Performance of IPO as Compared to CRISIL GRADE

8/8/2019 Performance of IPO as Compared to CRISIL GRADE

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PERFORMANCE OF IPO AS COMPARED TO CRISIL GRADE"

NAME DEEPAK BHATIA

BATCH FALL WINTER 07-09

ALUMNI ID DF-79-F -0357

EMAIL ID [email protected]

SUBMITTED TO:

MR. VIJAY BODDU

I
mailto:[email protected]:[email protected]


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Performance of IPO

ACKNOWLEDGEMENT

I would like to extend our sincere thanks to Mr.PARAMJIT SINGH, our project guide

for his guidance and support throughout our study. His calm demeanor and willingness to

teach has been a great help in our successfully completing the project. Our learning has

been immeasurable and working under him was a great experience.

My heartfelt thanks are also towards our guides of our Institute faculty. Without their

continuous help and enthusiasm the project would not have been materialized in the

present form.

Finally, I also wish to thank Indian Institute Of Planning And Management for

making this experience of this project possible. The learning from this experience has

been immense and would be cherished throughout my life.

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Performance of IPO

TO WHOM SO EVER IT MAY CONCERN

This is to certify that Mr. Deepak Bhatia, a student of Indian Institute Of

Planning And Management, New Delhi has completed his Thesis on

Performance of IPO as per Crisil grades under my guidance and all the

information in the thesis is authentic and acknowledged.

Paramjeet Singh

Head - Franchisee Business Unit (ROM)

IL & FS Investmart Securities Limited

33/15, Prashant Bunglow, Opp. Garware College,

Karve Road , Pune 411 004

Tel: 020-66020000 Mobile: 09320979016

Email :[email protected]

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mailto:[email protected]:[email protected]:[email protected]


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TABLE OF CONTENT

S. No. Particulars Page No.

1 Cover Page I

2 Acknowledgement II3 Executive Summary V

4 Synopsis VII

5 Introduction VIII

6 Abstract XI

7 CRISIL Background XIII

8 Financial Look at the topic XXI

9 Indian IPOs XIV

10 Empirical Findings XV11 Methodology XVI

12 Explaining the topic XVII

13 Outcomes XLVIII

14 Description of variables LI

15 Findings LIII

16 Conclusion LVII

17 Final Outcome LVIII

18 Annexure LX19 Bibliography LXII

Executive Summary

We propose that the long-run performance of IPOs is a function of pre-IPO factors,

including managerial decisions and the firms performance prior to going public and

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CRISILs contribution in evaluating and quantifying. We relate long-run performance to

a much richer set of explanatory factors than in the previous literature. Using a number of

variables, we provide empirical evidence in support of this proposition. The manner in

which a company is run before it is listed on the stock exchange gives a strong signal of

how its shares will perform in its first few years of coming to the market.

IPO grading is the grade assigned by a Credit Rating Agency registered with SEBI, to the

initial public offering (IPO) of equity shares or any other security which may be

converted into or exchanged with equity shares at a later date. The grade represents a

relative assessment of the fundamentals of that issue in relation to the other listed equity

securities in India. Such grading is generally assigned on a five-point point scale with a

higher score indicating stronger fundamentals and vice versa as below.

IPO grade 1: Poor fundamentals

IPO grade 2: Below-average fundamentals

IPO grade 3: Average fundamentals

IPO grade 4: Above-average fundamentals

IPO grade 5: Strong fundamentals

This paper attempts a detailed investigation of the boom and slump phases in the Indian

primary capital market. It concentrates on two key variables, namely, IPO volume and

initial returns and analyses their nature and interrelation during these two periods. This

study also analyses the firm-specific characteristics and their influence on the timing of a

company getting listed in the hot and cold market. The IPO volume series was auto

correlated over the entire period and especially during the boom period. This shows a

firm's decision to go public over the last decade depended on the number of other

companies that were getting listed over the previous months. Turning to the interrelation

of volume and initial return, the empirical exercise (Granger causality test) found no

significant relation between IPO volume and initial returns during the hot and cold

periods. This suggests that over the sample period, the Indian issuers' did not depend on

the information content of the initial returns while taking their decision to go public.

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Amongst the other characteristics that might have influenced the likelihood of IPOs

during hot and cold market (e.g. industry classification, age, size and under pricing of

new issues), this paper finds no significant influence of industry affiliation on the IPOs

during the boom period. The results also documented that more established firms have

greater likelihood to get listed on the capital market to raise large amounts and under

price more during the slump period.

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Synopsis

TITLE OF THESIS: Performance of IPOs as per CRISIL grades

WHY PERFORMANCE OF IPOs:

I propose that the long-run performance of IPOs is a function of pre-IPO factors,

including managerial decisions and the firms performance prior to going public. I wouldlike to relate long-run performance to a much richer set of explanatory factors than in the

previous literature. Using a number of variables, I will be providing a empirical evidence

in support of this proposition. The manner in which a company is run before it is listedon the stock exchange gives a strong signal of how its shares will perform in its first

few years of coming to the market.

IPO grading has been introduced as an endeavor to make additional information available

for the investors in order to facilitate their assessment of equity issues offered through anIPO.

Apart from that when a share is listed on the stock exchange what should be the

approximate first listed price of that share. I am trying to find and quantify those reasons

which decide the complete IPO journey. It includes little about IPO valuation thenprediction of listing price and then its long run performance.

I will explore the relationship between pre-IPO factors and its price performance in thelong-run believing that the pre-IPO performance of a firm taking a CRISIL grade as a

benchmark.

Research methodology..

In terms of selecting the research methodology, I have thought of analyzing differentIPOs which came during the last 2 years with a CRISIL grades. Our sample willinclude IPOs of three companies with three different grades.

Will various amount of allotment, different past performances and also in different

sectors to get an adequate coverage for the detailed study of the topic.

Primary ResearchData from companies.

Secondary researchSince the thesis is mainly based on secondary data only thus data required will be balance

sheet, profit and loss a/c and historical performances of other IPO's of the same company.

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Aim is to come out with some mathematical formula using various statistical tools toquantify the various parameters on which the complete performance cycle of theIPO depends from the IPO valuation to the first listing price and the long termperformance.

MethodologyThe paper will be organized as follows:

Section I will include the introduction

Section II will include the previous literature on long-run IPO under-performance and thevarious theoretical explanations for the anomaly.

Section III will describe the data

Section IV will include my methodology.

Section V will present the hypotheses I wish to test

Section VI will report my results.

Conclusions and recommendations for future research appear in section VII.

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Introduction

It has been empirically documented that the IPO market experiences cycles in terms of

volumes of new companies, which is referred to in the literature as hot and cold

periods. It is considered to be an empirical anomaly for which no unanimous explanation

is yet provided for. The most well known among the sighted explanations is technological

innovation or positive productivity shock that changes the prospects of IPOs from a

particular industry and an expectation of return associated with the CRISIL grade.

Empirical studies have found that small and young firms time their offers to use

investors optimism in their favor and get listed during the booming period may it be the

listing day or long term performance. There are evidences of high under pricing and

industry clustering during the hot periods, though their nature and extent have differed

from country to country.

The last decade is also important, since the Indian economy in general and primary

capital market, in particular, has undergone remarkable changes during this period. The

liberalization programmed initiated in 1992 along with other changes has enabled large

Foreign Direct Investment (FDI) and Foreign Institutional Investment (FII) inflows,

giving a big push to the capital market.

The abolition of the Controller of Capital Issues (CCI) also had a major impact on the

activities in the Indian primary market. It witnessed a boom phase (1993-96) when more

than 50 companies got listed every month. And after 2006 when it was made mandatory

for the companies to get their IPOs get graded from any Credit Rating Agency. This

paper attempts a detailed investigation of the boom and slump phases in the Indian

primary market.

