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SIT Journal of Management Vol: 5; Issue: 1, pp-78-117
ISSN: 2278-9111
IF: 1.232
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Bandyopadhyay & Saha
Inflation and Exchange Rate Impact on Stock Returns of Banking Companies
of India after the Crisis period of 2008-2009
Shounak Bandyopadhyay* & SoumyaSaha**
Abstract
The reasons for fluctuation of banking stock returns are manifold. This study looks at two specific
factors – Exchange rate and Inflation. Rich body empirical literature also proved the relationship
between inflation, exchange rate with stock returns both at industry level and firm level globally. In
the Indian context, very few studies have been done in this regard. The purpose of this study is to
find whether the banking industry as well as banking firms is exposed to exchange rate risks and
inflation. The study also investigate the casual relationship between inflation and Bank stock returns,
and exchange rate with bank stock returns both industry as well as firm level. The period of study is
1st January 2005 to 31st December 2014. To find the association between the said variables we use
correlation analysis. Augmented - Dickey Fuller test and Philip-Perron test has been used to find the
stationarity. The casual relation among the said is determined by using Granger causality test.
Regression analysis has been done to the find the impact of inflation and exchange rate on stock
returns. This study will help the investors, speculators, arbitrageurs for their investment decisions
and also managers of the company to make strategic decisions.
Keywords: Bankex; Banks; Exposure; Exchange Rate; Inflation; Granger Causality.
*Corresponding Author: SoumyaSaha
*Shounak Bandyopadhyay ; Student; M.Com; St. Xavier’s College (Autonomous), Kolkata
**SoumyaSaha; Assistant Professor; Post-Graduate Department of Commerce; St. Xavier’s
College (Autonomous), Kolkata; email: [email protected]; M: +91-9903935396.
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Introduction
Banks are a major part of any economic system. They provide a strong base to Indian
economy too. Even in share markets, the performance of bank shares is of great
importance. This is justified by the proof that in both BSE and NSE we have separate index
for Banking Sector Shares. But for our study we have taken only BSE Bankex and individually
the 12 companies of the BSE Bankex. Thus, the performance of share market, the rise and
the fall of market is greatly affected by the performance of Banking Sector Shares and this
paper revolves around some of those factors, their understanding and an empirical and
technical analysis of the same.
Internal or External conditions both are involved in measuring the sensitivity of returns of
stocks. Industrial Production, money Supply , Foreign exchange Rate, Interest rate, gold
prices, GDP and oil prices in the world economy are involved in external conditions whereas
dividend policy, earning per share etc. are the contributors of internal factors. This paper
studies the impact of Macro (External) factors on BSE Bankex. Macroeconomic indicators
are already exhibiting signs of slight improvement after a sustained period of deterioration
as Rupee is appreciatingagainst dollar, WPI inflation was 00 in November 2014, interest
rates and gold prices are decreasing with the RBI bringing down its repo rate recently and
industrial production has started to rise marginally.
The study which measures the impact of two specific macroeconomic forces on various
sectors of stock exchange index is rare in the literature so this study provides a new way in
the extending line of literature.
Review of Literature
Few Selected reviews on the particular area of study are as follows:
International Research Papers:
A study by SadiaSaeed and Noreen Akhter on “Impact of macroeconomic factors on
banking index in Pakistan”, analyzed the macroeconomic factors on the banking index of the
Karachi stock exchange. The macroeconomic variables selected were exchange rate,
industrial production, money supply, oil price and short term loan. The study found that oil
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price has impact on banking index, economic variables like money supply, exchange rate;
short term loan and industrial production rate had negative effects on the banking index.
The article entitled “Banking stock returns and their relationship to interest rate and
exchange rate: Australian evidence”, by John Simpson, investigated the dynamic interaction
and long run relationship between Australian bank stock returns and key macroeconomic
variables and monetary policy variables like interest rate and exchange rate. The study
found there was no evidence of Australian bank stock returns from co-integrated relation
with short run and long run exchange rate and interest rate during the study period.
Australian monetary authorities have placed a strong belief on the health and performance
of the banking sector and the financial sector as their setting of monetary and exchange rate
policy.
Gan, Lee, Yong and Zhang (2006)examined the relationship between stock prices and
macroeconomic variables for New Zealand. The variables are long-run and short-run interest
rate, inflation rate, exchange rate, GDP, money supply and domestic retail oil price. Their
findings suggest that there exist a long term relationship between stock prices and selected
variables in New Zealand. However, the Granger causality test suggests that New Zealand
stock exchange is not a good indicator for macroeconomic variables in New Zealand.
A research paper entitled “Interest rate risk and bank common stock returns from the Greek
banking sector” by Konstantinos Drakes, examined the effects of changes in the long run
interest rate on bank stock returns in the Athens stock exchange. The sensitivity of stock
returns was tested by interest rate, which allows the time varying conditional volatility. The
study found that the consistent provides evidence for significant sensitivity of bank stock
returns to interest rate co-movements.
Maghayereh (2003) investigated the long run relationship between the Jordanian stock
prices and selected macroeconomic variables using co-integration analysis and monthly
time series data from January 1987 to December 2000. This study treasures that
macroeconomic variables as exports, foreign reserves, interest rates, inflation, and
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industrial production are reflected in stock prices in the Jordanian capital market. The study
concludes that macroeconomic variables are significant in predicting changes in stock prices.
Erdogan and Ozlale (2005) investigated the influence of varying macroeconomic variables
on stock return of Turkey and found that industrial production and exchange rates were
positively related with the stock return. On the other hand, Circulation in Money (M1) had
no any significant impact on stock return.
Abdalla and Murinde (1997) examined interactions between stock prices and exchange
rates for four emerging markets (India, Korea, Pakistan and Philippines) using granger
causality and co-integration techniques, their study reveals a unidirectional causality
running from exchange rate to stock prices.
Pan et al. (1999) used daily market data to study the causal relationship between stock
prices and exchange rates in China and found that the exchange rates Granger-cause stock
prices with less significant causal relations from stock prices to exchange rate.
Agus and Carl (2004) investigated the statistical relationship between stock prices and
exchange rates using granger causality and Johansen co-integration test in four SEAN
countries (Indonesia, Philippines, Singapore and Thailand). The study found that the
relationship between stock prices and exchange rates is characterized by a feedback system.
The co-integration test found that all the stock prices and exchange rates in the four
countries are co-integrated and the causality runs from exchange rate to stock prices.
Ajayi and Mougoue (1996) investigate the short-and long-run relationship between stock
prices and exchange rates in eight advanced economies, the results on short-run effects in
the U.S. and U.K. markets. Their findings show that an increase in stock prices causes the
currency to depreciate for both countries.
