

XBRL permits the automatic exchange and reliable extraction of financial information across all software formats and

technologies, including the Internet. It enhances efficiency by allowing tagged financial information to be transmitted in many

formats and deployed with various analytical tools. This efficiency is a potential source of reduced costs. Regulators can benefit

from simplified programming, facilitated validation, greater flexibility in getting changes made to submissions, and more timely,

accurate, and consistent data for analysis and research.

Replacing the In-house development systems with Standardized Marketing Solutions, use of XBRL-compliant Statements and

Reports assures the entity a competitive edge and eases standard communication on the business front.

According to Indranil Chakraborty, Executive Director, PricewaterhouseCoopers (PwC) “XBRL is platform-neutral - it is the

same whatever type of computer or software you are using. It allows all recipients of financial information provided in the XBRL

format to analyse and use precisely categorised information instantly, with no need to re-key or convert to other formats. XBRL

reduces the need for human intervention when moving financial and business reporting information from one system to another or

one organisation to another.”

PROBLEMS OF XBRL

It has already been mentioned that, application of XBRL may create problem at least at its implementation stage. The benefits of

XBRL primarily depend on the accurate and timely availability of information. Some potential problems are as follows:

I. Primary cost of implementation is relatively high.

II. Security may be an important factor in data exchange through the internet.

III. To understand XBRL format, proper knowledge is required. Due to the lack of knowledge, XBRL implementation

is tough in different countries of the world.

IV. As XBRL is a standard, it has a finite life span. It is affected due to technological risk. XBRL may be obsolete after

certain time as a result of innovation of new technology.

The possibilities of errors arising in a XBRL filing are rare but possible. The business rules provide some validation checks to

ensure that stark differences are not thrown up. For instance, one of the business rules is that net current assets is a mandatory

field and hence cannot be blanked. The XBRL taxonomy may not have a line item that is unique to a particular entity. Tagging

this could pose an issue. In such instances, the entity has to use the best-fit option and tag it to an item in the taxonomy that

closely resembles the nature of the item. The Ministry of Corporate Affairs, India, has made it clear that there would be

extensions of the present taxonomy for the present.

Most XBRL-software products have reverse engineering tools wherein XBRL-filed documents are translated into commonly-used

and familiar documents. Companies could probably consider using XBRL for their internal reporting initially in order that all the

possible snags are ironed out prior to the regulatory filing. On its part, The Ministry of Corporate Affairs, India can probably

consider a dry run of XBRL filing prior to the due date to test for digressions. In case there are too many snags, it could permit

parallel filing for the current year and move over to XBRL from the next year — a commonly used practice when moving over to

enterprise accounting systems from accounting packages.



XBRL IN INDIAN CONTEXT

Different initiatives have been taken in the Indian context to apply XBRL. EDIFAR (Electronic Data Information Filing and

Retrieval) system was introduced by SEBI (Securities and Exchange Board of India) in 2002. This is an automated system for

filing, retrieval and dissemination of time sensitive corporate information.

EDIFAR is modeled on the lines of EDGAR (Electronic Data Gathering, Analysis and Retrieval) system of the US, and SEDAR

(Systems for Electronic Document Analysis and Retrieval) in Canada, and is being hosted on the SEBI web site. It is a repository

of business information for listed Indian companies. SEBI in association with NIC (National Informatics Center) has set up the

EDIFAR website to facilitate filing of certain documents/statements by the listed companies online on the web site. This would

involve electronic filing of information in a standard format by the companies. This system is useful for various classes of market

participants including companies, investors, regulatory organizations, research institutions and the stock exchanges.

The primary objective of EDIFAR is to centralize the information and accelerate its dissemination. It enhances the transparency

and efficiency of financial reporting for the benefit of all the stakeholders in the securities market.

EDIFAR is being implemented in a phased manner. All the listed companies are required to file disclosure statements and other

information (filing of the information in electronic format was made compulsory by the market regulator by amending the listing

norms of the exchange by inserting a clause to the Listing Agreement) with the stock exchanges where they are listed. The stock

exchanges disseminate this information through trading terminals, their website etc.

In the year 2005, Satyam Computer Services Limited, a Hyderabad-based IT services providing Indian company announced that it

had become India's first direct participant member of the XBRL International. In January, 2007 the Institute of Chartered

Accountants of India (ICAI) announced that, it would soon constitute a committee consisting of regulators to develop taxonomy

for the promotion of extensible business reporting. Again in 2007, ICAI constituted the XBRL Group for undertaking the

development and promotion of XBRL in India. In July, 2008 ICAI said it will come out with the first part of a user-friendly online

language, XBRL by September 2008.

A committee had also been setup by the ICAI for looking at the various aspects of XBRL and which was working on its

codification. The proposed upgrading of MCA21 (a comprehensive e-governance program that has already made the interaction

between companies and regulators much more efficient) would make India the twelfth country in the world to put corporate

financial information in the user-friendly XBRL (as per the news published in The Economic Times on 29 Aug, 2008). It will

allow hassle-free data mining and effective utilization of data for research.

According to Ved Jain, then President, ICAI, after placing of the language (i.e. XBRL) properly one can easily understand and

harmonize the financial statements. The development of general purpose XBRL taxonomy is applicable for commercial and

industrial companies. This taxonomy is based on the requirements of the Indian accounting standards and various Indian corporate

laws. Different institutions (like SEBI, the Ministry of Corporate Affairs, Reserve Bank of India and the Insurance Regulatory and

Development Authority) were also working with ICAI to develop that general purpose XBRL taxonomy and these institutions

were supporting ICAI in its XBRL development endeavors.

As per the future plan of ICAI, after the development of general purpose taxonomy, the institute would take up the development

of taxonomy for the financial sector, specifically banks and non-banking financial companies.

Recently, the Group has finalized the draft general purpose financial reporting XBRL taxonomy for commercial and industrial

companies. The draft taxonomy covers the financial statements, viz., Balance Sheet, Statement of Profit and Loss, and Cash Flow

Statement and the related nonfinancial information. The draft taxonomy has been developed conforming to Indian Accounting

Standards and Company Law while adapting the architectural features of the IFRS general purpose taxonomy 2006. ICAI was

also aiming to establish the Indian jurisdiction of XBRL international. It would encourage the development and adoption of

XBRL in India and represent Indian interests at the international level.

As a result, ICAI promoted eXtensible Business Reporting Language (XBRL) India as a Section-25 company to takeover this

responsibility from it. According to section 25(1)(a) and (b) of the Indian Companies Act, 1956, a section-25 company can be

established ‘for promoting commerce, art, science, religion, charity or any other useful object’, provided the profits, if any, or

other income is applied for promoting only the objects of the company and no dividend is paid to its members.

India's central bank, The Reserve Bank of India also took initiatives to develop XBRL. The Reserve Bank of India has

implemented Online Returns Filing System (ORFS) for submission of certain important returns by commercial banks to the

Reserve Bank. As part of the online submission of returns, the Reserve Bank will, in due course, adopt the international standards

for data sharing among business entities through eXtensible Business Reporting Language (XBRL). The Reserve Bank is moving

towards the XBRL standards under a high level Steering Committee chaired by the Deputy Governor, Shri V. Leeladhar.

The Securities and Exchange Board of India (SEBI) is also looking forward to use the extensible business reporting language

(XBRL) technology in the near future where listed companies filing their financial statements with the board will be required to

do so in XBRL compatible format. The XBRL is a way of electronic communication of business and financial data and is of

immense utility to the capital markets and the investing community.



The Ministry of Corporate Affairs (MCA) has made mandatory for most of the companies to file an XBRL compliant financial

statement beginning with FY 2010-11. The criteria for the companies which need to do such filling are as follows:

All companies listed in India and their subsidiaries, including overseas subsidiaries.

All companies having a paid up capital of Rs 5 crores or above, or a turnover of Rs. 100 crores or above.

The balance sheet and profit and loss account (financial statements) required to be filed in the XBRL format would be based on

the existing Schedule VI of the Indian Companies Act, and the currently prevailing Indian Accounting Standards. A filing

deadline of November 30, 2011 has been notified. Failure to adhere to the timeline may result in imposition of additional filing

fees.

THE FUTURE APPLICATION OF XBRL

XBRL provides an international platform for a global economy. Thus it has a great potential in future application. By applying

XBRL, companies can be accessed electronically for analysis, benchmarking, reporting and financial modeling.

XBRL can also be applied for cost accounting, performance measurement, financial statement analysis and decision making

purposes besides financial reporting. As the world becomes more fast and complex, all the information users in the financial

reporting supply chain are demanding more extensive financial and nonfinancial information for more clarity.

Due to cross border transactions and cross border capital flows, a sound, transparent financial reporting system (which is

internationally accepted) is needed to ensure global confidence in the capital markets. Here lies the future application of XBRL.

The use of XBRL will enable the financial community to analyze financial facts and figures as well as risk in a better way. So

from the capital market perspective also, XBRL has immense potentiality. Other potential XBRL applications include 'XBRL for

Taxes', 'XBRL for Regulatory Filings', 'XBRL for Accounting and Business Reports' etc. So the use of XBRL will become an

operational and financial reporting necessity and not a luxury.