This paper attempts a detailed investigation of the boom and slump phases in the Indian

primary capital market. It concentrates on two key variables, namely, IPO volume and

initial returns and analyses their nature and interrelation during these two periods.

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This study also analyses the firm-specific characteristics and their influence on the timing

of a company getting listed in the hot and cold market for the listing gain as well as for

the long term return as per the expectation associated with the different grades of

CRISIL. The IPO volume series was auto correlated over the entire period and especially

during the boom period. This shows a firm's decision to go public over the last decade

depended on the number of other companies that were getting listed over the previous

months. Turning to the interrelation of volume and initial return, the empirical exercise

found no significant relation between IPO volume and initial returns during the hot and

cold periods. This suggests that over the sample period, the Indian issuers' did not depend

on the information content of the initial returns while taking their decision to go public.

Amongst the other characteristics that might have influenced the likelihood of IPOs

during hot and cold market (e.g. industry classification, age, size and under pricing of

new issues), this paper finds no significant influence of industry affiliation on the IPOs

during the boom period. The results also documented that more established firms have

greater likelihood to get listed on the capital market to raise large amounts and under

price more during the slump period.

It concentrates on two key variables, namely IPO volume and initial returns series and

analyses their nature and interrelation during these two phases. This study also analyses

the firm-specific characteristics (i.e., age, industry type, size) and their influence on the

timing of a company getting listed during high volume period as compared to low volume

period. The remainder of the paper is organized as follows. This study contains a survey

of international literature, outlines the data sources for this study and then discusses the

methodology used and presents the results. Finally we summarize the main findings with

some concluding remarks.

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Abstract

We propose that the long-run as well as the listing performance of IPOs is a function of

pre-IPO factors, including managerial decisions and the firms performance prior to going

public. We relate long-run performance to a much richer set of explanatory factors than in

the previous literature. Using a number of variables, we provide empirical evidence in

support of this proposition. The manner in which a company is run before it is listed on

the stock exchange gives a strong signal of how its shares will perform in its first few

years of coming to the market. Using a UK data set, we find that the percentage of equity

issued and the degree of multinationality are key predictors of IPO performance.

It is well known that, on average, initial public offerings (IPOs) significantly

underperform the market over the long-run. Various explanations for this

underperformance (including the heterogeneous expectations of investors) have been

offered. In this paper, we focus on the evolution of financial analyst coverage and the

effect of the initiation of analyst coverage on the long-term performance of Initial Public

Offerings (IPOs). We find that IPOs followed by financial analysts significantly

outperform those that do not receive any coverage.

We also examine factors associated with financial analysts initiating coverage subsequent

to companies initial public offerings. Our results indicate that the original analyst

coverage decision is affected by: the extent of earnings history provided in the

prospectus; underwriter prestige; firm size; whether management included an earnings

forecast in the IPO prospectus and whether the post-IPO earnings indicate good news.

We also studied the initial and long-run performance of a unique sample of thrifts that

have recently converted from mutual to stock form. Because the market value of

converted thrifts equals the pre-conversion value plus all proceeds from the IPO, the

firms in our sample have a degree of under pricing automatically built into their offer

price. By simultaneously examining the initial return, long-run cumulative abnormal

returns, and returns over various post-issue sub periods, we are able to characterize

investor behavior in a way that is not possible with IPOs in other industries.

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We find that even after removing the large initial returns, cumulative excess returns for

the firms in our sample are significantly positive for up to five years post-issue. Excess

returns are significantly positive in all but one sub-period for five years post-issue. In

addition, these excess returns persist (as positive alphas) when performance is evaluated

using CAPM, 3-, 4- and 5-factor models. Our results are consistent with investor under

reaction in both the IPO and aftermarket. Thus, we find no evidence of investor

overreaction despite the large first-day returns and volume that would presumably attract

momentum investors.

We examine the effects of choice of underwriters, venture-capital support and industrial

classification, and the interactions of these effects on the long term performance of IPOs.

We find significant underwriter and venture capital effect and that underwriter effect

subsumes industry effect. Short term price momentum and long term price reversal

pattern is most pronounced for IPOs that are underwritten by leading investment banks

and backed by venture capital. The beginning of price reversal coincides with the

expiration of IPO lockup period.

Although by the end of the first year, IPOs on average underperforms the market,

investors can earn above market returns by investing in IPOs that are underwritten by

leading investment banks and backed by venture capitalists and divest before the

expiration of the lockup period.

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CRISIL BACKGROUND

A Standard & Poors Company:

CRISIL is Indias leading Ratings, Research, Risk and Policy Advisory company.

CRISILs majority shareholder is Standard & Poors, a division of The McGraw-Hill

Companies and the worlds foremost provider of independent credit ratings, indices, risk

evaluation, investment research and data.

CRISILs Grading of Initial Public Offerings (IPOs) is a service aimed at facilitating

assessment of equity issues offered to the public. The Grade assigned to any individual

IPO is a symbolic representation of CRISILs assessment of the fundamentals of the

issuer concerned on a relative grading scale. IPO Grades are assigned on a five-point

point scale, where IPO Grade 5 indicates the highest grading and IPO Grade 1 indicates

the lowest grading, i.e. a higher score indicates stronger fundamentals. An IPO Grade is

not an opinion on the price of the issue, pre- or post-listing.

The Grading Process:

CRISIL starts the IPO Grading process on receipt of a formal request from the issuer

company. CRISIL then sends a questionnaire seeking information on the companys

existing operations as well as proposed project(s). This is followed by site visits and

discussions with the key operating personnel of the company concerned. Apart from

officials of the company, CRISIL also meets its bankers, auditors, merchant bankers, and

appraising authority (if any). If the case so merits, CRISIL also obtains the views of

independent expert agencies on critical issues like , for instance, the technology proposed

to be used . Once all the required information has been obtained, CRISILs team of

analysts presents a detailed Grading Report to CRISILs Rating Committee which then

assigns the Grade.

Usually, the assignment of Grade takes three to four weeks after all the necessary

information has been provided to CRISIL. Once the Grade is assigned, the issuer

company is required to disclose the same and also publish it in the Red Herring

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Prospectus (RHP), which is filed with the Securities and Exchange Board of India (SEBI)

and other statutory authorities. CRISIL does not carry out any unsolicited Grading; the

process involves the full co-operation of, and interaction with, the issuer company

concerned.

IPO Grading are a one-time exercise, not subject to subsequent surveillance.

Grading Methodology:

The emphasis of the IPO Grading exercise is on evaluating the prospects of the industry

in which the company operates , the companys competitive strengths that would allow it

to address the risks inherent in the business(es) and effectively capitalise on the

opportunities available as well as the companys financial position. In case the IPO

proceeds are planned to be used to set up projects, either Greenfield or Brownfield,

CRISIL evaluates the risks inherent in such projects, the capacity of the companys

management to execute the same, and the likely benefits accruing from the successful

completion of the projects in terms of profitability and returns to shareholders. Due

weightage is given to the issuer companys management strengths and weaknesses and

issues, if any, from the corporate governance perspective. Accordingly, CRISILs IPO

Grading methodology examines the following key variables:

Business and Competitive Position

:: Industry prospects

Typical factors which are assessed here includes the growth prospects of the industry, the

extent of cyclicality, competitive intensity, vulnerability to technological changes and

regulatory risks inherent in the business .

:: Market position

A companys market position is indicated by its ability to increase/ protect market share,

command differential pricing and maintain margins at par with, or superior to its peers.