Indian Research Papers:
Luthra and Mahajan (2012) studied the impact of macroeconomic factors on BSE Bankex.
Macroeconomic variables involved GDP growth rate, inflation, gold prices and exchange
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rate. The results conclude that inflation, exchange rate and GDP growth rate affect the
Bankex positively. However Gold Prices affect BSE Bankex negatively but none of these
variables have a significant impact on the stock prices of banks.
A research paper by Pooja Singh (2014)on “An empirical relationship between Indian Stock
Market indices and macroeconomic indicators” tends to convey the relationship between
macroeconomic variables and Indian stock market. The explanatory variables are Index of
Industrial Production, Wholesale Price Index, Money Supply, Interest Rates, Trade Deficit,
Foreign Institutional Investment, Exchange rate, Crude Oil Price and Gold Price. Her
conclusion was that Indian stock market has significant influence of gold prices, inflation,
money supply, exchange rates and foreign institutional investments. The gold has adverse
effect on Indian Stock market that shows the increasing interest of investors in the precious
metal. There is a negative impact exchange rate on stock market. The money supply has
positive impact on the stock market that reveals that lager money in circulation has
favorable impact on stock market during the period of study. The inflow of foreign capital is
value addition to the market as it has significant impact over stock market.
In “Relationship between interest rate changes and banking stock returns in upmarket and
down market situation” by RenuGhosh, (2014)the effects of changes in the interest rate on
common stock returns of banks included in BANKEX in India is studied. The final conclusion
is that a weak relationship is found between Bank stock returns and interest rate changes in
India. With respect to relationship between Bank stock returns and Market returns there
exist a positive and significant relationship. The impact of market returns on the individual
bank stock returns, equally weighted portfolio returns and Bankex returns is higher than the
impact of interest rate changes.
BaranidharanSubburayan, Dr.VanithaSrinivasanin “The Effects of Macroeconomic Variables
on CNX Bankex Returns: Evidence from Indian Stock Market”investigated the long run
interaction between economic variables and CNX bank returns. The key active economic
variables namely exchange rate, interest rate and inflation rate were considered for
analysis. The study reveals from the analysis, selected sample macroeconomic variables
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were influencing the CNX Bankex index. Co-integration test exhibits imply the bank stocks
returns have fixed long run equilibrium relation with selected sample macroeconomic.
Bankex index and Interest rate has causal unidirectional relation with Exchange rate.
Naik and Padhi (2012) studies association between the Indian stock market index (BSE
Sensex) and various macroeconomic variables as industrial production index, wholesale
price index, money supply, treasury bills rates and exchange rates from the time period
1994 to 2011. The analysis reveals that macroeconomic variables and the stock market
index are co-integrated and, hence, a long-run equilibrium relationship exists between
them. This study perceived that the stock prices are positively relate to the money supply
and industrial production but negatively relate to inflation. The exchange rate as well as
short-term interest rate is found to be insignificant in determining stock prices. There is
bidirectional causation exists between industrial production and stock prices but
unidirectional causation from money supply to stock price, stock price to inflation and
interest rates to stock prices is established.
Ray (2013) examined the relationship between macroeconomic variables and stock prices.
The Industrial production presents a measure of overall economic activity in a country and
moves stock prices through its influence on expected future cash flows. Thus, it is expected
that an increase in industrial production index is positively related to stock price. The causal
relationship between industrial production and stock price in India is covered for a period,
1990-91 to 2010-11. The findings specified that there exist no significant causal relationship
between industrial production and share price in India. The result of regression, of course,
suggests that there may have been positive relation between stock price and real industrial
production. The increase in production of industry can enhance stock price and vice versa.
Sireesha (2013) examined the impact of macroeconomic factors upon the movements of
the Indian stock market index Nifty, gold and silver prices through linear regression
technique. Gold returns, Silver returns are selected for the analysis as they are important
now a days and are studied along with the stock returns. The performance of internal
variables shows the interdependence between these variable with returns on stock, gold
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and silver. Stock return is significantly influenced by GDP and inflation while gold return is
significantly influenced by money supply. External variables show significant impact on
dependent variables.
Research Gap:
In the Indian context, most of the studies conducted have been on Stock Market or BSE as a
whole. Very few studies have been conducted on a sectorial basis and specially focusing the
Bankex.All the studies have been confined only to the market level. The effects of variables
are not assessed on the companies, thus leaving out a vital piece of information. It is highly
anticipated that the Inflation Rate has a negative impact on the Indian bank stock market,
but in fact does it have any negative impact or does it provoke the bank stock market
because of grow up of the economy.
The purpose of this study is to investigate the casual relationship between inflation and
Bank stock returns, and exchange rate with bank stock returns Pre Post and during the crisis
period to get a detailed idea. The study is not just confined to the industry (Bankex), the
study also analyses the effect of the chosen variables on a firm level as well.
Objectives of the Study:
1. To examine the impact of exchange rate on Bankex and selected banks under
Bankex.
2. To examine the impact of inflation on Bankex and selected banks under Bankex.
3. To study the causal relationship between exchange rate and Bankex and banks under
Bankex.
4. To study the causal relationship between inflation and Bankex and banks under
Bankex.
Methodology of the study:
a) Sample Selection:
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The study mainly focuses on the WPI inflation index as a proxy for inflation rate and rupee-
dollar Exchange rates. Monthly data of both these variables were collected from the RBI
reference rate archive and the database for Indian economy.
b) Period of Study:
The period of study has been chosen as 1st January 2005 to 31st December 2014. The study
period has been sub divided bases on the price movements in BSE SENSEX into three sub-
periods namely;
i) Pre-crisis period: 1st January 2005 – 31st December 2007
ii) Crisis Period: 1st January 2008 – 31st July 2009
iii) Post Crisis period: 1st August 2009 – 31st December 2014.
c) Research tools:
The daily returns for the individual series are calculated based on the logged difference
as below:
Rit = [Ln (Pit) – Ln (Pit-1)] ………………..Equation…..(1)
Initially, correlation coefficients have been computed of the log values of the return
series of individual stock return of different companies with INR/USD and Crude Oil. It
has been observed that there exist correlations amongst different variables during the
sample period. However, such results do not necessarily imply, true existence of such
dependency as in many cases they may yield spurious results. Accordingly, further
investigation is necessary to establish the inferences drawn from the correlation results.
Descriptive Statistics of the Raw Return, especiallyMean, Standard Deviation has been
calculated.
1. Stationarity Test:
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Regressing non-stationary variables on each other leads to potentially misleading
inferences about the estimated parameters and the degree of association.
Therefore, before beginning of any testing, the order of integration of price series
must be determined. To identify whether the series are I(1), the augmented Dickey-
Fuller (ADF) test (Dickey and Fuller, 1979) has been employed, which involves
estimating the equation.