CONCLUSIONS

XBRL is likely to emerge as the global standard for reporting of financial and business information. Major stock exchanges are

likely to make it mandatory for companies to file disclosures and financial reports in XBRL format, in order to enable the easy

dissemination of such information to financial analysts and the investor community at large.

For software companies, XBRL presents a huge and lucrative new market. They can capitalize on the huge demand for software

to support the preparation, creation and analysis of XBRL data. XBRL will have a pervasive presence in all aspects of business. It

is important to build an awareness of the technology, as one is almost certain to encounter it in one’s professional life.

REFERENCES

1. Article on XBRL in the Economic Times on 29th Aug, 2008.

2. Mini, Joseph Tejaswi, (2011). “Corporates Move To New Data Reporting Language”, Times of India on 11th May.

3. Mohan, R. Lavi, (2011). “Coping With a New Language”, Business Line on 7th July.

4. http://www.icwai.org

5. http://www.rbi.org

6. http://www.icai.org

7. http://www.xbrl.org/in/

8. http://www.xbrl.org

9. http://www.sap.com/solutions/.../xbrl.../index.epx

*****

http://www.icwai.org/

http://www.rbi.org/

http://www.icai.org/

http://www.xbrl.org/in/

http://www.xbrl.org/

http://www.sap.com/solutions/.../xbrl.../index.epx



A STUDY ON WOMEN EMPOWERMENT THROUGH SELF HELP GROUPS –

WITH SPECIAL REFERENCE TO BIJAPUR DISTRICT, KARNATAKA

Pradeep K. Gupta23 Sanjay S. Hanagandi24

ABSTRACT

Background: Microfinance is a powerful instrument for poverty eradication in developing economies like India. Micro

finance initiatives like the SHG-Bank linkage programme, MFI – Bank Linkage model etc., in India has been increasingly

promoted for their positive impact on women empowerment.

Objectives of Study: The objectives of paper are:

1) To analyze and review the existing literature on the area of Microfinance and the Self Help Groups (SHG) in India.

2) To analyze the impact of SHG on women empowerment in Bijapur district of Karnataka.

Research Methodology: As a part of the secondary data collection various research papers, articles, reviews, conference

proceedings and websites were referred. Also mini Delphi study was done. As a part of the primary data collection, a sample

(random) of 100 women respondents were selected out of 20 SHG’s operating in the district of Bijapur. The data was

collected during the period July 2011 to December 2011.

Research Setting: Bijapur is a district head quarter located in the state of Karnataka in southern India. The district comprises

of 5 blocks (talukas) with population of around 18.07 lakh according to the census 2001. Farming and agriculture related

business is the main occupation for many people of the district.

Findings: The analyzed findings prove that the intervention of micro finance through SHG-Bank Linkage Programme has

positive impact on women empowerment in Bijapur district Karnataka.

Literary Value: The existing literature of SHG’s contribution towards women empowerment is miniature in the location in

study. The outcomes of this study will form a foundation for further study in the area.

KEYWORDS

Self Help Group (SHG), Poverty, and Women Empowerment etc.

INTRODUCTION

According to Reserve Bank of India, “Microfinance is an economic developmental tool whose objective is to assist the poor to

work their way out of poverty. It covers a range of services which include, in addition to the provision of credit, many other

services such as savings, insurance, money transfers, counseling, etc.” Microfinance is emerging as a powerful instrument for

poverty eradication in developing countries. Hence, most of the developing countries have introduced this programme to provide

various financial services to low income clients. In practice, the term Microfinance is often used to refer to small loans given to

the poor people. More broadly, microfinance refers to a variety of financial services offered to the low income people. These

services include savings, microcredit, micro insurance, micro pension and others.

It proved that the poor are bankable (Patel, 2002; * Dr. K. Kanniammal, Dr.U.Jerinabi, A. Arthi, 2011) and poverty alleviation

was possible without subsidies (Khandker, 1998). Several studies have been conducted in India, on the various aspects of micro

finance which shows that Microfinance in India is dominated by Self Help Group (SHGs)-Bank linkage programme as a cost

effective tool for providing financial services to the low income clients which has given a great opportunity to the low income

clients in India to achieve reasonable economic, social and cultural empowerment.

According to NABARD, Self Help Group (SHG) are defined as “small, economically homogeneous affinity groups of rural poor,

voluntarily formed to save and mutually contribute to a common fund to be lent to its members as per the group member’s

decision”. SHG’s is a small voluntary group of poor people, preferably from the same socioeconomic background formed with a

common objective of saving and providing financial support to the group members. Basically a SHG is a informal group of 10 to

20 members. These groups will avail financial support from NGO’s, commercial banks, regional rural banks and other financial

institutions. The Self Help Group (SHG)-Bank Linkage Programme, in the past two decades, has become a well known tool for

bankers, developmental agencies and even for corporate houses. This programme was launched in India in early 1992 by National

Bank for Agriculture and Rural Development (NABARD) with a small Pilot project by linking 255 SHGs with banks.

The programme has reached to linking of 69.5 lakh saving-linked SHGs of which exclusive women SHG’s are 53.1 lakh and 48.5

lakh credit-linked SHGs of which exclusive women SHG’s are 38.98 lakh and thus benefiting about 9.7 crore households (Status

23Lecturer, BLDEA’s A. S. Patil College of Commerce, Karnataka, India, [email protected] 24Sr. Lecturer, BLDEA’s A. S. Patil College of Commerce, Karnataka, India, [email protected]





of microfinance in India 2009-10, NABARD). Various studies show a positive correlation between Self Help Groups (SHGs)-

Bank linkage programme and women empowerment.


Hunt, J & Kasynathan, N (2002) says that microfinance has a positive Impact on women’s mobility and helps in reducing the

domestic violence. They observed that women need only a small opportunity to build their own pathway to empowerment. Access

to credit and peer support has enabled them to increase their power and decision making capacities in their households.

Ranjula Bali Swain and Fan Yang Wallentin (2007) argue that, women empowerment takes place when women challenge the

existing social norms and culture, to effectively improve their well being. The study empirically validates this hypothesis by using

quasi-experimental household sample data collected for five states in India for 2000 and 2003 and the results showed that, there is

a significant increase in the women empowerment of the SHG members group.

Dr. G. sudarsana Reddy (2010) says that the SHG-bank linkage programme plays an important role in women empowerment. The

study undertaken was based on various indicators like women household decision making power, financial autonomy, freedom of

movement, political participation acceptance to unequal gender role, exposure to media, access to education and experience to

members. Dr. K. Kanniammal, Dr. U. Jerinabi, and A. Arthi (2011), says that micro finance is a path towards empowering the

most marginalized among the poor to take charge of their life’s requirements. The study results proved that the intervention of

micro finance through SHG-Bank Linkage Programme has positive impact on the economic and social status of the members, in

terms of increase in income, savings, employment generation, asset creation, decrease in the dependency on money lenders,

improvement in decision making skills, participation in community affairs and the empowerment of women. Micro finance

activities have helped poor to come out of poverty and achieve social reorganization and empowerment.

V. V. Desai (2011) in his study says that, the enhancement of entrepreneurship qualities among the members of self help groups is

a significant step towards social and economic empowerment of women. Status of women has also improved by joining the

SHGs. His suggestions for improvement are the development of skill oriented training programmes, encouragement of good

leadership in the group and constant guidance and support through the government and non-government organizations.

Dr. Elizabeth Joey Henriques and Dr. Rekha Ramesh Gaonkar (2011) the study suggests that poor often use micro credit for

productive and income generating activities when compared to non-poor micro credit clients. The usage of micro credit also

depends on the age of the SHGs. It was been observed that the SHGs with longer period of time have a tendency to utilize credit

more towards financing non-income generating activities. The findings also reveal that the members of SHG are also dependent

on other financial institutions for their credit requirement.

Women Empowerment – A Prelude

Women Empowerment is a multifold concept comprise of economic empowerment, socio-cultural empowerment and political

empowerment. It refers to examine and increase the political, social and economic strengths of women. It involves developing self

confidence and decision making capacity. The data on various dimensions has been collected shown as table1 below:

Table-1: Socio Economic Profile of Sample Members

Sl. No. Variable Intervals Frequency Percentage

1. Age Less than 20 Years

20 Years to 40 Years

Above 40 Years

03

68

29

03

68

29

2. Education Illiterate

Primary School

High School

P.U.C

Degree

Others

32

22

24

16

06

00

32

22

24

16

06

00

3. Marital Status Single

Married

Widow / Divorced

05

85

10

05

85

10

4. Type of Family Nuclear

Joint

85

15

85

15

5.

Family Size Less than 4

4 to 8

Above 8

20

76

04

20

76

04

6. Number of Income Earners

In Family

One

Two

Three

More than Three

40

43

10

07

40

43

10

07

7. Occupation Agriculture

Agricultural Labor

01

00

01

00



Non Agricultural Labor

Animal Husbandry

Employed

Entrepreneur

Housewife

Others

22

03

08

23

37

06

22

03

08

23

37

06

8. Agricultural Land Owner Yes

No

35

65

35

65

9. Possession of House Own

Rented

Lease

78

22

00

78

22

00

Sources: Field Survey (2011).