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Factors evaluated would include the sources of competitive advantages like brand equity,

distribution network , proximity to key markets and technological superiority.

:: Operating efficiency

The emphasis here is on evaluating the factors which could give rise to operational

efficiency, depending on the industry where the company is operating and typically

includes areas like access to raw material sources, superior technology, favourable cost

structure and so on.

New Projects Risks and Prospects

Key issues evaluated here are the companys ability to successfully execute the projectthat is being undertaken and the potential upside to the shareholders on completion and

commissioning of the project. CRISIL carries out a detailed risk assessment of the project

with respect to issues like availability of finances, technology tie-ups in place, ability to

execute the project without time or cost overrun , market risks arising from capacity

additions and the mitigates in place to counter those risks.

Financial Position and Prospects

Ability to generate sustained shareholders value as reflected by trends in profitability

margins , EPS growth, Return on Capital Employed (RoCE) and Return on Net Worth

(RoNW) are evaluated by CRISIL. While the absolute levels and the trends are important,

CRISIL also compares it with peers operating in the same industry to understand a

companys relative position. Complementing this is an analysis of the companys ability

to generate free cash flows in the long term. The capital structure of the company is

evaluated from a perspective of balance between the cost of capital for shareholders and

financial risks associated with higher leverage.

Management Quality

The assessment is designed to evaluate a companys management depth, the profile of its

key operating personnel, the adequacy of the organization structure and systems in place

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as well as the managements stated plans and policies towards earnings growth and

shareholder returns. CRISIL also evaluates t he managements approach towards risk and

long term business plans in place.

Corporate Governance practices

While IPO grading is not intended to be a detailed evaluation of a companys corporate

governance practices, broad issues like app arent quality of independent directors, quality

of accounting policies and type of transactions with subsidiaries and associates is looked

into.

Compliance and Litigation History

The IPO Grade assigned is the outcome of a detailed evaluation of each of the factors

listed, and is a comment on the fundamentals of the company concerned and its growth

prospects from a long -term perspective. The assessment involves combination of both

quantitative factors as reflected in financial numbers, market shares etc as well as

qualitative factors like risks associated with new projects, or the managements ability to

deliver on the promises made.

A CRISIL IPO Grade does not comment on the valuation or pricing of the issue that has

been graded, nor does it seek to indicate the likely returns to shareholders from

subscribing to the IPO.

CRISILs IPO Grading Scale

CRISILs five -point IPO Grading scale is as follows:

CRISIL IPO Grade 5: Strong fundamentals

CRISIL IPO Grade 4: Above-average fundamentals

CRISIL IPO Grade 3: Average fundamentals

CRISIL IPO Grade 2: Below-average fundamentals

CRISIL IPO Grade 1: Poor fundamentals

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What a CRISIL IPO Grade Is Not:

It is NOT a recommendation to buy sell or hold the securities Graded

It is NOT a comment on the valuation or pricing of the IPO Graded

It is NOT an indication of the likely listing price of the securities Graded

It is NOT a certificate of statutory compliance

Why is IPO grading necessary?

An investor in a hitherto unlisted company may either have limited access to information

on it, or may find it challenging to appropriately assess, on the basis of the information

available, its business prospects and risks. An IPO Grade provides an additional input to

investors, in arriving at an investment decision based on independent and objective

analysis.

In recent times, with the stock market participation of new and foreign investors

increasing, there is need for greater value-added information on companies tapping the

capital market and their intrinsic quality . In this context, IPO Grades, being simple,

objective indicators of the relative fundamental positions of the issuers concerned, could

help in both widening and deepening the market.

IPO Grading is NOT a recommendation to buy sell or hold the securities Graded.

Similarly, it is NOT a comment on the valuation or pricing of the IPO Graded nor is it an

indication of the likely listing price of the securities Graded.

What are the issues that are assessed while arriving at the grading?

The emphasis of the IPO Grading exercise is on evaluating the prospects of the industry

in which the company operates, its competitive strengths that would allow it to address

the risks inherent in the business(es) and effectively capitalize on the opportunities

available as well as the companys financial position.

In case the IPO proceeds are planned to be used to set up projects, either Greenfield or

Brownfield, rating agency evaluates the risks inherent in such projects, the capacity of the

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companys management to execute the same, and the likely benefits accruing from the

successful completion of the projects in terms of profitability and returns to shareholders.

Due weightage is given to the issuer companys management strengths and weaknesses

and issues, if any, from the corporate governance perspective.

Accordingly, IPO Grading methodology examines the following key variables:

Business and Competitive Position

New ProjectsRisks and Prospects

Financial Position and Prospects

Management Quality

Corporate Governance practices

Compliance and Litigation History

SEBI does not play any role in the assessment made by the grading agency. The

grading is intended to be an independent and unbiased opinion of a rating agency.

Validity of grading

The assigned grade would be a one time assessment done at the time of the IPO and

meant to aid investors who are interested in investing in the IPO. While the grading itself

is valid for a period of 6 months from the date of issuance, the grading letter will have a

validity of 2 months from the date of issue and would need to be revalidated

subsequently- there would not be any additional charges for the revalidation. Rating

agency however reserves the right to change the grading after the same has been assigned

should the circumstances so warrant.

Facilitating informed investment decisions

Continuing its tradition of pioneering and developing innovative products and services to

help its clients take better informed business decisions, CRISIL introduces its latest

product offering CRISIL IPO grading.

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CRISIL, the originator of this concept, has been at the forefront of developing the IPO

grading model into a usable form. The views and feedback of the regulator, market

participants, investors and investor forums have been core inputs in the development of

this product. Therefore, CRISIL has a uniquely evolved understanding of this globally

revolutionary idea.

CRISIL believes that IPO grading provided by an independent agency would be free from

bias and add structure to the tools available at present for assessing the investment

attractiveness of an equity security.

The IPO grading will be based on CRISILs proprietary framework that has been

developed to help investors arrive at their own judgment on factors those drive

equities as an asset class. The debt market has benefited immensely from the available of

such an assessment in the form of credit rating - a representation of a relative assessment

of the fundamentals of the debt security, likelihood of timely repayment of interest and

principal. The IPO grading product of CRISIL is a relative assessment of the

fundamentals of the equity security. Investment decisions for IPOs are at present based

on voluminous and complex disclosure documents, which pose a challenge to investors to

arrive at informed decisions. The focus, in these documents is meeting regulatory

guidelines on disclosures. Though seemingly there is a lot of information available on

IPOs through free research on websites, media and other sources, investors often look

for structured, consistent and unbiased analysis to aid their investment decisions.

Moreover, information available on new companies varies with the size of the issue, the

market conditions and the industry that the issuing company belongs to. CRISIL IPO

grading aims to bridge this gap and facilitate more informed investment decisions.

CRISIL IPO grading brings value to issuer, merchant banker and investors

It provides an independent, unbiased assessment of the fundamentals of the

company.

The grade enables easy comparison between companies, irrespective of the size or

the industry they operate in.

It is a collaborative initiative to widen and deepen market participation.

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Increasing participation from new and foreign investors necessitates greater

awareness about the company and its fundamentals.

It will help issuers to benchmark themselves and project their underlying strength

better.

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Financial Look: Long-Term Underperformance of New Issues?

Financial economists are still searching for appropriate answers to what explains the

long-term underperformance of new issues, both IPOs and SEOs. This section reviews

three major explanations proposed by academic research papers on this question.

A. The Windows of Opportunity Hypothesis or the Fads Hypothesis:

Given temporary overvaluation of a firm, two hypotheses have been proposed: (i) the

windows of opportunity hypothesis; and (ii) the fads hypothesis. Although they are

labeled with different titles, they are built on the same overvaluation phenomenon.