𝑦𝑡 = 𝛼 + 𝛽𝑡 + 𝛽𝑦𝑡−1 + 𝜃𝑗∆𝑦𝑡−𝑗 + 𝜀𝑡
𝑘
𝑗=1
……………… Equation…..(2)
Where is a constant, the coefficient on a time trend and K the lag order of the
autoregressive processes.
The null hypothesis of the Augmented Dickey-Fuller t-test is
H0:θ = 0 (i.e. the data needs to be differenced to make it stationary)
Versus the alternative hypothesis of
H1: θ < 0 (i.e. the data is stationary and doesn’t need to be differenced)
The Phillips–Perron test is a unit root test. That is, it is used in time series analysis to
test the null hypothesis that a time series is integrated of order 1. It builds on
the Dickey–Fuller test
Null hypothesis in ,
……………equation (3)…
Where is the first difference operator.
Like the augmented Dickey–Fuller test, the Phillips–Perron test addresses the issue
that the process generating data for might have a higher order of autocorrelation
than is admitted in the test equation—making endogenous.
Whilst the augmented Dickey–Fuller test addresses this issue by introducing lags
of as regressors in the test equation, the Phillips–Perron test makes a non-
parametric correction to the t-test statistic.
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1. Regression Model:The study applied the following regression model to find out the
impact of both Exchange Rate and Inflation movement on Stock Return of individual
firm.
𝑅𝑖𝑡 = 𝛽𝑜𝑖 + 𝛽𝐸𝑅 𝑖𝑅𝐸𝑅 + 𝛽𝑖𝑛𝑓 𝑖𝑅𝑖𝑛𝑓 𝑡 + 𝛽𝑚𝑘𝑡 𝑖𝑅𝑚𝑘𝑡 𝑡 + 𝜀𝑖𝑡
…………………..Equation…..(4)
where, i=1,...,13 and t=1,...,120, where the coefficients βmi , βER i and βinf i represent a
measure of sensitivity of Stock Return, i, to Market Risk, Exchange Risk and Inflation risk; εit
is the disturbance term. The introduction of Market Returns, Rmkt, as a third independent
variable, explicitly controls market movements, thereby reducing any correlation between
disturbances. The potential problem of multicollinearity may arise in estimating such a three
factor model from the possibility that the market, Exchange Rate and inflation factors are
correlated. In order to control this problem,Return of Market Portfolio is regressed on the
changes in the Exchange Rate Return and Inflation Return as shown by Equation (5),
𝑅𝑚𝑘𝑡 = 𝛾0 + 𝛾1𝑅𝐸𝑅 𝑡 + 𝛾2𝑅𝑖𝑛𝑓 𝑡 + 𝜀𝑡
…………………..Equation…..(5)
Then, the component of the Market Portfolio Return that is orthogonal to the changes in
the Exchange Rate Return and Inflation Return is obtained by calculating
𝐹𝑚𝑘𝑡 = 𝑅𝑚𝑘𝑡 − ( 𝛾0 + 𝛾1𝑅𝐸𝑅 𝑡 + 𝛾2𝑅𝑖𝑛𝑓 𝑡)
…………………..Equation…..(6)
Finally, firms’ Inflation and Exchange Rate exposure is estimated by regressing firms’ Stock
Market Returns on the changes in the Inflation Rate and Exchange Rate and orthogonal
component of the Market Portfolio, as illustrated by Equation
𝑅𝑖𝑡 = 𝛽0𝑖 + 𝛽𝐸𝑅 𝑖𝑅𝐸𝑅 𝑡 + 𝛽𝑖𝑛𝑓 𝑖𝑅𝑖𝑛𝑓 𝑡 + 𝛽𝑚𝑖𝐹𝑚𝑘𝑡 + 𝜀𝑖𝑡
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…………………..Equation…..(7)
Where R it is the Stock Return of firm i, Fmktis the estimated orthogonal component of the
Market Portfolio (BSE SENSEX), and R ER tis the percentage change in the Exchange Rate
index (INR/USD) over the same period and Rinf t is the percentage change in the Inflation
over the same period. The value obtained for β ER i for the different firms can be
interpreted as the level of exposure to Foreign Exchange Rates, since it indicates the
sensitivity that a stock shows towards these fluctuations. Similarly, the value obtained for β
inf i for the different firms can be interpreted as the level of exposure to Inflation rates,
since it indicates the sensitivity that a stock shows towards these fluctuations. The above
regression model is used to examine the levels of exposure to Foreign Exchange Rate
changes and inflation rate changes that should be reflected in the statistical significance of
the coefficient β ER i and β inf i (two-tailed test) and the direction of such exposure, which is
indicated by the sign that accompanies the coefficient. A positive coefficient (β ER i) means
that Stock Return increases when the Indian Rupee is depreciated against USD. Similarly, a
positive coefficient (β inf i) means that Stock Return increases when the inflation is
increased and vice versa.
2. Granger causality Test
Granger causality can identify whether two variables move one after the other or
contemporaneously. When they move contemporaneously, one provides no information for
characterizing the other. In simple words, the basic premise of the granger’s is that future
cannot cause the present or the past. In our analysis we use the test to find out whether the
exchange rate return series and stock return series data, precede each other or
contemporaneous. The testing is done, based on the following equation.
…………………..Equation…..(8)
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Where yt and xt are two time series and k is the lag length chosen according to the
suitability. If βi is zero, with lag length (i=1, 2,….k), xt fails to granger cause yt. However, the
Granger causality test is applicable to the stationary series only. Testing non-stationary
series with respect to another non-stationary series can generate misleading results, wholly
spurious for drawing inferences.
Empirical analysis and results:
1. Results of the unit root test:
The unit root test is applied to test the stationarity of the data. There exist several test to
test the presence of unit root in the series among them, the most commonly used in the
literature is the Augmented Dickey-Fuller (ADF) test and Phillip Perron test to analyse
stationarity in the time series. The application of unit root test is initial step before
proceeding to the Granger’s causality test. Following are the results of the test:
All the selected variables were non-stationary at price level but they are stationary at 1st
difference or return.
Table 1: Pre Crisis period: Null Hypothesis: Stationarity Test
Variables ADF Lag PP Bandwidth
Bankex -6.06385* 0 -6.6281* 9
Axis Bank -5.75176* 0 -6.2054* 8
Bank of India -3.95426* 1 -7.23435* 10
Bank of Baroda -6.17414* 0 -6.25848* 9
Canara Bank -4.5984* 0 -4.436* 9
Federal Bank -6.51382* 0 -7.57136* 8
HDFC Bank -6.26095* 0 -7.12* 12
ICICI Bank -7.07775* 0 -7.32149* 5
IndusInd Bank -4.23765* 0 -4.08524* 13
Kotak Bank -6.142* 0 -6.14194* 4
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PNB -6.08065* 0 -7.75568* 15
SBI -5.26536* 0 -5.29976* 7
YES Bank -5.26536* 0 -4.69569* 7
*Significant at 5% level
At the price level we find that all the data of share prices of the different companies are
non-stationary. Only in the return series the data are found to be stationary and significant
at 5% level which has been shown in table 1.