68% of the respondents (Refer Table 1) represent the middle age group, followed by 29 % and 3% falling in elderly and young

category respectively. This indicates that most of the respondents were within the economic active age group. The educational

factor reveals that 32% respondents had no formal education, 22% had basic primary education, 24% had possessed high

schooling, whereas, 16% had possessed pre university level education while the remaining 6% are university graduates.

Further it was revealed that 5% of the respondents are spinsters, 85% are married and 10% are divorced or widowed. 85% of the

respondents live in nuclear family and only 15% live in joint family. 20% of the members have up to 4 members in their family

76% have 4-8 members and only 4% have members above 8 in their family. 40% of the respondents have a single income earner

in their family, 43% of the respondents have a two income earner in their family, 10% of the respondents have a three income

earner in their family and 7 % of the respondents have more than 3 income earner in their family. Majority (37%) are housewife

catering to homely needs, 23% are entrepreneurs, 22% are non agriculture labors, 8% are employed, 6% have other occupations,

3% are into animal husbandry and the rest 1% of the respondents are doing agriculture. 65% do not possess agricultural land. 78%

of the respondents own a house.

DATA ANALYSIS & INTERPRETATION

Analysis of the Respondents (SHG’s and Women Empowerment)

Table-2: Prime motive of joining SHG

Prime Motive Frequency

To develop saving habits 86

To get access to credit facilities 77

For achieving economic self reliance 15

For Socio political empowerment and sustenance 19

Others 0

Sources: Field Survey, 2011.

The table2 inferred that 86 respondents say that they joined SHGs to develop saving habit, 77 have joined to get access to credit

facilities, 15 to achieve economic self reliance and 19 for socio political empowerment and sustenance.

Table-3: Period of Functioning of SHG

Category Frequency

Less than one year 15

One year to three year 40

More than three year 45


From the table3, it is inferred that 15 out of 100 respondents says that their SHG is functioning from less than 1 years, 40 says that

their SHG is functioning from 1to 3years and 45 says that their SHG is functioning from more than 3 years.

Table-4: Frequency of Group Meeting

Category Frequency

Weekly once 10

Fortnightly once 45

Monthly once 45


From the above table4, it is inferred that 10 0ut of 100 respondents meet once in a week, 45 out of 100 meet in every 15 days and

the rest 45 meet monthly once.



Table-5: Saving Amount per Meeting

Saving Amount Per Meeting Frequency

Less than Rs. 100 35

Rs. 100 to Rs. 300 65

More than Rs. 300. 0


From the above table it’s clear that 35 out of 100 respondents save less than Rs.100 per meeting, rest 65 respondents save Rs. 100

– Rs.300 per meeting.

Table-6: Credit Facilities Availed After Joining Self Help Group (SHG)

Responses Frequency

Yes 76

No 24


From the above table6, it is inferred that 76 0ut of 100 respondents have availed the credit facility and the rest 24 have not availed

any loan facility.

Table-7: If Yes, Amount of Loan Availed

Loan Amount Frequency

Less than Rs. 3000 5

Rs. 3000 to Rs. 10000 26

More than Rs. 10000 45


From the above table7, it is inferred that 6.57% (5 out of 76) respondents have availed the loan of less than Rs 3000, 34.2% (26

out of 76) respondents have availed the loan of Rs. 3000 to Rs. 10000 and the rest 59.21% (45 out of 76) respondents have availed

the loan of more than Rs. 10000.

Table-8: Decision Makers w.r.t. Utilizing Loan

Responses Frequency

Self 41

Others 12

Both 23


From the above table8, it is inferred that 53.94% (41out of 76) respondents take their own decision for utilizing the loan amount,

15.78%(12 out of 76) respondents agree to the decision taken by others for utilizing the loan amount, and the rest 30.26% (23out

of 76) respondents take joint decision with others.

Table-9: Purpose of Loan

Purpose Frequency

Education of Their Dependents 19

Household Consumption and Improvement 09

Acquire Assets 23

Self Employment 38

Medical Treatment 11

To Repay the Debts 08

Others 06


Table9 depicts information on purpose of loan taken by the respondents. It is clear from the table that 19% of the respondents

have taken the loan for the education of their respondents, 9% of the respondents have taken the loan for the household

consumption and improvement, 23% of the respondents have taken the loan for acquiring assets, 38% of the respondents have

taken the loan for self employment activities, 11% of the respondents have taken the loan for medical treatment, 8% of the

respondents have taken the loan to repay the debts, and the rest 6% of the respondents have taken the loan for other purposes.

Table10 below depicts information on the impact of joining Self Help Groups by the respondents. It is clear from the table that

40% of the respondents have experienced a rise in their income, 19% of the respondents were able to educate their dependents,

17% of the respondents have improved nutrition in their household, 43% of the respondents have taken up self employment

activities, 65% of the respondents have achieved social awareness, 7% of the respondents have achieved political awareness, and

the rest 5% of the respondents have received other benefits.



Table-10: Impact of Joining Self Help Group

Impact Frequency

Rise In Income 40

Education of Dependents 19

Improved Nutrition In Household 17

Self Employment 43

Social Awareness or Participation 65

Political Awareness or Participation 07

Others 05


CONCLUSIONS AND DISCUSSIONS

The study results proved that the intervention of micro finance through SHG-Bank Linkage Programme has positive impact on

women empowerment, in terms of increase in social awareness and participation, savings habits, income level, self employment,

asset creation, repayment of other debts, improvement in decision making skills and improved nutrition level at their household.

Micro finance through SHG-Bank Linkage Programme has enabled poor women to get access of various financial products and

services. The self help group concept enabled many women to achieve social recognition. Greater emphasis has to be given to

provide education, training and creating awareness among the members of the group.

LIMITATIONS

The present study is confined only to the women SHGs of Bijapur district, Karnataka.

The questionnaire has to be translated in the regional language (Kannada) as most of the respondents don’t understand

English language. Hence the data was collected by the researcher himself and the reliability of the data depends upon

the validity of the data furnished by the respondents.

REFERENCES

1. Swain, Ranjula Bali, (2006). “Can Microfinance Empower Women? Self Help Groups in India”, Department of

Economics, Uppsala University, pp 61-82.

2. Swain, Ranjula Bali, and Fan Yang Wallentin, (2007). “Does Microfinance Empower Women? Evidences from Self

Help Groups in India”, Working Paper, Department of Economics, Uppsala University.

3. Kanniammal, K.; U. Jerinabi; and A. Arthi, (2011). “Impact of Micro Finance through SHG-Bank Linkage Programme

On Women of Rural Priority Communities In Coimbatore District”, International Journal of Micro Finance, Puducherry,

Vol-1, No.1. pp. 34 – 42.

4. Patel, A.R., (2002). “Rural Credit Delivery System”, Kurukshetra 11 (2):4-8.

5. Khandker, R.S., (1998), “Fighting Poverty with Micro Credit: Experiences in Bangladesh”, Oxford University Press,

New York: World Bank, pp- 228.

6. Sathiyabama, N., and Meeenakshi Saratha, M., (2011). “Women Empowerment and Self Help Groups in

Mayiladuthurai Block, Nagapattinam District, Tamilnadu”, International Journal of Research in Commerce &

Management, Vol. 2 (2011), Issue No. 9, (September), pp. 112-117.

7. Desai, V. V., (2011). “Self Help Groups: An Integrated Approach of Empowerment for She Entrepreneurs”,

International Journal of Research in Computer Application & Management. Vol. 1 (2011), Issue No. 7 (September), pp

133-136.

8. Henriques, Elizabeth Joey, and Rekha Ramesh Gaonkar, (2011). “Micro-Credit: A Study of Micro-Credit Usage by Self

Help Group Members in Goa”, International Journal of Research in Commerce, Economics & Management, Vol: 1,

Issue No. 3 (July), pp 56-60.

9. Babajide, Abiola, and Taiwo Joseph, (2011), “Microcredit and Business Performance in Nigeria: The Case of MFI

Finance Enterprises”, International Journal of Research in Commerce & Management, Vol. 2, Issue No. 11 (November),

pp 43-49.

10. Juliet Hunt, “Reflections on Microfinance and Women’s Empowerment,” Independent Consultant Nalini Kasynathan,

Oxfam Community Aid Abroad, South Asia, pp 20-26.

11. http://www.microfinancegateway.org/p/site/m/template.rc/1.26.12263/#1

12. www.microfinance.lu/comas/media/37_swain_an.pdf

13. http://rbidocs.rbi.org.in/rdocs/publicationreport/pdfs/yhmr190111.pdf

*****

http://www.microfinancegateway.org/p/site/m/template.rc/1.26.12263/#1

http://www.microfinance.lu/comas/media/37_swain_an.pdf

http://rbidocs.rbi.org.in/rdocs/publicationreport/pdfs/yhmr190111.pdf



CORPORATE SOCIAL RESPONSIBILITY AND

SUSTAINABLE ENVIRONMENTAL DEVELOPMENT

A. Suryanarayana25 Bandaru Srinivasa Rao26

ABSTRACT

At the threshold of 21st Century, we are confronted with two conflicting scenarios for the posterity of human kind. While there

are possibilities of a bright future with many and varied advances in Science and Technology, a grim scenario is also looming

large with burgeoning population, starved of natural resources and choked by environmental pollution. Faced with such

imminent threat, there is a growing realization that rational utilization of environmental endowments of life support systems

like water, air, and soil is an imperative for Sustainable Development.