Depending on whose viewpoint we consider, it seems that two different titles have

evolved. From the managers viewpoint, propose the windows of opportunity

hypothesis which predicts that when a firm is substantially overvalued the managers are

likely to issue equity, taking advantage of the opportune time to lower the cost of capital.

Relying on the observation that equity issuing firms perform poorly following the issue

rather than a theoretical foundation justifying this hypothesis. From the viewpoint of

investors, that there are fads in the securities markets contrary to the assumption of the

rational expectations models in the literature and further that the IPO market overpricing

is a good case in point for the presence of fads. Using the market-to-book equity ratio as a

proxy for overvaluation, find that underperformance persists even after the market-to-

book equity ratio is controlled for, which is inconsistent with the hypothesis.

The size-adjusted long-term excess returns do not have any meaningful role in the log

regressions predicting whether a firm relies on equity or debt financing, which is also

inconsistent with the hypothesis.

On the average, it is true that IPO firms underperform the market or a matching control

group of non issuers, but the IPO literature often overlooks the fact that a large fraction of

new issues exhibit better performance than the matching control group of non issuers.

There is one exception that investigates long-term performance of over- and under-valued

IPOs.

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They find that overvalued IPOs become even more overvalued in the short-run exhibiting

momentum, but fall back in the long run exhibiting reversals; undervalued IPOs, on the

other hand, earn lower returns in the short run but earn higher returns in the long run.

Both windows of opportunity and the fads hypotheses are unable to explain why

there exist another group of IPO stocks that over perform the market or their counterpart

non issuers:

Over performance implies under pricing at the offer.

B. The Agency Cost Hypothesis:

Given divergence of interest between managers and shareholders, we believe that

managers prefer to divert the proceeds from new issues of equity or excess cash flows to

investments in projects with negative net present value at the expense of shareholders

wealth. The selfish behavior undertaken by management in combination with the excess

cash on firm balance sheets points to Jensen's value-destroying hypothesis. Consistent

with this agency cost explanation, the long-term decline in operating performance is

greater for firms that have higher free cash flows.

The IPOs provide an interesting empirical setting in which another important hypothesis

related to agency costs emerges because the interests of managers and shareholders

become less closely aligned as managers stakes decrease and ownership becomes more

disperse. As a result, an issuing firms performance should suffer after going public.

In a similar line of logic, a negative relation exists between the level of agency costs prior

to new issues and subsequent changes in performance because new equity issues often

decrease the proportion of equity owned by managers thereby diminishing the managers

incentives for value maximization.

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C. The Earnings Management Hypothesis:

One peculiar observation from various statistical data compiled by the past studies is that

both IPO and SEO firms exhibit unusually large and significant gains in operating

performance above and beyond the industry average or the control group of matched non

issuers one year prior to the offer date. We also observe that firms in the year prior to the

offering provide a higher return.

This observation leads us to suspect the presence of aggressive earnings management that

is intended to lead investors to be overly optimistic about the issuers prospects. When

initial high earnings cannot be sustained, disappointed investors revalue the firm down to

a level justified by the fundamentals. Decomposing net income into cash flow from

operations and accruals from accounting adjustments, post-issue underperformance in net

income is caused by accruals. When accruals are further decomposed into four categories

by time period (current and long-term) and manager control (discretionary and

nondiscretionary) discretionary current accruals drive the post-issue earnings

underperformance and predict underperformance in post-issue stock performance.

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Indian IPOs

Theoretically, a firms objective is to collect as much money as possible from the

investors for a given quantum of shares offered and thus firms are expected to go public

when initial returns are low. The empirically established positive relation between initial

return and IPO-volume poses a puzzle and underlines the signaling role (or information

content) of initial return series.

While the above-mentioned literature suggests concentration of IPOs during the hot

periods and change in the firms characteristics during hot and cold markets,

The IPO literature has frequently emphasized the role of investors optimism and the

consequent hot market. Ritter (1984) provided empirical evidence of investors over-

optimism in bidding up the aftermarket prices. Empirical literature has documented that

the underperformance is more for small and young companies that mostly go public

during the hot market period. Companies planning to go public try to predict the

investors sentiment on the basis of market behavior.

Against the above backdrop, the objective of this paper is to study the Indian IPO market

and to analyze the factors that influenced the volume, under pricing and timing of issues

during these two phases.

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Empirical Findings

Table 1 reports the average of the key variables over the entire 1990s. It shows that the

average amount raised over the years has increased in the second half of 1990s. The

number of new issues that got listed on the BSE went up considerably during 1993-96.

These three years were followed by a slump when the number of IPOs went down

substantially.

A close inspection of the monthly volume of IPOs shows that there were two distinct

phases in 1990s.

Table 1: Averages of Key Variables for Indian IPOs

Size: Money raised by the issuers from public at constant prices;

Under pricing (U_D): percentage difference between listing and offer price.

A_UD: Under pricing adjusted for the market return over the issue-listing period;

Volume: The average of the number of IPOs getting listed on BSE in a month over the

financial years considered here.

Note: All averages reported in the Table are significantly different from zero at 1 per centlevel.

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Performance of IPO

Empirically the timing of this structural break has also been confirmed by the Markov

Switching process. Chart 1 shows the number of IPOs over the months and the

probability of the structural break (switching regression model) in the Indian primary

market. There was a boom phase when (on average) more than fifty IPOs got listed on

BSE per month (1993:01 to 1996:09) and subsequently a slump when only a handful of

companies raised money from the stock market.

It is evident from the literature survey that the IPO volume and the under pricing series

follow a certain time pattern. The periods of large number of IPOs and the high initial

return are followed by periods when only a handful of companies get listed in the market.

Empirical evidence suggests that the firms decision to go public could also depend on

the behaviour/decision of the other companies and on the observed initial returns. The

decision to get listed and its timing are also found to depend on firm-specific

characteristics (e.g. industry affiliation, size of the issue, age). Having identified the

boom and slump periods, and in light of the above observations, the subsequent sections

of this paper analyze the pattern and characteristics of the key variables over different

phases in the Indian IPOs market.

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Performance of IPO

Time Pattern of IPO Volume and Initial Returns

In this section we analyze the IPO volume and under pricing series individually taking

into account different phases in the Indian IPO market during 1993-2001. Earlier

researchers argued that the fluctuation in IPO volume series could be related to three

factors. These are changes in private firms aggregate demand for capital, changes in cost

of issuing equity (under pricing) and variation in investor optimism. In face of cyclical

pattern in the IPO market, empirical evidence from the developed countries suggests that

the volume and under pricing series depend considerably on their past behaviour.

Table-2 reports the autocorrelation in the monthly volume, under pricing and index

adjusted under pricing series for the Indian IPO market. Empirical results show that the

monthly volume of IPOs has a large autocorrelation (first lag coefficient being 0.62) that

dampens slowly over the increasing lags.

However, the other two series, namely, under pricing (U_D) and market adjusted under

pricing (A_UD) did not show signs of any strong autocorrelation or uniform damping

pattern over the sampling period. The inertia or time dependence is indicative of the

information content of the IPO volume (number of IPOs in a month) which could have

signaled the issuers about the buoyant market conditions or the new profit prospects.