Table 2: Crisis Period: Stationarity Test
Variables ADF Lag PP Bandwidth
Bankex -3.37679* 0 -3.32306* 4
Axis Bank -3.43995* 0 -3.42045* 3
Bank of India -5.25873* 0 -5.85739* 7
Bank of Baroda -3.47216* 0 -3.41958* 5
Canara Bank -3.63811* 0 -3.61132* 4
Federal Bank -3.5342* 1 -3.44476* 8
HDFC Bank -3.31543* 0 -3.22161* 9
ICICI Bank -3.17631* 0 -3.13732* 5
IndusInd Bank -3.07038* 0 -3.05794* 2
Kotak Bank -3.35771* 0 -3.28663* 6
PNB -3.64478* 0 -3.59889* 4
SBI -4.22776* 0 -4.23449* 2
YES Bank -2.85434** 0 -2.77389** 3
*Significant at 5% level, **Significant at 10%level.
Table 2 shows the results of ADF test and the PP test for crisis period. The twelve selected
banks stock price is found to be non-stationary as the null hypothesis is accepted at 5 per
cent level of significance for both the tests. But the stock returns series of all these twelve
banks are found to be stationary as the null hypothesis is rejected at 5% level of significance
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for both the tests. Yes bank return from stock was foundto stationary at 10% level of
significance.
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Table 3: Post crisis period: Stationarity Test
Variables ADF Lag lent PP Bandwidth
Bankex -7.678363* 0 -7.746529* 5
Axis Bank -8.077296* 0 -8.344861* 6
Bank of Baroda -7.285793* 0 -7.253365* 3
Bank of India -7.960556* 0 -8.029899* 4
Canara Bank -6.770507* 0 -6.688906* 4
Federal Bank -8.092419* 0 -8.150751* 4
HDFC Bank -7.985942* 0 -7.985943* 1
ICICI Bank -4.254569* 0 -3.554272* 2
IndusInd Bank -7.208212* 1 -7.786377* 11
Kotak Bank -8.369662* 0 -8.37374* 1
PNB -4.672352* 0 -4.672352* 0
SBI -8.095897* 0 -8.095897* 0
YES Bank -7.052061* 0 -7.072245* 10
*Significant at 5% level.
Table 3 shows the results of ADF test and the PP test for post-crisis period. The twelve
selected banks stock price is found to be non-stationary as the null hypothesis is rejected at
5 per cent level of significance for both the test. But the stock returns series of all these
twelve banks are found to be stationary as the null hypothesis is rejected at 5% level of
significance for both the tests.
2. Correlation Analysis:
Table 4: Correlation between Bankex, exchange rate and inflation
Variable Pre- Crisis Crisis Post- Crisis
Bankex
Exchange rate
Inflation Exchange rate
Inflation Exchange rate
Inflation
-0.419368 -0.270699 - 0.308732 -0.230831 -0.699770 0.138696
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Table 4 gives the correlation between Bankex, exchange rate and inflation for three
different periods. There is a statistically significant negative correlation between the
industry (Bankex) and the variables (Exchange rate, Inflation), i.e. if Bankex goes up
exchange rate and inflation goes down and vice versa except in case of the relationship
between Bankex and inflation in the post crisis period which is positive. This means that if
the inflation rate increases the Bankex movement is upward rising and if inflation rate is
decreasing the Bankex also shows a downward trend.
Table 5: Pre-Crisis period:Correlation between Banks, exchange rate and inflation
Variables Exchange Rate Inflation
Axis -0.465223 -0.3349
Bank of India -0.449336 -0.0407
Bank of Baroda -0.003012 -0.1861
Canara Bank -0.443582 -0.1371
Federal Bank -0.328799 -0.4051
HDFC Bank -0.553558 -0.1469
ICICI Bank -0.408343 -0.0346
IndusInd Bank -0.212668 -0.2737
Kotak Bank -0.259976 0.19327
PNB -0.207201 -0.0388
SBI -0.315344 -0.2529
Yes Bank -0.33814 -0.4192
Table 5 shows the results of correlation among the companies listed in Bankex and the
variables exchange rate and inflation in the pre-crisis period. The results show a statistically
significant negative correlation between the companies and the variables (Exchange rate,
Inflation), i.e. if company return goes up exchange rate and inflation goes down and if
exchange rate and inflation goes up the company return falls.
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Table 6:Crisis period: Correlation between Banks, exchange rate and inflation
Variables Exchange Rate Inflation
Axis -0.30554 -0.22546
Bank of india -0.006546 -0.11905
Bank of Baroda -0.141499 -0.2855
Canara Bank -0.45765 -0.1814
Federal Bank -0.352823 -0.31347
HDFC Bnak -0.34425 -0.28401
ICICI Bank -0.394559 -0.17239
IndusInd Bank -0.307203 -0.21048
Kotak Bank -0.29016 -0.24665
PNB -0.184498 -0.27442
SBI -0.080017 -0.19467
Yes Bank -0.288472 -0.19057
Table 6 shows the results of correlation among the companies listed in Bankex and the
variables exchange rate and inflation in the crisis period. The results show a statistically
significant negative correlation between the companies and the variables (Exchange rate,
Inflation), i.e. if company return goes up exchange rate and inflation goes down and if
exchange rate and inflation goes up the company return falls.
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Table 7: Post Crisis period: Correlation between Banks, exchange rate and inflation
Variables Exchange rate Inflation
Axis -0.366663 0.146675
Bank of india -0.554823 0.154169
Bank of Baroda -0.583572 0.157891
Canara Bank -0.569033 0.281551
Federal Bank -0.191376 0.196974
HDFC Bnak -0.103353 0.067268
ICICI Bank -0.341957 0.178914
IndusInd Bank -0.519509 -0.05992
Kotak Bank -0.04461 -0.00529
PNB -0.352015 0.213284
SBI -0.249802 0.215123
Yes Bank -0.658487 0.146298
Table 7 shows the results of correlation among the companies listed in Bankex and the
variables exchange rate and inflation in the post-crisis period. The results show a statistically
significant negative correlation between the companies and Exchange rate i.e. if company
return goes up exchange rate and inflation goes down and if exchange rate and inflation
goes up the company return falls. Whereas the correlation between company returns and
inflation is positive, i.e. if company returns increases inflation also increases except in case
of IndusInd Bank and Kotak Mahindra Bank.