The challenges of pollution and wanton degradation of natural resources cannot be adequately faced without an informed and

intelligent understanding of their causes and consequences. Alongside, it is necessary to build up professional capabilities to

develop and adopt policies, strategies, and programs for efficacious environmental management by understanding the

relationship between ecological devastation and deteriorating human conditions.

This Paper surveys a range of topics from sustainable development and ecological imperatives to strategies for managing

some of the more important and complex environmental issues. This is an attempt to sensitize the participants to the problems

of pollution, waste management, bio-diversity, and forest management through the principles of Natural Resources and

Environmental Management for Sustainable Development.

KEYWORDS

Sustainable Development, Ozone Depletion, Waste Management, Bio-Diversity, and Forest Management etc.

INTRODUCTION

Not only in India but all over the world, there is now a growing concern for ethical norms in all spheres of human activities. It

may be pubic behavior, business, or environment. In India, the ethical debate has been intense recently due to scams and other

such practices in business, and many other dealings. As a consequence, many companies have come to realize that ethical

practices may be a good business especially for the organizations engaged in Indian exports. These organizations have to satisfy

the importer in regard to the quality, ethics, and environmental standards. When one discusses about environmental ethics, one is

not merely concerned about the business organization and their conformance to environmental standards.

It is a larger issue that concerns ethical behavior of all organizations ranging from international bodies, national government,

opinion makers, media, intelligentia, public and private enterprises, and NGOs to common person. Environmental ethics concerns

the value system of societies—the value system that has brought the state of environment to the present situation in which there is

exploitation not only of nature but also exploitation of some societies by others. This calls for a need to exploration of crucial

issue of relationship between environmental or ecological insights and our social and political system.

ECOLOGICAL CHALLENGE, ENVIRONMENT AND BUSINESS

Ecology is the study of how living things—plants and animals—interact with one another and with their environment. As a

contributor to pollution, business is involved in society’s ecological problems. These problems are complex and their solution

requires many trade-offs between economic production and a cleaner, safer environment. The ecological challenge occurs when

wastes created by human productive activities cannot be readily absorbed into the environment without causing harm.

The ecological challenge requires businesses to formulate long-run company strategies that: (1) make the most efficient use of

scarce industrial resources, (2) reduce wastes that pollute the environment, and (3) keep industrial production within the limits set

by nature’s ecological systems. Strong public interest in preserving nature’s ecological balance and a clean, healthy environment

means that business must constantly strive to achieve these goals for society. At the same time, business is expected to produce

needed goods and services at reasonable prices. The task for corporate and public policymakers is to find socially acceptable ways

to meet both ecological and economic goals.

NEED FOR NATURAL RESOURCES AND ENVIRONMENTAL MANAGEMENT

At the threshold of 21st Century, we are confronted with two conflicting scenarios for the posterity of human kind. While there are

possibilities of a bright future with many and varied advances in Science and Technology, a grim scenario is also looming large

with burgeoning population, starved of natural resources and choked by environmental pollution. Faced with such imminent

25 Professor, Department of Business Management, Osmania University, Andhra Pradesh, India,

[email protected] 26 Professor & HOD, Department of MBA, QIS College of Engineering & Technology, Andhra Pradesh, India,

[email protected]





threat, there is a growing realization that rational utilization of environmental endowments of life support systems like water, air,

and soil is an imperative for Sustainable Development.

The challenges of pollution and wanton degradation of natural resources cannot be adequately faced without an informed and

intelligent understanding of their causes and consequences. Alongside, it is necessary to build up professional capabilities to

develop and adopt policies, strategies, and programs for efficacious environmental management by understanding the relationship

between ecological devastation and deteriorating human conditions.

This Paper surveys a range of topics from sustainable development and ecological imperatives to strategies for managing some of

the more important and complex environmental issues. This is an attempt to sensitize the participants to the problems of pollution,

waste management, bio-diversity, and forest management by presenting and promoting the principles of Natural Resources and

Environmental Management.

OZONE DEPLETION: TRADE AND INDUSTRIAL INTERESTS LADEN WITH ETHICS

Ozone layer in the stratosphere forms a shield for earth against ultraviolet radiation (UV-B) from outer space. Ozone is a colorless

gas. Depletion of ozone results in the formation of holes in its shield. UV-B arising from the Sun would reach the earth if there are

ozone holes resulting in a lot of harmful effects to humans, plant life, marine life, and the entire food chain. Ozone depletion, as a

global problem, attracted the attention of one and all in 1980’s.

The discovery of ozone hole became a matter of great concern as trade and industrial interests of many countries were involved.

European and Japanese were afraid if there would be a total ban on the use of CFCs, their refrigeration industry would suffer but

Americans are now keen for the elimination of CFCs and other ozone depleting substances, as they had developed some

substitutes for CFCs. Finally, a solution was found when in 1987, twenty four countries and later by 1990 most of the nations

signed an agreement, the Montreal Protocol, committing themselves to phasing out the production and use of ozone depleting

substances mainly CFCs.

India was a late entrant into the ozone negotiations, becoming a full time participant only just before the first Meeting of Parties

(MoP-1) in Helsinki in 1989. Business interests are at great stake when negotiations to phase out a product start taking place at

international level. CFC phase out has its implications for business, though this industry is smaller in comparison to industries like

coal and oil that are related to climate change.

It is a well known fact that CFC industry is in the hands of few companies like DuPont and ICI unlike those related to carbon

emission and climate change. Yet it is usually seen that the companies involved in the businesses of products so affected would

try to see that phasing is delayed. In case of CFC, as a couple of major companies that produced CFC could come up with the

substitute, it became easier for them to agree to phase out the product, thus supporting the Montreal Protocol for control of ODS.

DuPont had developed a substitute and therefore it was of strategic interest for this company to favor the Montreal Protocol.

Businesses play a key role in the international environmental agreements and in shaping the environment in which we and our

future generations would live. But it is in the interest of neither the governments nor industry to block scientifically established

agreements to a future date. As it has happened in case of CFC phase out, the Montreal Protocol is being implemented fairly well,

though there may still be a few loopholes. Similarly, in case of climate change, the industry will accept the reality of emission

control and may not resist once some kind of market incentives become available to phase out of fossil fuels.

WASTE MANAGEMENT: ETHICAL DUTY TO MAINTAIN QUALITY OF EARTH’S ENVIRONMENT

Disposal of wastes or the management of wastes is an uphill task for any governments, their agencies, or organizations. Detection

of chemicals of toxic nature in the water supply, fruits and vegetables, and in the water tables, and their risk to health focused the

attention of public, governments, and the other organizations on the disposal of hazardous wastes and release of toxic substances

in environment. Bhopal gas tragedy in 1984 led the Government of India to review the practice of waste management of

hazardous substances. Enactment of Environment Protection Act (EPA) of 1986 was one of the outcomes of Bhopal tragedy.

Subsequent to EPA, Ministry of Environment and Forests (MEF), Government of India, promulgated Hazardous Wastes

(Management and Handling Rules) in 1989 and amended them in the year 2000. From then onwards, efforts to inventories the

hazardous waste generation was initiated.

Hazardous wastes arising from the industrial activities have been increasing year after year due to increase in industrial

production. This along with increasing municipal solid wastes posed a great challenge and called for expert managerial efforts.

Hazardous wastes are those that are toxic to plants and animals, inflammable, explosive, corrosive or highly reactive chemically.

Integrated System for Waste Management

Agenda 21 addressed the problem of waste management stating that sound management of wastes is among the major

environmental issues for maintaining the quality of Earth’s environment and achieving sustainable development. Accordingly,

waste management is to be done through following systems: (i) Minimum of production of wastes, (ii) Maximizing reuse of waste

and recycling, (iii) Promoting environmentally sound waste disposal practices, and (iv) Extending waste services.



If consumption is more and unsustainable, production will increase resulting in increased waste production. A waste minimization

policy should be formulated with suitable mechanism to quantify the waste production and to establish national goals by

formulating a database. Adopting suitable technology is one solution in this regard. The Government and UN should promote

waste minimization by facilitating exchange of data and know-how. Reuse of wastes has become very important not only in view

of the fact that wastes pose a threat to environment but also due to the fact that waste disposal has become very expansive. Time

has now come to formulate a national program for waste reuse and recycling. According to UN all the governments should have

such definite programs by 2010 by all the developing countries. To encourage the use of recyclables, a system of refund/deposit

be introduced.

Human Resource Development efforts should be intensified to train the people in various organizations to reorient current waste

management practices to include waste reuse and recycling. Even after treatment, waste still remain and have an impact on

environment. Treatment of municipal wastes like garbage and fecal material should be given priority. The sanitary landfills

scientifically serve as large sites for decomposing biodegradable materials. Though landfills have several drawbacks, they should

ideally be constructed in such a way that they are covered with plastic sheets all around or semi-permeable soil/clay/sand/gravel to

prevent water pollution from leaching. Municipal solid waste, garbage from kitchens, food processing industry and degradable

wastes from slaughterhouse can be composted in composing plants and sold as fertilizer. Incarnation, through burning, is another

method of disposal especially of garbage.