Table2: Autocorrelation in Volume and under pricing Series

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Performance of IPO

From Table 2, the differences in the pattern of the autocorrelation coefficients over

different phases become quite evident. For the boom period, the coefficients display a

high initial autocorrelation coefficient and a slower damping pattern over the lags while

during the cold period, the same show a weak and mixed pattern. So, it appears that

during the boom period, a companys decision to go public depended more on past IPO

volume as compared to the slump periods. The above analysis suggests that the nature

and pattern of autocorrelation over the entire period of time and over the sub periods

differ considerably. The high autocorrelation in the monthly IPO volume during the hot

market might be indicative of the investors optimism resulting from the array of

liberalization measures announced during the first half of 1990s. The under pricing series

also illustrated considerable autocorrelation and slow damping pattern over the increasing

lags during the boom period. The similarity in the autocorrelation pattern of the IPO

volume series and under pricing series might be indicative of one influencing the other

during the hot market period as observed in the developed markets. We investigate the

interrelation between the volume and under pricing series in greater detail below.

Interrelation of IPO Volume and under pricing Series

Besides the observed autocorrelation, the other question that needs due attention is the

relation between the IPO volume and under pricing. This is important since the existence

of such a relation would imply that the issuers time their offers in response to the value-

relevant information available from the under pricing series and vice-versa.

For empirical investigation of the IPO volume and under pricing relation, this paper

attempts Granger causality tests to examine the presence of any causal relation between

IPO volume and past under pricing.

Granger causality test assumes that X causes Y if the past values of X help in predicting

Y in addition to past values of Y. The causality test is generally done by running

regression of the following form:

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Performance of IPO

If the i coefficients ( i=1m) are jointly and significantly different from zero (the F-

test statistics is greater than its critical value) then the null hypothesis X does not cause

Y is rejected. In order to determine the optimal lag length (m) this paper uses the

Schwarz information criterion.

Before proceeding to test the causal relation between monthly volume of IPOs and the

monthly average under pricing one must ensure that the two series considered are either

stationary or have 'same statistical property'. By 'same statistical property' it is meant that

the series have to be differenced or de-trended the same number of times to render them

stationary. To test for the stationarity of the series, Augmented Dickey Fuller (ADF) test

was performed. The ADF test found the presence of unit root (at one per cent level) for

the three series considered during the whole period and for the hot period. However, all

the three series were stationary during the cold period. The causality test this paper

considers the hot and cold periods separately and not the entire period. To take care of the

non-stationary problem during 1993-96, IPO volume and under pricing series were

difference for the hot period. However, the series in levels were used for the cold phase.

Table 3 reports the coefficients and Granger-F statistics for volume and under pricing for

the periods considered, the lags being selected on the basis of Schwarz information

criterion as mentioned above. The coefficients of under pricing over different lags in the

causality test (for whether under pricing caused the IPO volume) reported low values and

none of them were significant at 10 per cent level (Table 3, Column 2 and 3). The value

of the Granger F-Statistics (0.29) and its P-value (0.96) also confirm the joint

insignificance of the estimated under pricing coefficients and reject the hypothesis that

monthly IPO volume was Granger caused by past values of the under pricing series.

The causality analysis for the cold period (that used the above mentioned variables in the

levels) supported the finding for the hot period. The coefficients of under pricing (as

indicated in Table 3, Column 7 and 8) took low values.

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Performance of IPO

Table 3: Causal Relation between Volume and Under pricing

Note: Hot Period (Jan 1993 to Sep 1996): more than fifty IPOs in a month; Cold Period

(Oct 1996 to March 2001): less than fifty IPOs in a month; Granger F test for causal

relation of volume of IPO and initial returns and vice versa; Under pricing (U_D):

percentage difference of listing and offer price; Vol: No. of IPOs getting listed on BSE in

a particular month; and, Vol1 (volume) and U_D1 (Under pricing) reported here refer to

the first differenced series vol and U_D for the hot market.

These results reveal that the issuers decision to go public during the boom and the slumpperiods did not depend on the past values of the under pricing series. They also show that,

unlike the international experience, the Indian issuers decision to go public was not

directly dependent on the information content (investors optimism or the cost of rising

funds) of under pricing series. This might be because; the time consumed by a company

for getting listed in a stock exchange in India was very high over the study period.

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Performance of IPO

The average time lapsed in between the offer date and listing date being 4 months, the

company deciding to go public was able to get listed only after six months (two months

are considered for registration and other official formalities prior to the offer). Under

pricing being the percentage difference of the offer price from the listing price (the

former being decided six months before the latter) might have recorded the breaking

news over this long time lag rather than the investors optimism perse. Given such an

institutional structure, it might have created considerable uncertainty on the issuers part

to decide about the investors sentiments and/or cost of raising funds after a considerable

period of time from the observed under pricing.

We also test the existence of any reverse causality (under pricing is caused by IPO

volume) in the Indian IPO market which was argued that the under pricing might increase

during the hot market period with the increases in the number of listing, since the hot

market is often a result of technological innovation or positive productivity shock that

increases the number of companies getting listed from new and high risk industries. The

high risk factor pulls up the initial returns during the hot periods. Empirical results for

India reject this reverse causality hypothesis for both hot and cold periods (Table 3,

columns 4 and 5, 9 and 10). These findings apparently question the hypothesis of positive

technological shock resulting in industry clustering - an argument that is often forwarded

to explain hot market. The industry clustering issues are discussed in more detail later.

The above empirical analysis might be summarized by saying that the issuers in India

during the 1990s did not base their decision to go public on the past values of under

pricing series. The boom during the first half of 1990s was perhaps a result of the

investors optimism and business conditions rather than the information content of the

under pricing series. While the above analysis concentrated on the time pattern and the

information content of the under pricing and IPO volume series, it remained silent about

the firm-specific characteristics and their influence on the timing of a firm going public

during hot and cold periods. The next sub-section is devoted to a detailed study of these

factors influencing the companies decision to go public during the hot and cold periods.

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Performance of IPO

Firm-Specific Characteristics of IPOs during Hot and Cold Phases

In order to test as to how the firm-specific characteristics influence likelihood of listing

across two periods, the analysis in this sub-section following Helwege and Liang

( examines the likelihood of an IPO in hot phase vis--vis a cold phase, conditional upon

the companies that actually went public during these phases.

This is done by employing a log it model where the dependent variable is a coding for a

discrete qualitative outcome [e.g. whether a particular company is going public in high

volume (hot phase, Y=1) or low volume (cold phase, Y=0)].

The general specification of the model is as follows:

where the set of parameters _ reflect the impact of changes in X on the probability. The

logistic distribution used here is given by:

Like other non-linear distributions, the coefficients do not represent the marginal effect

of the change in a particular variable. The marginal effect in such case is given by

Where f (.) is the density function to the corresponding cumulative distribution F (.).

In an attempt to examine whether the likelihood of IPOs getting listed from a particular

industry is more during the hot period, all industries were broadly classified as Primary,

Manufacturing (MNF), Services (SER) and computer related industry (SOFT) and were

included as explanatory variables in the logit model. It is documented in the IPO

literature that small and young companies are likely to go public during the hot period to

take advantage of investors enthusiasm.

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Performance of IPO

To examine the validity of this argument the issue amount (SIZE) and the age of the IPO

companies are included in the model to evaluate their influence on the likelihood of the

Indian companies getting listed in the hot market. The other factor of interest is the under

pricing of the IPO firms. Signaling theory claims that the good firms would get listed

during the hot market and under price more to win investors confidence. The IPO

underperformance school, on the other hand, believes that new firms would try to collect

as much money as possible from the enthusiastic investors during the hot market. So, the

above mentioned model includes under pricing as an explanatory variable and evaluates

companies pricing decision during the hot and cold periods. The constant term was not

included in the model in order to avoid the dummy variable trap problem. The

coefficient of the logit model estimated using maximum likelihood estimation technique

is reported in the Table 4.