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3. Descriptive Statistics:
Table 8: Pre crisis period: Descriptive Statistics of Bankex
Variables Mean Std. Dev.
PREBANKEX 0.031137 0.080685
Table 9: Pre crisis period: Descriptive Statistics of Banks, Exchange Rate and Inflation
Variables Mean Std. Dev.
PREAXIS 0.045913 0.094794
PREBANKOFINDIA 0.037718 0.127206
PREBARODA 0.02306 0.109629
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Table 8 and table 9 reports the descriptive Statistics for the monthly returns of Bankex and
the selected banks respectively for the period of 1st January 2005 to 31st December 2007 i.e.
pre-crisis period. The mean return was highest for Axis bank at 0.045913 it has a deviation
of 0.094794 which indicates that although it has highest growth in this period it has very low
risk. The maximum deviation is for Kotak Mahindra Bank during the sample period with a
mean of 0.042143 meaning although Kotak Mahindra Bank had high risk it had high growth
also.
Table 10: Crisis Period:Descriptive Statistics of Bankex
Variables Mean Std. Dev.
CRISIS BANKEX -0.015745 0.171925
Table 11: Crisis Period:Descriptive Statistics of Banks, Exchange Rate and Inflation
PRECANARA 0.012385 0.109365
PREFEDERAL 0.019818 0.093701
PREHDFC 0.033416 0.078419
PREICICI 0.033366 0.098299
PREINDUSIND 0.020727 0.151625
PREKOTAK 0.042143 0.171893
PREPNB 0.013734 0.101741
PRESBI 0.035843 0.098654
PREYES 0.045173 0.077669
PREEXCHNGE -0.002586 0.015128
PREINFLATION -0.003993 0.136308
Variables Mean Std. Dev.
CRISISAXIS -0.00276 0.207058
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Table 10 and table 11, reports the descriptive Statistics for the monthly returns of the
Bankex and selected banks respectively for the period of 1st January 2008 to 31st July 2009
i.e. the crisis period. The mean rate of return was highest for PNB at 0.002055 and a
deviation of 0.163928. This is an example of low growth and low risk, whereas the all the
companies had negative growth. The maximum deviation is for Yes Bank at 0.256264 during
the sample period with a mean return of -0.023404 this goes with the trend during the crisis
period of negative growth and high deviation.
Table 12: Post Crisis Period: Descriptive Statistics of Bankex
Variables Mean Std. Dev.
PostBankex 0.014309 0.079847
Table 13:Post Crisis Period:Descriptive Statistics of Banks, Exchange Rate and Inflation
CRISISBANKOFINDIA -0.005426 0.183866
CRISISBARODA -0.002774 0.176669
CRISISCANARA -0.00795 0.143436
CRISISFEDERAL -0.017657 0.189927
CRISISHDFC -0.007455 0.136878
CRISISICICI -0.025508 0.210998
CRISISINDUSIND -0.020422 0.225529
CRISISKOTAK -0.036013 0.255737
CRISISPNB 0.002055 0.163928
CRISISSBI -0.014094 0.179914
CRISISYESBANK -0.023404 0.256264
CRISISEXCHNG 0.010192 0.057801
CRISISINFLATION -0.079777 0.274147
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Variables Mean Std. Dev.
POSTAXIS -0.00928 0.226703
POSTBANKOFINDIA -0.001366 0.134018
POSTBARODA 0.014019 0.096752
POSTCANARA 0.006959 0.130576
POSTFEDERAL -0.00708 0.185681
POSTHDFC -0.006991 0.214935
POSTICICI -0.011778 0.223632
POSTINDUSIND 0.034236 0.095712
POSTKOTAK 0.010129 0.099451
POSTPNB -0.017666 0.224538
POSTSBI -0.027089 0.2817
POSTYES 0.024263 0.127129
POSTEXCHNG 0.004248 0.028885
POSTINFLTN 0.021061 0.363211
Table 12 and table 13, reports the descriptive Statistics for the monthly returns of the
Bankex and selected banks respectively for the period of 1st August 2009 to 31st December
2014 i.e. the post crisis period. The mean rate of return was highest for IndusInd Bank at
0.034236 with a deviation of 0.095712 which shows low growth and low risk when
compared to other companies who have either low or negative growth with high risk. The
maximum deviation is for SBI at 0.2817 with a mean of -0.027089 which indicates negative
growth and high risk.
4. Regression analysis:
Table 14: Regression Analysis of Bankex for three different periods
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Period Exchange rate Inflation
Bankex
pre -2.153986* -0.14512*
crisis -0.897783* -0.13887*
post -1.916664* 0.016938
*significant at 5% level
Table 14 infers that during the sample period Bankex is exposed to variation in Exchange
and Inflation rates. The coefficients are negative and statistically significant. This implies
that there is a negative effect on the Bankex returns if the rupee depreciates against US
dollar or the inflation rate rises. Only during the post crisis period inflation shows no
negative impact on Bankex, but the positive return is not statistically significant.
Table 15: Pre-Crisis: Regression Analysis of Banks
Variables Exchange Rate Inflation
Axis Bank -2.451739* -0.258539
Bank of India -2.976269* -0.205515*
Bank of Baroda -0.198134 -0.01529
Canara Bank -2.622114* -0.160388
Federal Bank -1.651239* -0.278168*
HDFC Bank -2.572272* -0.067572
ICICI Bank -2.467884* -0.088632
IndusInd Bank -1.874138 -0.2355
Kotak Bank -2.62146 0.053073
Punjab National Bank -1.055668 -0.140559
State Bank of India -1.644271 -0.261896*
Yes Bank -1.335145 -0.251114*
*significant at 5% level
Based on table 15 out of the 12 companies taken in the pre-crisis period 6 companies along
with Bankex are exposed to rupee dollar exchange rate and 5 companies are exposed to
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variation in inflation rates. The regression coefficients between stock prices and exchange
rate are negative and statistically significant implies that depreciation in rupee against dollar
has a negative impact of stock prices. The regression coefficients between stock prices and
inflation are negative and statistically significant except that of Kotak bank suggests that
increase of inflation has a negative impact on stock prices.
Table 16: Crisis: Regression Analysis of Banks
Variables Exchange Rate Inflation
Axis Bank -1.0704 -0.163258
Bank of India -1.0704 -0.163258
Bank of Baroda -0.405705 -0.181324
Canara Bank -1.122742* -0.087537
Federal Bank -1.128345* -0.209759*
HDFC Bank -0.795044* -0.136582*
ICICI Bank -1.422073* -0.123345
IndusInd Bank -1.174206* -0.16544
Kotak Bank -1.251015* -0.221871*
Punjab National Bank -0.499494* -0.160809
State Bank of India -0.230417* -0.126243
Yes Bank -1.253855* -0.169905
*Significant at 5% level
Table 16 indicates that during the crisis period 7 companies along with the Bankex is
exposed to rupee dollar exchange rate and 3 companies is affected by inflation variation.