All cities should follow good practice of waste disposal as tipping of waste using mechanized equipment for leveling and

compacting and placing a daily cover of soil on top of it before compacting in future. If the wastes generated by hospitals, nursing

homes, and health centers are not properly handled and disposed, they could pose a major environmental and health problem.

Same is true of ‘Plastics,’ the wonder materials. In 1996, MEF, Government of India, set up a National Plastic Waste

Management Task Force for dealing with the various aspects of plastic use, appropriate technologies for recycling, waste

collection, and public awareness.

BIODIVERSITY: SOME ECOLOGICAL, ECONOMIC, AND SOCIAL ASPECTS

Biodiversity means the variety and variability of all living organisms. It constitutes the biological wealth at three levels: Genetic,

Species, and Ecosystem diversities. All of them are linked and constitute a gene pool. Genetic biodiversity means the variation of

genes within a species. Specie can have varieties and each variety has its own genes or genetic make up. Diversity of genes within

specie increases its ability to adapt to disease, pollution, and other changes in environment. When varieties of species are

destroyed, genetic diversity gets diminished. Species biodiversity means variety of species within a region and can be measured

on the basis of species in a region. More species biodiversity means more biological wealth.

Ecosystem biodiversity refers to a variety of ecosystems in a particular region or zone such as forests, wetlands, arid zones,

deserts, etc. All of them have their own fauna and flora. Much of the biodiversity of the world is in the developing countries.

North, in this context, managed to use environmental negotiations to promote a broader agenda from sustainable development to

international equity. It succeeded in linking North’s demand for preservation of biodiversity to South’s interest in financial

assistance for conserving biodiversity and access to biotechnology. North was prompt in declaring South’s biodiversity as a global

or common heritage.

This attitude was apparent at Rio on the question of IPRs. The Rio document, while reflecting the superior power position of the

North, shows a shift in international norms towards concerns of the South. By asserting their sovereignty over nature’s resources,

the South secured legitimization of their right to put restrictions of access to their resources. They received partial success in

regard to technology transfer. While the Biodiversity Convention allows research, profits, and technology to be shared with the

country whose resources are used, the type of language used here falls short of making this mandatory. Even then, the U.S. was so

very much opposed to it that President George Bush declined to sign the convention; it was done later by the new administration.

MANAGEMENT OF FORESTS: SOME CONFLICTING AND ESCALATING DEMANDS

The management of forest resources and forest ecosystems is a challenging task. This task becomes more challenging when other

important related issues like biodiversity face serious threat. (Tropical forests, for example, contain about 90% of biodiversity). It

is true that forests are renewable resource. Forests contribute to economic development by providing goods and services for

industry, people and forest dwellers. They provide employment and play a vital role in maintaining ecological balance, quality of

environment, preventing soil erosion, conserving water, regulating water cycle, maintaining balance between Oxygen and

Carbondioxide and preventing floods. But, are they managed in a way that would keep them renewable and will they continue

playing vital roles as above?

India has a diverse ecosystem and diverse forest spread over from North East to Western Coast and Andaman Nicobar islands to

alpine forests of Himalayas. Forest Management is a specialized discipline and a complex system that deals with various aspects

such as legal, administrative, economic, social, and scientific. It aims at efforts towards conservation of forests, which include

maintaining its ecosystem, for production of goods and services that are economically, socially, legally viable and sustainable.

The most common problem confronting the forestry sector is its inability to satisfy the many conflicting and escalating demands

being placed on it. It is evident that future sustainable forestry development for India will require a comprehensive balanced and

targeted strategy.



Although the pace of diversion of forestland has come down, the area stipulated for compensatory afforestation has not been

commensurate. It is clear that the country’s forest resources are not being managed to their full potential. Some of the reasons

contributing to the present status are: sustainable forest management is complex, costly, and difficult; and research and technology

inputs have been low and sporadic. With more and more concerted and well-directed initiatives, it is expected that the objective of

the National Forestry Action Program—to enhance the contribution of forestry and tree resources to ecological stability and

people-centered development through qualitative and quantitative improvement in the forest resources - would be realized.

CONCLUDING REMARKS

In conclusion, it may be pointed out that the issue of sustainability of environment goes beyond the problems relating to

protection of environment or nature in terms of pollution, resource utilization, or waste disposal. It relates to the issue of

exploitative nature, an attitudinal problem, which should be addressed in a rational and logical way. Pollution control became a

high social priority all over the world and environmental cleanup continues to receive public support, and there is evidence of

increasing cooperation between business and environmentalists.

However, the cleanup task is far from over and will continue to be a challenge for business during the remainder of the twenty

first century. Ecological issues can be condensed into a few basic guidelines for business. As society’s major economic institution

for production of goods and services, business cannot ignore the natural environment. It must live in harmony with nature by

observing guidelines such as (1) to reduce pollution to the extent that is economically and technically reasonable, (2) to design

future facilities and activities for ecological harmony, and (3) to respond meaningfully to ecological concerns expressed by

stakeholders and the general public.

In this situation, society needs to be cautious that it does not become overzealous and excessive in its demands for virtually zero

ecological effects from business activities. Almost every human activity has an ecological impact. The idea is not to prevent

activity, but rather to assure that activity is in harmony with nature so that nature’s powers of self-restoration will maintain a clean

and livable environment.

REFERENCES

1. Bowen, Howard R.: Social Responsibilities of the Businessman, New York: Harper, 1953.

2. Chamberlain, Neil W.: The Limits of Corporate Social Responsibility, New York: Basic Books, 1973.

3. Convention on Biological Diversity (CBD), 1988.

4. Convention on Ozone layer protection (The Vienna Convention), 1985.

5. Convention for the Protection of Ozone Layer (Montreal Protocol), 1987.

6. Earth Summit 2002 – Johannesburg Summit.

7. The Environment (Protection) Act, 1986.

8. The Environment (Protection) Rules, 1986.

9. Global Environmental Negotiations – 1999, CSE.

10. Municipal Solid Wastes (Management and Handling) Rules, 2000.

11. Recycled Plastics Manufacture and Usage Rules, 1999.

12. The Radiation Protection Rules, 1971.

13. The Forest (Conservation) Act, 1980.

14. United Nation’s Conference on Human Environment Report, 1972.

15. United Nations Development Programme (UNDP), USA, Reports.

16. United Nations Environment Programme Vienna Convention Report, 1963.

17. World Commission on Environment and Development Report (Our Common Future), 1987.

18. World Summit for Sustainable Development (Johannesburg) Report, 2000.

*****



IMPACT OF CREDIT RATING ON INVESTMENT ALTERNATIVES AND STOCK

PERFORMANCE OF SELECTED PUBLIC SECTOR BANKS IN INDIA

K. Kannan27 S. Prakash28

ABSTRACT

This study has been undertaken to analyze the impact of credit rating given by various private agencies in India for the

purpose of understanding how effectively credit risk is being managed by selected public sector companies in the banking

industry.

From this study, the practical implication of different type of ratings given by these agencies on various investments provided

by these banks has been derived and the ratings provided by CRISIL and ICRA for the selected companies have been

considered for analysis and also for arriving at appropriate findings and for providing required suggestions.

Banking industry has been selected for this study due to the variability in the efficiency among the public sector banks in India

to deliver their long- term financial commitments towards their customers and other stakeholders. Public sector banks in India

in recent times have faced increasing problems with Non- Performing Assets (NPAs) due to the inability of their customers to

repay debts within the stipulated time period.

This has also created a significant influence on the performance of the stocks of these companies. Investors buying stocks of

these banking companies are not able to receive high returns on the stocks due to the liquidity crunch faced by banking

companies in India.

Therefore, the risk levels of various investment alternatives of the selected banks have been assessed in this study through the

credit ratings and its impact on the market prices of the stocks of these companies has also been measured.

The suggestions provided in this study would help the selected companies to identify their credit risk areas and take

appropriate steps in the future which would help them to achieve financial soundness in the future.

KEYWORDS

Credit Rating, CRISIL Credit Rating Information Services of India Ltd, Pass Through Certificate, Upper & Lower

Tier II Bond, and Current Account Savings Account Deposit etc.

IMPORTANCE OF CREDIT RATING AGENCIES IN INDIAN STOCK MARKETS

The importance of credit rating is to protect the investor who cannot get inside information about the instruments for investments

due to lack of time and expertise. Credit rating has assumed an important place in the modern and developed financial markets. It

is a boon to the companies as well as for the investors. It facilities the company in raising funds in the capital market and helps the

investors to select their risk return trade off.

As investors are concerned with the timely payment of interest and principal, credit rating indicates the credit worthiness of

borrowers. Credit rating essentially indicates the risk involved in a debt instruments as well as its quality. Higher the credit rating

greater is the probability that the borrower will make timely payment of principal and interest and vice versa.

Thus credit rating is not a general evaluation of the issuing organization. It essentially reflects the probability of timely repayment

of principal and interest by a borrower to a company. The credit rating is not a onetime evaluation of credit risk of a security. The

rating agency may change the rating considering the changes periodically.