Table 4 shows that the primary, manufacturing and services sector considered here have

similar positive coefficients. The value of the -coefficient for the software sector is

however considerably less (1.4) than that of other sectors. All the sector-specific

coefficients were significant at one per cent level. This might be interpreted as the non-

existence of industry clustering in India during hot market, since companies from

primary, manufacturing and service sector got listed in the market during this period.

Table4: Factors Influencing the Likelihood of IPOs

Note: *ME is Marginal Effect of the logit model

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Performance of IPO

PRI is the dummy variable which takes value one if the company is from Primary Sector,

otherwise zero. Similarly, MNF, SER and SOFT are the dummies for Manufacturing,

Service and Software sector respectively. SIZE is issue amount adjusted by GDP deflator;

U_D is under pricing: percentage difference of listing and offer price [Pl-Po/ Po]; Age is

the difference between the incorporation year and the listing year.

The marginal effect (ME) reported in Table 4 confirms this finding as the ME figure for

the software sector was lower than the other three sectors. Table 4 however, shows that

the coefficients of size, age, and under pricing have negative signs and were significant at

1 per cent level. The negative signs of the size, age and under pricing coefficients suggest

that the large and well-established firms got listed in the cold period and under pricing

were more. It might be because investors being less enthusiastic during the cold period,

only the well-established firms could convince them about their prospective investments

and raise funds through IPOs. It is generally believed that firms raising large amounts of

money are scrutinized more by the market than their small size counterparts.

So a larger size might have acted as a signal to the market and helped the issuers to raise

more money during the cold period. The IPO firms during the cold period might have

used under pricing as a signaling device (as suggested by the signaling theory) to

persuade the investors about their good quality and raise large amounts of money from

the market.

The results thus do not support the industry-clustering hypothesis explaining hot period

for the Indian IPO market in 1990s. Firms from all the existing sectors of the economy

took advantage of the booming primary market and investors optimism and raised funds

from investors in the first half of 1990s. The evidence presented in this paper suggests

that the likelihood of established companies raising large amount from the primary

market and under pricing considerably is more during the cold phase whereas the small

and young companies time their issues during boom phase.

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Performance of IPO

Methodology:

For the initial after market (close of the first day of trading), we computed the market

adjusted abnormal returns (MAAR0) for each firm. For simplicity, we describe the

methodology below.

The total return for stock i at the end of the first trading day is calculated as:

Ri , 1 = ln(Pi , 1 / Pi , 0)

where Pi , 1 is the price of stock i at the close of the first trading day, Pi , 0 is the offer

price and Ri , 1 is the total first-day return on the stock.

The return on the market index during the same time period is:

Rm , 1 = ln(Im , 1 / Im , 0)

where Im , 1 is the market index value at the close of first trading and Im , 0 is the market

index value on the offer day of the appropriate stock, while Rm , 1 is the first days

comparable market return.

Using these two returns, the market adjusted abnormal return for each IPO on the first

day of trading is computed as:

MAARi, 0 = 100 {[ ( 1 + Ri , 1 ) / ( 1 + Rm , 1 )] 1}

Table 2 gives the average first day returns for the IPOs for the entire sample and for

offers and placements separately.

For the sample the average MAAR0 was found to be 9.74% with an associated tstatistic

of 8.54 (the t-statistics on initial returns must be interpreted with caution since the

distribution of initial returns is positively skewed). The MAAR0 has a median of 6.30 and

a standard deviation of 17.21. However, it is still rather surprising, since placements are

usually available to institutional investors who are more likely to be better informed

about the true value of an issue.

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Performance of IPO

It is not clear why institutional investors need a higher first day return incentive to

encourage them to participate in the new issues market. The differences in average initial

returns between offers and placements might be related to the degree of uncertainty about

the true value of an issue.

Since the placement is usually the method of issuance used by smaller companies, we can

argue that the differences in initial returns between the two methods are related to the

market value of the offerings.

Using gross proceeds for the issue as a proxy for the size of the company that in turn can

be used as a proxy for the uncertainty about the true value of an issue, i.e., the smaller the

size of the company the higher the uncertainty. We did not find any significant

relationship between the gross proceeds from the issue and the initial returns (the Pearson

product moment correlation coefficient was found to be -0.03). Only 11% of the total

number of placements started trading below their offer price as compared to 19% of the

offers for sale. This provides further evidence for the asymmetric information model in

that the placements are usually available to institutional investors who are believed to be

better informed about the true value of the issue. Though the presence of some

overpricing is surprising. The market adjusted long-run after-market returns were

calculated following the first month of trading using the LSPD, which reports the

monthly return, measured on the last day of the month on which the stock is traded. These

returns incorporate dividend payments and are adjusted for rights and scrip issues.

Allowing for the initial under pricing and the possibility of price support in the first few

trading days, the first month of trading was excluded from the study of long run returns. It

is expected that this month would allow prices to adjust downwards towards the true

market equilibrium after the support has been withdrawn. The following methodology is

used to calculate the long-run returns:

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Performance of IPO

where MABHRi denotes the market adjusted buy and hold return for a firm i over a 37

month period (for the purpose of the study this constitutes only monthly readings since

the first month of trading is excluded from the data) and Pi, t and Im, t denote the end of

the t month share price for the firm i and the corresponding end of the month index

respectively. These returns exclude initial under pricing. Buy and hold returns were

preferred to Cumulative Average Abnormal Returns (CAARs). Conrad and Kaul showed

that cumulative abnormal returns are biased because they not only process true returns

but also the upward bias in single period returns induced by errors in measurement. In

contrast buy and hold returns do not suffer from this bias. Moreover CAARs implicitly

assume frequent and thus costly portfolio rebalancing. Barber and Lyon also argued that

the abnormal returns should be calculated as the simple buy and hold return on the

sample firm less the simple buy and hold return on the benchmark.

In this study, we have not adjusted the monthly abnormal returns for systematic risk. The

previous studies demonstrated that the average betas decline with the length of time after

the IPO and the average difference in betas between the IPOs and matching firms

becomes too small to have any significant effect on the results.

The average monthly MABHR returns with the associated t-statistics for the 37 months

after going public. Many IPOs were delisted in their first year of trading because of

acquisition/takeover/merger while another 11 were delisted in the second year (one

company was declared bankrupt while the other 10 were delisted due to

acquisition/takeover/merger). The third year saw a similar fall with only 10 firms getting

delisted (all of them were delisted because of acquisition/takeover/merger). So over the

three-year period 23 firms were delisted which is about 10% of the total sample. 25 of the

36 monthly average market adjusted returns were found to be negative with 7 of them

having t-statistics lower than -2.0.

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Performance of IPO

The average MABHR for the sample period as a whole was found to be -17.81% with a t-

statistic of -3.68. The under-performance of the IPOs is both statistically and

economically significant. We believe that our results are free from any significant

survivorship bias because only 23 firms were delisted in the 36 month period.

We also calculated the long-run performance for small and large firms, profitable and

loss-making firms, firms with assets and liabilities and firms with different turnovers.

Table gives the average MABHR returns of the cross sectional study. Our results confirm

that under-performance is largely concentrated in the smallest issuing firms. The large

firms in the sample, in fact, do not show a statistically significant underperformance. The

cross sectional study also exhibits some interesting results. Firms which earned profits in

the last three years before they were listed show more underperformance than the firms

that were running losses before their listing (for three years before listing). This indicates

that listing provides an efficient monitoring for badly performing firms while firms with

healthy profits in the pre IPO period suffer from a management slack. As expected, firms

with net liabilities perform worse than firms with net assets before the IPO. Firms with

large turnover in the year before flotation perform better than small turnover firms.