The regression coefficients between stock prices and exchange rate are negative and
statistically significant implies that depreciation in rupee against dollar has a negative
impact of stock prices. The regression coefficients between stock prices and inflation are
negative and statistically significant suggests that increase of inflation has a negative impact
on stock prices.
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Table 17: Post Crisis: Regression Analysis of Banks
Variables Exchange Rate Inflation
Axis Bank -2.887774* 0.002784
Bank of India -2.562367* 0.020677
Bank of Baroda -1.938169* 0.014625
Canara Bank -2.528289* 0.054136
Federal Bank -1.213568 0.01773
HDFC Bank -0.741163 0.025901
ICICI Bank -2.36397* 0.014917
IndusInd Bank -1.771633* -0.037394
Kotak Bank -0.167228 -0.005951
Punjab National Bank -2.444066* 0.025404
State Bank of India -2.416907* 0.019815
Yes Bank -2.911097* 0.001427
*significant at 5% level
According to table 17 during the post-crisis period 9 companies is exposed to variation in
exchange rate and no companies with the Bankex is exposed to inflation variation. The
regression coefficients between stock prices and exchange rate are negative and statistically
significant implies that depreciation in rupee against dollar has a negative impact of stock
prices. Inflation variation has a positive impact on stock prices of all companies except that
of IndusInd Bank.
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5. Granger Causality Test.
Table 18: Pre-Crisis: Granger Causality Test
Null Hypothesis: F-Statistic Prob.
PREBANKEX does not Granger Cause PREEXCHNGE 0.28614 0.7533
PREEXCHNGE does not Granger Cause PREBANKEX 0.592 0.5598
PREAXIS does not Granger Cause PREEXCHNGE 0.14062 0.8694
PREEXCHNGE does not Granger Cause PREAXIS 1.35677 0.2734
PREBANKOFINDIA does not Granger Cause PREEXCHNGE 0.11179 0.8946
PREEXCHNGE does not Granger Cause PREBANKOFINDIA 1.11409 0.3419
PREBARODA does not Granger Cause PREEXCHNGE 0.02114 0.9791
PREEXCHNGE does not Granger Cause PREBARODA 0.10199 0.9034
PRECANARA does not Granger Cause PREEXCHNGE 0.94318 0.401
PREEXCHNGE does not Granger Cause PRECANARA 0.18713 0.8303
PREFEDERAL does not Granger Cause PREEXCHNGE 0.09142 0.9129
PREEXCHNGE does not Granger Cause PREFEDERAL 0.8074 0.4558
PREHDFC does not Granger Cause PREEXCHNGE 1.06362 0.3583
PREEXCHNGE does not Granger Cause PREHDFC 0.02668 0.9737
PREICICI does not Granger Cause PREEXCHNGE 0.39121 0.6798
PREEXCHNGE does not Granger Cause PREICICI 1.73747 0.1938
PREINDUSIND does not Granger Cause PREEXCHNGE 0.36054 0.7004
PREEXCHNGE does not Granger Cause PREINDUSIND 1.0101 0.3766
PREKOTAK does not Granger Cause PREEXCHNGE 0.72988 0.4906
PREEXCHNGE does not Granger Cause PREKOTAK 0.28847 0.7515
PREPNB does not Granger Cause PREEXCHNGE 0.7529 0.48
PREEXCHNGE does not Granger Cause PREPNB 0.09645 0.9083
PRESBI does not Granger Cause PREEXCHNGE 0.6107 0.5498
PREEXCHNGE does not Granger Cause PRESBI 1.52842 0.2338
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PREYES does not Granger Cause PREEXCHNGE 0.74067 0.4883
PREEXCHNGE does not Granger Cause PREYES 2.99152 0.0709
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Null Hypothesis: F-Statistic Prob.
PREBANKEX does not Granger Cause PREINFLATION 0.71427 0.498
PREINFLATION does not Granger Cause PREBANKEX 2.30866 0.1174
PREAXIS does not Granger Cause PREINFLATION 0.45886 0.6365
PREINFLATION does not Granger Cause PREAXIS 1.40118 0.2625
PREBANKOFINDIA does not Granger Cause PREINFLATION 0.49313 0.6157
PREINFLATION does not Granger Cause PREBANKOFINDIA 0.96833 0.3917
PREBARODA does not Granger Cause PREINFLATION 0.33516 0.718
PREINFLATION does not Granger Cause PREBARODA 0.27566 0.761
PRECANARA does not Granger Cause PREINFLATION 1.85038 0.1753
PREINFLATION does not Granger Cause PRECANARA 0.6686 0.5201
PREFEDERAL does not Granger Cause PREINFLATION 0.45732 0.6375
PREINFLATION does not Granger Cause PREFEDERAL 0.51372 0.6036
PREHDFC does not Granger Cause PREINFLATION 0.62112 0.5443
PREINFLATION does not Granger Cause PREHDFC 2.77853 0.0787
PREICICI does not Granger Cause PREINFLATION 0.35671 0.703
PREINFLATION does not Granger Cause PREICICI 2.79856 0.0774
PREINDUSIND does not Granger Cause PREINFLATION 0.1203 0.8871
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PREINFLATION does not Granger Cause PREINDUSIND 0.30313 0.7408
PREKOTAK does not Granger Cause PREINFLATION 0.23616 0.7912
PREINFLATION does not Granger Cause PREKOTAK 0.18252 0.8341
Null Hypothesis: F-Statistic Prob.
PREPNB does not Granger Cause PREINFLATION 0.46499 0.6327
PREINFLATION does not Granger Cause PREPNB 0.26305 0.7705
PRESBI does not Granger Cause PREINFLATION 1.28015 0.2932
PREINFLATION does not Granger Cause PRESBI 0.90142 0.4171
PREYES does not Granger Cause PREINFLATION 0.17403 0.8414
PREINFLATION does not Granger Cause PREYES 0.25563 0.7767
*rejected at 5% level.
Table 18 shows that the variables have no significant impact on each other and there is no
lead lag effect on the variables i.e. they move contemporaneously.
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Table 19: Crisis Period: Granger Causality Test
Null Hypothesis: F-Statistic Prob.