The impact of credit rating agencies on financial markets has become one of the most important policy concerns facing the

international financial architecture. Ratings indicate a relative credit risk and serve as an important metric by which many

investors and regulations measure credit risk.

SCOPE OF STUDY

Credit rating is an important tool which helps companies as well as investors to know the level of risk which is present in different

investment options. In recent times, capital markets in India have been subject to greater regulations and therefore assessment of

credit risk has become important for investors and companies.

Stocks of various companies in India are subject to greater market risk due to the increasing trading being done in the markets

with the help of several market intermediaries such as the stock brokers, investment managers and other financial intermediaries.

27Assistant Professor , RVS Institute of Management Studies, Tamilnadu, India, [email protected] 28Professor, Department of MBA, SNS College of Technology, Tamilnadu, India, [email protected]





Banking companies in India have been facing problems relating to bad debts and also difficulties in debt management due to

delayed repayments made by customers and its negative influence on the overall capital structure of these banks.

Banking companies, today also issue debt based instruments such as bonds and debentures which are credit rated by various

agencies which significantly influence the decisions of investors.

This study relates to an assessment of effectiveness of credit risk management by selected private banks in India during 2006-07

to 2010-11.

This study would help to review the credit risk management policies periodically and identify specific factors which either

influence the stock prices of the companies fundamentally or which create high risk levels for investors. This would help the

companies to identify specific areas through which risk protection can be provided to investors and also serves as a feedback for

investors to know whether stocks of the selected companies would provide the expected return for investors.

OBJECTIVES OF STUDY

1. To know about the level of influence of credit rating on the investment alternatives and the stock performance of

selected banking companies in NSE during 2006-07 to 2010-11.

2. To identify credit rating agencies which significantly influence the investment alternatives and market performance of

the stocks of selected banking companies.

3. To find out the best performing stock with regard to the credit rating provided by the rating agencies.

4. To make a comparative analysis of the market prices and the fluctuations of the selected companies.


A good research plan calls for developing the most efficient way of gathering the required information. In this we discuss the

research design chosen to conduct the study and data analysis, keeping in mind the objectives of the study and to validate the

findings. The structure of the research plan is described below:

Research Design

The present study is an attempt to find out the impact of credit rating on investment alternatives and stock performance of selected

public sector banks in India.

In this study the researcher has chosen Five Public Sector Banks in India and the last five years (2007-2011) details of market

price of stock market were used.

The research design used for the study was Analytical Research Design.

Sources and Collection of Data

The researcher has identified secondary resources for the collection of data.

Secondary Data

The secondary data relating to the details of the Banks and market price of stocks of selected companies were taken

from National Stock Exchange, CRISIL, and Default risk websites.

DATA ANALYSIS

Table-1: Credit Rating Given By Agencies as On 31/12/2011

S. No. Name of

Company Nature of Investment

Credit Rating

Given by CRISIL

Credit Rating

given by ICRA

1 Allahabad Bank Perpetual Debt AA+ (Stable)

Upper Tier II bonds AA+ (Stable)

Lower Tier II bonds AA+ (Stable)

Certificate of Deposits A1+

CASA Deposits A1+

2 Andhra Bank Perpetual Tier I bonds AA+ (Stable)

Upper Tier II bonds AA+ (Stable)

Pass through Certificate AAA (SO)

- -

3 Bank of Maharashtra Perpetual Debt AA (Stable)

Upper Tier II bonds AA (Stable)

Bonds AA+ (Stable)



Certificate of Deposit A1+

Overall Debt Programme A1+

Lower Tier II bonds AA+

Upper Tier II bonds AA

4 Bank of India Perpetual Tier I Bonds AAA (Stable)

Lower Tier II Bonds AAA (Stable)

Certificate of Deposit A1+

Perpetual bonds AA+ (Stable)

Overall Debt Programme AAA(Stable)

MAAA

Upper Tier II bonds AA+(Stable)

5 Canara Bank Perpetual Tier I bonds AAA (Stable)

Upper Tier II bonds AAA (Stable)

Lower Tier II bonds AAA (Stable)

Infrastructure bonds AAA (Stable)

Certificate of Deposits A1+

Lower Tier II bonds LAAA

Certificate of Deposits Programme A1+

Table-2: Comparison of Annual Market Prices of Stocks of Selected Companies

(Annual % Increase or Decrease in the mkt. price of the stocks as shown in NSE India)

S. No. Name of the Bank As on

31-03-07

As on

31-03-08

As on

31-03-09

As on

31-03-10

As on

31-03-11

1 Allahabad Bank -10.75 + 10.79 -49.38 +260.28 + 49.48

2 Andhra Bank -8.81 +2.36 -39.49 +135.22 +38.85

3 Bank of Maharashtra +28.24 +35.25 +135.27 +126.26 +19.4

4 Bank of India +26.69 +64.18 -16.93 +54.45 +37.23

5 Canara Bank -28.6 +4.92 -24.59 +151.04 +54.61

RESULTS AND DISCUSSIONS

This study has discussed about the influence of credit rating on various investment alternatives provided to selected public sector

banks in India. Various other investment options such as the Savings based investments and other similar alternatives could have

been discussed in this study.

Also, various factors influencing stock prices such as the market risk, market return, the trading volumes of stocks, economic

factors could have been discussed in this study for knowing the extent of correlation between the market related factors and the

market prices of stocks of these companies.

FINDINGS

The following are some of the major findings of this study:

The Pass through Certificate Investments has not been provided by the banks frequently during 2006-07 to 2010-11

even though they have performed consistently.

Tier-I Perpetual Bonds of the banks issued during 2006-07 to 2010-11 have received a higher credit rating than the Tier-

II Perpetual Bonds of the banks.

The CASA investments are not frequently provided by the banks to the investors and also have not received a high

credit rating from CRISIL and ICRA.

The Overall Debt Programme of the banks have been credit rated by ICRA and a AAA (Stable) MAAA credit rating has

been given for Bank of India and a A1+ credit rating for Bank of Maharashtra.

The Certificate of Deposits of most of the banks has received a lower credit rating of A1+ from CRISIL and ICRA.

The stock prices of Bank of Maharashtra have shown a continuous increasing trend during the period 2006-07 to 2010-

11.

The stock prices of Allahabad Bank have shown significant variations during the period 2006-07 to 2010-11.



CONCLUSIONS

This study shows that out of the total sample size taken, only one bank has been consistently received a credit rating of AAA or

AA from CRISIL and ICRA during the period 2006-07 to 2010-11 and other most of the other banks in India have not received a

consistent credit rating during the period considered for study. Also, the stock prices of these companies show fluctuations during

the years 2006-07 to 2010-11 and therefore, it shows that credit rating given by agencies have a major influence on the stock

prices of these companies.

REFERENCES

1) Pandey, I. M., (2006). Financial Management, Tata McGraw Hill Publishers Co. Pvt. Ltd, 8th Edition, pp. 715-718.

2) Maheshwari, S. N., (2005). Financial Management- Principles and Practice, New Delhi, Sultan Chand Publishers Pvt.

Ltd., 10th Edition, pp. D 470-D 474.

3) Punithavathy, Pandian, (2009). Security Analysis and Portfolio Management, Vikas Publishing House Pvt. Ltd.,

Second Reprint Edition, pp. 13-16.

4) Bhalla, V. K., Investment Management, New Delhi, Sultan Chand Publishers Pvt. Ltd., 1999, 11th Edition, pp. 17-19.

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6. www.nseindia.com

7. www.capitaline.com

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THESIS ABSTRACT

ON

FUNDAMENTAL ANALYSIS AS A METHOD OF SHARE VALUATION

IN COMPARISON WITH TECHNICAL ANALYSIS

BY

Venkatesh. C. K.29

THESIS ABSTRACT

Understanding the theory behind stock prices is vital before investing. Most retail investors find it difficult to make any real gains

out of Stock market investments. Many of them lost money when the stock markets went through a massive fall in the years 1993-

1996. For many years after that, retail investors largely avoided the markets. Late 1999 and the first few months of the year 2000

saw a partial revival of the general interest. However, the crash, particularly in technology stocks since April 2000, has given

investors another reason to shy away from the markets.

Does this mean that only a privileged few can make money on the markets? What about the common man? Basically, investing in

shares in Stock market is not everybody’s ball game and the success of the investor depends on certain key factors which are:

a. Access to information,

b. Ability to think rationally,

c. Correct mental attitude,

d. Time, and

e. A sound theoretical frame work.

There are three Principal theories of stock valuation and investments: a) The Fundamental analysis, b) the Technical analysis, and

c) the Efficient Market Hypothesis. Rational investors will take a decision to buy, sell or simply hold stock based on their notion

about the traded share prices: whether it is more or less what it should be or is going to be. Unless an investor has this clarity,

nobody can make rational decisions. The theories of investing essentially deal with how to form these views. They give a logical

frame work, using which an investor can take decision in a rational manner.

FUNDAMENTAL ANALYSIS

This is done at three levels- Economy, Industry and Company. At Economy level Fundamental analysis focuses on the Economic

indicators of the country to assess the present and future growth of the economy. Major economic indicators include the Interest

rates, Gross Domestic Product growth rate, Inflation, Purchasing Power etc. The basic assumption is that if the economy grows,

companies would do well.