The plot of market adjusted monthly returns for the sample. The returns vary between 1%

and -2.7% over the study period. The returns peak at 1% in the 18th month of trading. A

minimum return of -2.7% is recorded in the 36th month.

The plot for the cumulative raw returns and also the cumulative Index adjusted monthly

returns. The cumulative raw returns are positive and are stable around 2.5% for the first

15 months and peak at 16.28% in the 28th month. They fall to 10.07% by the 37th month.

The cumulative Adjusted monthly returns are negative and suffer a continuous decline. A

sharp fall starts after the 28th month and continues till the end of our period of study

(37th month). At the end of the 37th month the cumulative adjusted monthly returns were

-24.57%.

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Performance of IPO

First day market adjusted returns (in %) for the IPOs

The average monthly MABHR returns after going public.

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Performance of IPO

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Performance of IPO

Explaining the topic

Hypothesis 1: There is a positive relationship between the size of a firm at the time

of its going public and its listing share price performance after the IPO.

As a proxy for quality of a firm, the variable COST has been used. It represents the total

direct costs of going public expressed as a percentage of the funds raised by an IPO. The

costs of flotation include under-writer commissions, legal, printing and auditing. Table 7

reports the average direct costs of going public as well as the costs of flotation as a

percentage of the funds raise at the time of offer.

Of all the elements that add up to make the costs of flotation, the underwriters

commission component is expected to vary according to the quality of the firm. The other

components are expected to depend on the size of the offer. By dividing the total cost by

the size of the offer, we eliminated the components which vary with the offer size but

retain the ones that vary with the quality of the firm whose shares are on offer. We make

the following four assumptions before formulating a hypothesis on COST.

(1) Bigger firms are better quality firms

(2) Bigger firms raise larger amounts of capital from their listing

(3) Bad quality firms are bad long term performers

(4) Assumption (3) is known to the underwriter and hence this information is reflected in

the cost of underwriting.

Based on these assumptions, we hypothesize that as the size of the funds raised increases,

the quality of the firm becomes better (because larger IPOs are often made by more

established firms and so there is less risk about the true quality of the firm) and hence the

proportion of costs of the funds raised decreases (underwriters charge a relatively smaller

commission for underwriting bigger firms). So we expect a negative coefficient for

COST. The following hypothesis is considered for COST:

Hypothesis 2: The higher the cost of flotation expressed as a percentage of the fund

raised, the worse is the quality of the firm and the worse is the listing and long run

performance.

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Performance of IPO

The other variables used as a proxy for quality, risk and reputation of a firm are:

DURATION, PROFLOAT and MSHARE. While the variable DURATION gives the age

of a firm (in days) from the date of incorporation to the day of listing, the variable

PROFLOAT gives the average profits (or losses) for the last three years before the firms

listing. The market share of the underwriter is expressed as variable MSHARE.

The mean age of the firm in the sample was around 9 years. Some companies were 20

formed to takeover others and hence the DURATION for some of them is as less as 50

days.

The age of the firm has been suggested as a proxy for the risk (i.e. quality) of the IPO

firm and interpreted his evidence as being consistent with the over-optimism explanation.

We anticipate the coefficient for age (DURATION) and underwriter reputation

(MSHARE) to be positive for the long-run return analysis. A firm which is profitable

before flotation should continue to be so after the IPO. The profit in period t is normally

highly correlated with profit in t-1 period. This suggests that the more profitable a

company is before its listing, the better is its long-run performance. So we expect a

positive coefficient for PROFLOAT. We consider the following hypotheses for the

variables used as a proxy for quality and reputation of a firm.

Hypothesis 3:

(i) Underwriter Effect

The major benefit associated with IPOs is the cost saving of the underwriter spread,

which is typically about 7% Therefore, from an investors viewpoint, if the firm passes

through the full amount of cost savings to investors, holding other things equal, IPO

shares might be relatively cheaper compared to their counterparts in IPO market.

Another often cited reason for these firms to raise capital through IPOs is that the size of

the issue is too small to attract an interested underwriter. Instead of allowing for the

business to grow to a size suitable for IPO, these firms choose not to wait. However, the

choice of not undergoing underwriting process (i.e. a lack of validation from a third

party) may send out negative signals to market investors.

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Performance of IPO

Beside the size of IPO issue, we argue that investment banks are selective in underwriting

IPOs. In order not to tarnish their reputation which in turn will have an impact on their

future earnings, the top investment banks will only underwrite IPOs which they believe

will have a strong showing in the aftermarket. A strong showing in the aftermarket will

not only allow the underwriters to profit further by exercising their over allotment options

but also strengthen their position in the IPO market. Premarket activities undertaken by

underwriters can signal to public the strength of the demand for the IPOs.

Previous Studies show that an upward adjustment of the offer price is positively related to

the degree of under pricing which is an indication of a strong demand for the IPOs.

Investors overreaction to the signal of strong demand on issue date will lead to undue

self attribution bias which further induces overreaction to continue in the short-run.

However, eventually, prices will revert to the fundamental value. It is usually argued that

underwriters offer to buy back stock in the aftermarket is equivalent to giving a put

option to investors to compensate for the potential adverse selection bias due to the

presence of information asymmetry. They further assert that only reputable investment

banks are in the position to convince investors that they will carry out such a promise in

the aftermarket. Therefore, we argue that the more reputable the underwriters are, the

higher will be the perceived quality of the signal.

The selectiveness of underwriters and the signaling effect of underwriter premarket and

aftermarket activities have lead us to formulate the following hypotheses:

H3 (i) H1: Stock prices of firms which have undergone direct public offering will under

perform those of firms whose IPOs are underwritten by investment banks.

H4 (i) H2: Aftermarket patterns (positive short run price momentum and long run price

reversal) as predicted by DHS will be more pronounced for IPOs underwritten by more

reputable investment banks.

(ii) Venture-Capital Effect

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Performance of IPO

Conflicting theories has been forwarded by researchers on the role played by venture

capitalists in certifying the quality of an IPO. On one hand, we argue that the backing by

venture capitalists is a signal of quality and thus help in alleviating the problem of

information asymmetry existed between management and investors. On the other hand

the backing by venture capitalists will result in adverse selection in that only lower

quality enterprises will be willing to sell part of the company to venture capitalists at a

discount.

The empirical evidence on the relationship between issue-date return and pre-IPO venture

funding is inconclusive. Some studies find that venture capital backing is associated with

lower initial returns, others with higher initial returns Francis and

The evidence show that a venture capital financed IPO has better long-term aftermarket

performance, especially in the case of smaller companies. The better long-term

performance can be attributed to the fact that reputable venture capitalists would

effectively screen the companies they back and thus signaling the quality of their IPOs.

In addition, the expiration of IPO lockup period, the heavier selling by venture capitalists

results in a larger negative abnormal return and a higher trading volume.

To evaluate the impact of Venture-Capital financing on stock performance, two

hypotheses are formulated:

H3(ii)H1: If venture capitalists play a certification role, the aftermarket patterns (positive

short run price momentum and long run price reversal) will be more pronounced for

venture capital backed IPOs.

H3(ii)H2: The exit by venture capitalists on the expiration of the lockup period will

hasten the price reversal.

(iii) Interaction of Underwriter and Venture-Capital Effects

Specifically, lower initial pricing and underwriting spread are observed for IPOs backed

by highly reputable venture capitalists. Since the reputation of underwriter as well as the

reputation of venture capitalist can have an effect on the performance of IPOs, one could

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Performance of IPO

argue that because well-established venture capitalists may have a strong business

relationship with some of the leading underwriters, there is a possibility that any venture

capital effect observed may be an underwriter effect in disguise.