CRISISBANKEX does not Granger Cause CRISISEXCHNG 5.08698 0.0251*
CRISISEXCHNG does not Granger Cause CRISISBANKEX 0.23348 0.7953
MRKTRSDL does not Granger Cause CRISISEXCHNG 3.21316 0.0763
CRISISEXCHNG does not Granger Cause MRKTRSDL 0.45574 0.6445
CRISISAXIS does not Granger Cause CRISISEXCHNG 3.10715 0.0818
CRISISEXCHNG does not Granger Cause CRISISAXIS 0.18834 0.8307
CRISISBANKOFINDIA does not Granger Cause CRISISEXCHNG 3.07436 0.0836
CRISISEXCHNG does not Granger Cause CRISISBANKOFINDIA 1.15794 0.3469
CRISISBARODA does not Granger Cause CRISISEXCHNG 6.93447 0.01*
CRISISEXCHNG does not Granger Cause CRISISBARODA 1.11999 0.3581
CRISISCANARA does not Granger Cause CRISISEXCHNG 4.60261 0.0328
CRISISEXCHNG does not Granger Cause CRISISCANARA 0.4696 0.6363
CRISISFEDERAL does not Granger Cause CRISISEXCHNG 3.56232 0.061
CRISISEXCHNG does not Granger Cause CRISISFEDERAL 1.64259 0.2341
CRISISHDFC does not Granger Cause CRISISEXCHNG 7.31125 0.0084*
CRISISEXCHNG does not Granger Cause CRISISHDFC 0.06018 0.9419
CRISISICICI does not Granger Cause CRISISEXCHNG 3.07807 0.0834
CRISISEXCHNG does not Granger Cause CRISISICICI 0.0342 0.9665
CRISISINDUSIND does not Granger Cause CRISISEXCHNG 4.95012 0.0271*
CRISISEXCHNG does not Granger Cause CRISISINDUSIND 0.25243 0.7809
CRISISKOTAK does not Granger Cause CRISISEXCHNG 4.51051 0.0346*
CRISISEXCHNG does not Granger Cause CRISISKOTAK 0.48553 0.627
CRISISPNB does not Granger Cause CRISISEXCHNG 5.87809 0.0166*
CRISISEXCHNG does not Granger Cause CRISISPNB 0.23675 0.7928
CRISISSBI does not Granger Cause CRISISEXCHNG 5.58266 0.0193*
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CRISISEXCHNG does not Granger Cause CRISISSBI 1.01083 0.3929
CRISISYESBANK does not Granger Cause CRISISEXCHNG 4.84854 0.0286*
CRISISEXCHNG does not Granger Cause CRISISYESBANK 0.99154 0.3995
CRISISBANKEX does not Granger Cause CRISISINFLATION 0.35145 0.7107
CRISISINFLATION does not Granger Cause CRISISBANKEX 2.2414 0.1489
Null Hypothesis: F-Statistic Prob.
CRISISAXIS does not Granger Cause CRISISINFLATION 0.7414 0.4971
CRISISINFLATION does not Granger Cause CRISISAXIS 1.25219 0.3207
CRISISBANKOFINDIA does not Granger Cause CRISISINFLATION 0.10479 0.9013
CRISISINFLATION does not Granger Cause CRISISBANKOFINDIA 1.802 0.2069
CRISISBARODA does not Granger Cause CRISISINFLATION 0.75796 0.4898
CRISISINFLATION does not Granger Cause CRISISBARODA 2.01423 0.1761
CRISISCANARA does not Granger Cause CRISISINFLATION 0.21342 0.8108
CRISISINFLATION does not Granger Cause CRISISCANARA 2.55471 0.119
CRISISFEDERAL does not Granger Cause CRISISINFLATION 0.83914 0.4559
CRISISINFLATION does not Granger Cause CRISISFEDERAL 2.46508 0.1268
CRISISHDFC does not Granger Cause CRISISINFLATION 0.06804 0.9346
CRISISINFLATION does not Granger Cause CRISISHDFC 1.71061 0.222
CRISISICICI does not Granger Cause CRISISINFLATION 0.92428 0.4233
CRISISINFLATION does not Granger Cause CRISISICICI 2.36077 0.1366
CRISISINDUSIND does not Granger Cause CRISISINFLATION 0.22555 0.8014
CRISISINFLATION does not Granger Cause CRISISINDUSIND 2.39481 0.1333
CRISISKOTAK does not Granger Cause CRISISINFLATION 0.07588 0.9274
CRISISINFLATION does not Granger Cause CRISISKOTAK 1.86852 0.1966
CRISISPNB does not Granger Cause CRISISINFLATION 0.56507 0.5827
CRISISINFLATION does not Granger Cause CRISISPNB 1.47826 0.2667
CRISISSBI does not Granger Cause CRISISINFLATION 0.16679 0.8483
CRISISINFLATION does not Granger Cause CRISISSBI 2.42667 0.1303
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CRISISYESBANK does not Granger Cause CRISISINFLATION 1.06902 0.3739
CRISISINFLATION does not Granger Cause CRISISYESBANK 1.71724 0.2209
*rejected at 5% level
In table 19, we see that in the crisis period all the Companies and Bankex have a
unidirectional relationship with exchange rate except ICICI Bank, Axis Bank and Bank of
India. This means that the Foreign Exchange dealings of these Banks have an impact on the
Exchange rate. Here all the firms take a lead as exchange rate lags. But the firms and
Inflation rate moves contemporaneously throughout the crisis period.
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Table 5.3: Post-Crisis period: Granger Causality Test
Null Hypothesis: F-Statistic Prob.
POSTBANKEX does not Granger Cause POSTEXCHNG 1.54946 0.221
POSTEXCHNG does not Granger Cause POSTBANKEX 0.26494 0.7682
POSTAXIS does not Granger Cause POSTEXCHNG 0.04917 0.9521
POSTEXCHNG does not Granger Cause POSTAXIS 0.3447 0.7099
POSTBANKOFINDIA does not Granger Cause POSTEXCHNG 0.12574 0.8821
POSTEXCHNG does not Granger Cause POSTBANKOFINDIA 0.43039 0.6523
POSTBARODA does not Granger Cause POSTEXCHNG 0.03852 0.9622
POSTEXCHNG does not Granger Cause POSTBARODA 0.66397 0.5187
POSTCANARA does not Granger Cause POSTEXCHNG 0.28499 0.7531
POSTEXCHNG does not Granger Cause POSTCANARA 0.50402 0.6067
POSTFEDERAL does not Granger Cause POSTEXCHNG 0.30901 0.7354
POSTEXCHNG does not Granger Cause POSTFEDERAL 3.14847 0.0503
POSTHDFC does not Granger Cause POSTEXCHNG 1.92311 0.1554
POSTEXCHNG does not Granger Cause POSTHDFC 0.11917 0.8879
POSTICICI does not Granger Cause POSTEXCHNG 1.49283 0.2332
POSTEXCHNG does not Granger Cause POSTICICI 1.28936 0.2832
POSTINDUSIND does not Granger Cause POSTEXCHNG 2.07966 0.1341
POSTEXCHNG does not Granger Cause POSTINDUSIND 1.66979 0.1972
POSTKOTAK does not Granger Cause POSTEXCHNG 0.2755 0.7602
POSTEXCHNG does not Granger Cause POSTKOTAK 0.12676 0.8812
POSTPNB does not Granger Cause POSTEXCHNG 0.48865 0.616
POSTEXCHNG does not Granger Cause POSTPNB 1.43108 0.2474
POSTSBI does not Granger Cause POSTEXCHNG 1.02344 0.3658
POSTEXCHNG does not Granger Cause POSTSBI 0.26855 0.7654
POSTYES does not Granger Cause POSTEXCHNG 1.53631 0.2238
POSTEXCHNG does not Granger Cause POSTYES 0.20462 0.8155
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POSTBANKEX does not Granger Cause POSTINFLTN 0.24035 0.7871
POSTINFLTN does not Granger Cause POSTBANKEX 1.85214 0.1662
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Null Hypothesis F-Statistic Prob.