However, the economy is not the only thing that affects the prices of securities. Even in a growing economy, certain sectors are

likely to benefit more than the others. Factors like Government attitude, Competition level, threat of potential entrants, Cost

structure, Foreign entrants, all affect the way an industry evolves in time and hence affects the stock prices of the companies in

that industry. Interestingly, while investors look for higher growth securities in an expanding economy, the same investors may

opt for more stable and income oriented companies like consumables and other FMCG stocks in a contracting economy.

Within an industry the next task is identifying the company. For that a number of factors are looked into which includes

company’s competitive advantage, its market share, the quality of leadership and top management, the future prospects of the

company, control over certain important strategic sources of raw material, the labor relations, the financials of the company etc.

In analyzing the financial data the most popular source of information are its financial statements- the balance sheets and the

income statements. The factor to be calculated is the intrinsic value of the company that is its net worth. Also, one can make use

of key financials like the profitability ratios, debt equity ratios or leverage, current ratio, asset turn over ratio, the interest coverage

ratio, the debt service ratio, price earning ratio, EPS (basic and diluted) etc. which ratios should be looked into depends on the

age, leverage, market position of the company, etc.

The major advantage of fundamental analysis is that it is good for predicting long term trends. Value investing is possible with

such analysis. However, the major disadvantage is that while it gives subjective indications of directions of movements of security

prices it does not necessarily predict the quantum of movement. It is time consuming and complex for the layman. Also, valuation

techniques vary from industry to industry and the ratios can often hide several important insights if not properly interpreted.

29Assistant Professor, Government First Grade College, Karnataka, India, [email protected]




TECHNICAL ANALYSIS

Technical analysis, on the other hand, is the evaluation of securities by means of studying statistics generated by market activity,

such as past prices and volume. Technical analysts do not attempt to measure a security's intrinsic value but instead use stock

charts to identify patterns and trends that may suggest what a stock will do in the future. Technical Analysis operates on the

theory that market prices at any given point in time reflect all known factors affecting supply and demand for a particular market.

Consequently, technical analysis focuses, not on evaluating those factors directly, but on an analysis of market prices themselves.

This approach theorize that a detailed analysis of, among other things, actual daily, weekly and monthly price fluctuations is the

most effective means of attempting to capitalize on the future course of price movements. Technical strategies generally utilize a

series of mathematical measurements and calculations designed to monitor market activity. Trading decisions are based on signals

generated by charts, manual calculations, computers or their combinations.

Technical analysts look for patterns and indicators and stock charts that will determine the stocks future performance. Technical

analysis can include volume or open interest figures long with the study of price action.

The basic advantage of technical analysis is that it often gives the investor a pictorial representation of the trend using point and

figure charts or bar charts which lends lucidity to the analysis. The method concentrates more on the when to buy question thus

ensuring maximization of profits. There are several disadvantages as well and many people have been known to loose huge

amount of money by resorting to technical analysis. One of the major reasons is that the technique is open to interpretation. The

results could be affected by the personal prejudices of the analysts.

Looking at past prices, two different analysts can predict completely different futures. It is some what akin to predicting the

weather of the day by looking at the morning sky. An even greater problem stems from a fact that there is a time lag between the

trends actually creeping in and its identification. This could result in the miscalculation of the time for the buy or sell call.

MARKET ANALYSIS

This hypothesis states that the security markets are efficient. They already discount all information declared on the company.

Hence, it is futile to try to pick under-priced stocks. Prices of securities in future will move in line with future events, this theory

says it is futile to try to predict security prices.

Adherents to this theory do not try to pick stock indices are proxies for overall market. Since stock indices are proxies for the

overall market, typical believers of this theory will bet on indices. An index-linked mutual fund is an example of this approach to

investing. Here the investor is not trying to do better than the market, but aims to profit only as much as the overall market

movement.

The evidence in favor of this theory is mixed. In the US markets, most mutual fund managers do not manage to do better than the

overall market. Hence, this class of mutual fund investors would do well to just invest in a basket of stocks reflective of overall

market levels. This is also a passive approach to investing.

However, not many people believe in this hypothesis. This is because many investors have shown that it is possible to do

consistently better than the overall market. The other two theories given above belong to the school of active investing, which

believes that it is possible to do better than market averages on a consistent basis.

Fundamental and technical analyses are on completely opposite sides of the spectrum. Earnings, expenses, assets and liabilities

are all important characteristics to fundamental analysts, whereas technical analysts could not care less about these numbers.

Which strategy works best is always debated, and many volumes of textbooks have been written and many have involved in

research to find which method would give better answers for predicting stock movements.

THE CURRENT STUDY

The current study is an effort made to investigate the relevance of Fundamental analysis and Technical analysis while investing in

the Stock market. For the said purpose the researcher has taken both Primary and Secondary data into consideration.

The first chapter of the Dissertation introduces to the major concepts underlying the fundamental and Technical analysis. It clearly

defines the role of Fundamental and Technical analysis in share valuation process. All details pertaining to these two tools are

comprehensively explained in this chapter.

The Second chapter presents a review of related Literature. The Literature in the foreign context identifies that the respondents

rely upon both fundamental and technical analysis for predicting the future movements at different time horizons. At shorter time

horizons they rely most upon technical analysis and as the time horizon increases as the length is extended they switch over to

Fundamental analysis. Technical analysis is considered useful in forecasting trends and the fundamental analysis is more useful in

forecasting turning points.



The Literature in the Indian context clearly identifies that models which are based on a theory of economic fundamentals alone do

not offer sufficient explanations of short term price movements. It is noted that standard Macro economic analysis cannot predict

most short term rate/price changes.

The third chapter focuses on the methodology. It defines the problem statement, gives operational definitions of the terms used

and lists out the variables used. The hypotheses used in the study are also listed. The sampling procedure used is Convenience

sampling.

The last part of dissertation is about the Statistical analysis, Interpretation of outputs, summarizing findings and arriving at

conclusions. ANOVA, Chi-Square test and descriptive analysis have been used to test the data for differences and dependencies.

Secondary Data Analysis

Analysis of Secondary data is based on F-SCORE as developed by Piotroski (2000) and GSCORE as developed by Mohanram

(2005). For the purpose of Secondary analysis companies having High and Low FSCORE/GSCORE were chosen. Ten companies

on the top of the list and ten companies at bottom were chosen from three sectors namely:

a. Information Technology.

b. Manufacturing.

c. Banking.

After such analysis constructed F-Score / GSCORE is compared with market returns and trading volumes for assessing the

Volatility and Distribution of returns of firms with High F-Score/G-Score and Low F-score/G-Score is made using standard

deviation, a correlation is established between the score and returns in the different periods. Financial distress of low score firms

is measured by computing Z test.

Later, in the analysis both FSCORE and GSCORE results are unified so as to develop a unified approach to integrate the two tools

of analysis, that is, Fundamental and Technical analysis. There after an effort is made to measure the covariance of past returns

and past trading volume of each individual stock.

Primary Data Analysis

Primary data is collected by serving a structured questionnaire to various respondents who directly connect themselves to the

stock market.

The chosen respondents were Brokers, Sub-Brokers, Fund Managers, Portfolio managers and the Bankers who involve

themselves directly in stock market operations.

A total of 600 (six hundred) questionnaires were served to the respondents in Delhi, Mumbai, Bangalore, Cochin, Hyderabad and

Hubli. The chosen sample is the mixture of different levels of the management.

The study tries to find:

The commonly used method in forecasting trends and turning points in the stock market.

The demographic profile of respondents (for the purpose of this study respondents include, brokers, sub-brokers, mutual

fund companies, institutional investors).

The frequency of usage of analysis by various stock market Participants while investing in different sectors.

The adoptability of Fundamental and Technical analysis to various market conditions / situations.

The importance given by dealers to Fundamental and Technical analysis over intervals of forecasting horizons.

The commonly used method while taking positions in Large, Mid and Small Cap companies.

The adoptability of Fundamental and Technical analysis when the market is bullish and bearish.

The advice given by the informed investors for Long and Short term investors with respect to the usage of analysis.

KEY WORDS

Fundamental analysis, Technical analysis, Efficient Market Hypothesis, Valuation of Shares, Earnings Per Share, Relative

Strength Index, Technical Indicators, Book Value, Market Value, Debt Equity ratio, Dividend Yield, Dividend Pay Out ratio,

Economy, Industry, Company analysis, Book-to-Market Value ratio, Price Earning ratio.

*****



BOOK REVIEW

ON

INVESTMENT ANALYSIS AND PORTFOLIO MANAGEMENT

BY

Pravin Narayan Mahamuni30

Book Title

Investment Analysis and Portfolio Management.

Author (s)

Frank K. Reilly, University of Norton, Dame,

Keith C. Brown, University of Texas, Austin.

Edition

Eight (Indian Edition)

Publication By

Cengage Learning India Pvt. Ltd.

Purpose of Book

To help you learn how to manage your money so that you will decide the maximum benefit from what you earn.