On the other hand, if investors perceive the backing by venture capitalists as an extra-

layer of assurance for high quality in addition to the positive signal provided by reputable

underwriters, IPOs underwritten by leading investment banks and backed by venture

capitalists will have the best aftermarket performance.

In order to examine if there is an interaction between the effects of types of underwriters

and the method of financing, we first subdivide the sample by underwriter market

leadership and then by venture capital financing.

H3(iii)H1: Underwriter effect subsumes venture capital effect. Once we factor out the

underwriter effect, there will be no significant venture capital effect.

H3(iii)H2: If venture capitalists play a certification role, the aftermarket patterns

(positive short run price momentum and long run price reversal) will be most pronounced

for IPOs underwritten by more reputable investment banks and backed by venture

capitalists.

(iv)Industry Effect

The investors are more likely to be affected by overconfidence when dealing with vague

and subjective information. Since it is more difficult to value growth stocks, self

attribution bias will have a stronger influence in the valuation of these stocks. The

eventual correction of the mispricing will result in a stronger short term price momentum

for growth stocks and the momentum effect is strong for growth stocks but weak or

nonexistent for value stocks.

We also examined whether the long-term performance of IPOs is different between high-

tech IPOs and non-high-tech IPOs, and this may allow us to determine if the market

overreacts to high-tech IPOs.

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H3(iv)H1: The aftermarket patterns (positive short run price momentum and long run

price reversal) as predicted by DHS will be more pronounced for IPOs by firms in the

high-tech industry.

(v) Interaction of Underwriter and Industry Effect

Lastly, we also examine the interaction between the underwriters and industry effects.

The sample is first subdivided by underwriters market leadership and then by firms

industry classification. It is our contention that the initial mispricing of high-tech IPOs

together with the signaling effect of underwriter reputation will result in a stronger short-

run price momentum.

The following two hypotheses are tested:

H3(v)H1: Underwriter effect subsumes industry effect. Once we factor out the

underwriter effect, there will be no significant industry effect.

H3(v)H2: The aftermarket patterns (positive short run price momentum and long run

price reversal will be most pronounced for high-tech IPOs underwritten by more

reputable investment banks.

Hypothesis 4: The more profitable a company is before its listing, the better is its

long-run performance after the IPO.

We use industry based on the industry groups the sample firms belong to. These codes are

the primary research has shown that there are marked differences in the long-run

performance of individual industries.

Industry classification has been used to capture this difference. Indirectly, they also help

to adjust for differences in business cycles between industries. Year classification are also

added which correspond to the year of the IPO. 4 yearly classifications are used for the

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sample period and they adjust for business cycles in that they allow for the fact that the

IPOs are taking place at different stages of a business cycle.

Proxies for multi-nationality and diversity of products for a firm have also been used.

This study is the first attempt to address long-run under-performance using the nature of a

firm. DIVERPRD gives the number of two digit standard industrial classification for the

firm. This is used to show how diverse the company is in its products shows the

multinational character of a firm.

Hypothesis 5: The higher the dilution of the original share holding (the higher the

percentage of equity sold) in the IPO, the worse is the long-run performance.

The estimation method is ordinary least squares. We use the market-adjusted buy and

hold return after three years as the dependent variable in the regression analysis. 6 The

minimum equity issued was 7% while the maximum was 100%. Expecting the presence

of outliers we indorsed the data on equity issued. Examination of the data showed that

there was a gradual increase in the percentage of equity issued.

The empirical model is displayed as follows:

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Outcomes

An examination of the distribution of the long-run returns and the independent variables

shows that most of them are positively skewed but are not significantly non-normal. Only

MABHR and FLOAT are negatively skewed but are also not significantly non-normal.

The results are presented for small firms, large firms and the full sample. As shown

earlier, the small firms drive the long-run under-pricing where as the share price

performance of large firms is driven by the managerial decisions and the firms financial

performance before the IPO.

As expected, we find a positive relationship between the size of a firm and its long run

performance. The larger the size of a firm in terms of the assets at the time of floatation

the better is the long run performance.

The results on the size variables suggest that hypothesis1cannot be rejected.

The size of a firm prior to its going public has a positive impact on the long-run

performance of the firm in the post-IPO period. Of the quality, risk and reputation

variables used COST and PROFLOAT show significance. The underwriters reputation

and the age of the firm fail to explain the long-run under-performance. For small firms

the higher the costs (as a percentage of funds rose) of flotation, the more is the under-

performance. This lends support our earlier proposition that underwriters know of the risk

involved with an IPO firm (especially if it is a small firm) and hence charge higher

underwriting costs to risky firms. For large firms this effect is absent thereby indicating

the underwriters perception of firms. They categorize small firms as risky and large

firms comparatively less risky though it is difficult to comment on an underwriters

definition of small and large firms.

These findings validate hypothesis 2 and signal that the ratio of the cost of flotation to

the funds raised explains the long-run performance of an IPO firm.

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We do not find any significant relationship between the age of the firm and its long-term

performance. These results were arrived at, after an exhaustive model building exercise,

where we tested a range of non-liner models along with linear ones. The non-linear

models did not improve the regression results.

In order to account for general market conditions, market-adjusted returns are used to

evaluate the aftermarket performance of initial public offerings

whereRi,tis the monthly return of firm i on month t,Rm,tis the monthly return of market

index in month t, andARi,tis the monthly abnormal return of firm i in month t.

The market-adjusted Cumulative Abnormal Return (CAR) is calculated as follows:

whereRi, lis the monthly return of firm i in month l,Rm,lis the monthly return of market

index in month l, and CARi,tis the cumulative market-adjusted abnormal return for firm i

from month 1

Hypothesis 3 thus stands valid.

We document three other variables that have an effect on the long-run performance of

IPO firms. One of these is a performance variable (PROFLOAT) and the other two are

nature of the firm variables (GSCOPE and DIVERPRD). Ownership structure

(EQUISSUE) and initial under pricing (MAAR0) also explain long-run under-pricing.

We find a negative relationship between the profitability of a firm prior to going public

and its long-run performance. The result is stronger for larger firms. The more profitable

a firm is prior to going public; the worse is the long-run performance.

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This result is surprising and contradicts ourhypothesis 4.

It suggests that firms go public at the height of their performance thus seizing their

window of opportunity. Similar conclusions were reported by others researchers who

showed that long-run share price performance and the change in operating performance

from before to after flotation are negatively related: when operating performance fails to

sustain pre-listing levels of profitability, share prices fall, indicating that investors were

surprised by the change in operating performance.

We also find a significant relationship between the degree of multi-nationality of a firm

and its long-run performance. This effect was strong for both the small and large firms

alike. The more multinational a firm (in terms of subsidiaries in different countries) the

better is the long-run performance. This could be the result of diversification of the risk of

a firm and the positive effect this has on investors sentiments.

This result validates hypothesis 5 and suggests that investors value multinational firms

more than domestic firms. Multi-nationality signals quality and reputation of a firm.

We also document a relationship between ownership change at the time of IPO and long-

run performance. We find that the higher the proportion of equity sold at the time of

offering (i.e. the higher the dilution of original share holdings) the worse is the long-run

performance.

The result is stronger for large firms. These results are consistent with the predictions of

previous researchers who argued that incentives of an owner/manager change when

shares are issued to another party.

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Description of the variables used in the study

Industry Dummies: The dummies are based on different industry categories.

Year Dummies: These are based on the 5 different years of the initial public offering.

AR = Market adjusted Abnormal Return

CAR = the market-adjusted Cumulative Abnormal Return

Proxies for q

Performance of IPO as Compared to CRISIL GRADE

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