POSTAXIS does not Granger Cause POSTINFLTN 3.29818 0.0441*
POSTINFLTN does not Granger Cause POSTAXIS 0.00046 0.9995
POSTBANKOFINDIA does not Granger Cause POSTINFLTN 0.99214 0.3771
POSTINFLTN does not Granger Cause POSTBANKOFINDIA 1.6066 0.2095
POSTBARODA does not Granger Cause POSTINFLTN 0.11377 0.8927
POSTINFLTN does not Granger Cause POSTBARODA 0.00381 0.9962
POSTCANARA does not Granger Cause POSTINFLTN 0.22867 0.7963
POSTINFLTN does not Granger Cause POSTCANARA 0.12805 0.8801
POSTFEDERAL does not Granger Cause POSTINFLTN 1.55029 0.221
POSTINFLTN does not Granger Cause POSTFEDERAL 0.20723 0.8134
POSTHDFC does not Granger Cause POSTINFLTN 0.32181 0.7261
POSTINFLTN does not Granger Cause POSTHDFC 0.12223 0.8852
POSTICICI does not Granger Cause POSTINFLTN 0.27355 0.7617
POSTINFLTN does not Granger Cause POSTICICI 2.14199 0.1268
POSTINDUSIND does not Granger Cause POSTINFLTN 0.34976 0.7064
POSTINFLTN does not Granger Cause POSTINDUSIND 0.09771 0.9071
POSTKOTAK does not Granger Cause POSTINFLTN 0.14961 0.8614
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POSTINFLTN does not Granger Cause POSTKOTAK 0.48877 0.6159
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Null Hypothesis Obs. F-Statistic Prob.
POSTPNB does not Granger Cause POSTINFLTN 62 0.10041 0.9046
POSTINFLTN does not Granger Cause POSTPNB 0.17741 0.8379
POSTSBI does not Granger Cause POSTINFLTN 62 0.98081 0.3812
POSTINFLTN does not Granger Cause POSTSBI 1.43316 0.247
POSTYES does not Granger Cause POSTINFLTN 62 0.39418 0.6761
POSTINFLTN does not Granger Cause POSTYES 0.50964 0.6034
*rejection at 5% level
In table 5.3 shows that variables has no significant effect on one another and hence no lead
lag effect exists except there is a unidirectional relationship among the stock prices of Axis
bank and inflation rate in which Axis Bank has a lead and inflation rate lags.
Discussions and Conclusion:
The present study has been initiated to explore the impact of Exchange rate and Inflation
rate on the stock prices of Bankex and the twelve companies listed under Bankex during the
study period from 1st January 2005 to 31st December 2014.
The study reveals that Exchange rate has a negative and statistically significant impact on
the Bankex and stock price of Axis Bank, Bank of India, Canara Bank, HDFC Bank, Federal
Bank and ICICI Bank during the pre-crisis period i.e. 50% of the samples taken under
consideration. This implies that as the Exchange rate increases, there will be a decline in the
stock prices of these companies. Although there is a negative impact on the other six
companies namely Bank of Baroda, IndusInd Bank, Punjab National Bank, State Bank of
India, Yes bank but they are statistically not significant.
Inflation on the other hand has a negative impact which is statistically significant on the
stocks of 33 % i.e. 4 out of 12 the banking companies.
The Granger Causality test shows that the variables move contemporaneously with each
other. In other words there is no lead lag relationship among the variables.
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The study of the samples during the crisis period reveals that 9 companies out of 12 i.e. 75%
of the companies and Bankex is exposed to exchange rate fluctuation. This means that the
depreciation of rupee will lead to the fall of the stock prices of these 9 companies. The
companies which are not significantly affected are Axis Bank, Bank of India, and Bank of
Baroda. During the same period Inflation significantly negatively affected just 3 out of the 12
companies i.e. just 25% of the sample.If the results of the Granger causality test are
interpreted it shows that there exists a lead lag relationship among exchange rate and the
firms. The exchange rate is affected by the movement of stocks of the company, which may
indicate that the exchange rate is affected by the workings of the bank during this period.
But inflation moves contemporaneously with the companies i.e. it shows no lead lag effect.
The analysis of the post crisis period sample shows that the exchange rate has an exposure
on 75% of the banking companies. This exposure is negative in nature. The companies which
are not statistically significantly impacted are Federal Bank, HDFC Bank and Kotak Bank.
Inflation had no statistically significant on any of the companies or the Bankex.The granger
causality test during the post crisis period reveals that none of the variables have a lead lag
effect amongst each other.
The findings of this study have some important implications. Exchange rate has significant
information to forecast Bankex and banking company performance. This is probably due to
the fact that all the Banking companies listed in Bankex engage in foreign exchange. This
gives the speculators and arbitrageurs the opportunity to estimate future stock prices and
take profitable decisions in their favour. This also raises the question that whether the
banking company managers are careful enough when dealing with dealing with foreign
exchange. As for inflation, it has a weak influence on most companies. Therefore, the study
shows that estimating the stock price from this variable is a weak estimate. And this also
might indicate that the banks are able to manage themselves well when it comes under
inflationary pressure.
Other researchers can develop this study by adding other variables such as Interest rate,
Repo rate, money supply etc. that is not included in this study and the effects of this
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variables on stock prices individually or collectively (Bankex) and how the new variables
affect each other. Also if we compare this study with a different crisis period and take into
consideration the same variables and the results are similar, it is quite possible a future
crisis period may be identified, keeping in mind the behaviour and movement of the
variables. But further studies are to be conducted on this issue.
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