To Whom It Is Applicable

This book is addressed to graduate & post graduate students as well as faculty members who are looking for an in-depth

knowledge of investment & portfolio management.

About the Book

This book is divided into 7 Parts i.e. Investment Background, Developments in Investments theory, Valuation principles and

practices, Analysis and management of common stocks & bonds, Derivative security analysis, and Specification & evaluation of

Assets management.

PART ONE: THE INVESTMENT BACKGROUND

This part deals with a background for study of investments:

Chapter 1: The Investment Setting

This chapter deals with the understanding basic concept like Investment, Risks & Returns related to the investments etc &

background that can be used in subsequent chapters.

Chapter 2: The Asset Allocation Decision

This chapter examines some of the practical implications of risk management in the context of asset allocation. The chapter

concludes by examining asset allocation strategies across the national boarder to show the effect of market environment and

culture on investing patterns.

Chapter 3: Selecting Investments in Global Market

In this chapter, they address the issues about the investment opportunities available in financial market, by surveying investment

alternatives. This is essential step for making the asset allocation decision which was discussed in previous chapter 2 and for later

30 Asstistant Professor, ZES’s Dnyanganga Institute of Career Empowerment & Research, Pune, Maharashtra, India,

[email protected]




chapters where they analyze several individual investments, such as bonds, common stocks & other securities. Also they

mentioned very important point regarding how to construct and evaluate portfolios of investments.

Further this chapter is divided into three sections i.e. discussion of including foreign & domestic securities in portfolios, in the

second part they discuss securities in domestic & global markets, describing their main features & cash flow patterns, and third

part contains the historical risk & return performance of several investment instruments from around the globe and also examines

the relationship among the returns for many these securities. These results provide strong empirical support for global investing.

Chapter 4: Organization and Functioning of Securities Market

Including this chapter was very good attempt made by the authors because of every investor to become a successful in a global

environment, they must know what financial market is available around the world & how they operate. Therefore, this chapter

gives broad view of securities market & provides detailed information of how the major stock markets functions. Finally,

conclude with a consideration of how global securities markets have changed during the recent years & probably will change in

the near future.

Chapter 5: Security-Market Indexes

In this chapter, they discuss several ways that investor use market indexes, what characteristics cause various indexes to differ.

Also present the major U.S. and global stock-market indexes & their characteristics. In the lateral part of this chapter contains

bond market indexes which are relatively new topic because the creation and maintenance of total return bond indexes are new.

This chapter ends with examine how alternative indexes relate to each other over monthly intervals.

PART TWO: DEVELOPMENTS IN INVESTMENT THEORY

This part is to provide background on risk & asset valuation:

Chapter 6: Efficient Capital Market

This chapter describes the concept of efficient capital market, which hypothesizes that security prices reflect the effect of all

information. Also, describes behavioral finance & discuss how it explains many anomales is expanded and updated to include

recent studies. There is an expanded discussion of implications of recent findings related to the efficient market hypothesis for

both analyst and portfolio managers.

Chapter 7: An Introduction to Portfolio Management

This chapter provides an introduction to portfolio theory which was developed by Markowitz. This theory helps to understand the

first rigorous measures of risk for investor and showed how one selects alternative assets to diversify and reduce the risk of

portfolio.

Chapter 8: An Introduction to Asset Pricing Model

In this chapter they describe one of the model i.e. Capital Asset Pricing model (CAPM). The background on the CAPM is

important because the risk measure implied by this model is necessary input for continuing discussion on the valuation of risk

assets.

Chapter 9: Multifactor Models of Risk and Return

The discussion of the theory and practice using multifactor’s models of risk and expected return has been updated & expanded.

The connection between the Arbitrage Pricing Theory (APT) and their implications of APT explained in both ways by

conceptually and with relevant new examples.

PART THREE: VALUATION PRINCIPLES AND PRACTICES

This part deals with the analysis of financial statements & introductory part of Security valuation:

Chapter 10: Analysis of Financial Statements

This chapter contains detailed examine the financial statements & which type of information they provides, followed by

discussion of why & how financial ratios are useful to give answers to several important related questions. Also provides

examples of computation of ratios that reflect liquidity, operating performance, risk & growth analysis and address four major

areas in investments where financial ratios have been effectively employed.



Chapter 11: An Introduction to Security Valuation

This chapter describes the basic principles of valuation and applies those to the valuation of variuos securities. Given more

emphasis on the two alternative approaches to valuation i.e. present value of cash flows and relative valuation ratios. This chapter

concludes by reviewing the basic factors that are influence the two criticale variables that determine intrinsic value of an asset

irrespective to the valuation model.

PART FOUR: ANALYSIS AND MANAGEMENT OF COMMON STOCKS

This part deals with the various techniques for analysis of stocks:

Chapter 12: Macroanalysis and Microvaluation of the Stock Market

This chapter is divided into two sections i.e. Macroanlysis, deals with macroeconomic variables that affect the stock market and

Microvaluation of these markets using valuation concept & valuation techniques which is mentioned in previous chapter i.e.

present value of cash flow & relative valuation.

Chapter 13: Industry Analysis

This chapter discuss about analyze of different industries to determine if intrinsic value of an industry is equal to or greater than

its market price, which will help readers to identify the uses & benefits of Industry analysis. Based on this relationship, investors

can decide how to weight the industry in their stock portfolio.

Chapter 14: Company Analysis and Stock Valuation

This chapter clears that difference between company analysis & stock valuation which will more important from the point of view

of readers. Also discuss some competititive strategies that can help firms maximize returns in industry’s competitive environment.

Finally conclude with a discussion of important factors to consider when analyzing foreign stocks.

Chapter 15: Technical Analysis

This chapter starts with an examination of basic philosophy underlying technical analysis & discusses the advantages & problems

with the technical approach.

Chapter 16: Equity Portfolio Management Strategies

This chapter contains an expanded discussion of relative merits of passive versus active management techniques for equity

portfolio focusing on the important role of tracking error. Also includes enhanced analysis of equity portfolio investment

strategies, including fundamental and technical approaches, as well as a detailed description of equity style analysis.

PART FIVE: ANALYSIS AND MANAGEMENT OF BONDS

This part discusses the fundamentals of bonds, their analysis & valuation. Also helps to understand the bond portfolio

management strategies:

Chapter 17: Bond Fundamentals

This chapter reviews some basic features of bonds and examines the structure of the world bond market. The most of the part

involves an in-depth discussion of major fixed income investments. The chapter concludes with a brief review of price

information sources for bond investors.

Chapter 18: The Analysis and Valuation of Bonds

This chapter deals with application of valuation principles that was introduced in previous chapter 11 – Valuation of Bonds.

Chapter 19: Bond Portfolio Management Strategies

This chapter initially discusses the five alternative bond portfolio management strategies i.e. passive management, active

management, core-plus bond management, matched-funding, and structure active management. And remaining part deals with the

implication of capital market theory and market efficiency on bond portfolio management.



PART SIX: DERIVATIVE SECURITY ANALYSIS

The chapters in this part address this concern by providing the investor with a framework for understanding how derivatives are

valued & practice:

Chapter 20: An Introduction to Derivative Markets & Securities

This chapter being with an overview of derivative market & role played by derivative securities in modern investment portfolios.

The chapter concludes with several specific examples of how investors use derivatives to adjust the risk-return characteristics of

their portfolios.

Chapter 21: Forward and Futures Contracts

This chapter initially discuss important difference between these two exiting market and describe the future contracts are market

to market on a daily basis. Further examine how investors can use forward and future contracts to hedge the risk with the help of

demonstrating several applications and strategies in the end of chapter.

Chapter 22: Option Contracts

This chapter contains several new & updated examples designed to explain how investors use option contracts in practice.

Chapter 23: Swap Contracts, Convertible Securities, and Other Embedded Derivatives

This chapter deals with revised discussion of some advanced applications i.e. Swap contracts, Convertible securities, real option,

structure notes etc. Also explain updated examples and application of each of these applications.

PART SEVEN: SPECIFICATION AND EVALUATION OF ASSET MANAGEMENT

This part is designed to address the needs of individual and institutional investors:

Chapter 24: Professional Asset Management

This chapter examines how the operational structure of asset management companies. Further discussed how to analyze the

aggregate market, alternative industries, and individual companies as well as their stocks & bonds in order to build a portfolio is

consistent with investment objectives. The chapter concludes with the discussion of ethics and regulation in asset management

industry.

Chapter 25: Evaluation of Portfolio Performance

This chapter deals with the how to evaluate the performance of portfolio through providing an updated & considerably expanded

application of performance measurement techniques.

CONCLUSIONS

The authors made very good attempt to include the two separate subjects but related to each other. This book is systematically

bifurcated into seven parts, and each part contains several chapters related to them.

The Investment Analysis and Portfolio Management book is provides in-depth knowledge of investment analysis and portfolio

management with the help of explaining, examining, and applications of new & updated several examples which will helpful for

both individual as well as institutional investors to apply these in their day today practical life for gaining advantage.

SUGGESTIONS

In this book, the authors relate concepts to U.S market. The book would have been more applicable & interesting if they would

have explained concepts to Indian context, which will be helpful to India reader to cope up this subject & gaining the advantage in

their practice.

*****

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