ANALYSIS OF FACTORS INFLUENCING UNDERPRICING LEVEL DURING THE INITIAL

PUBLIC OFFERING: A STUDY OF SELECT COMPANIES GOING PUBLIC IN BOMBAY STOCK

EXCHANGE

Dr. Mahesh R. MBA, Ph. D.,

Associate Professor, DOS in Business

Administration, University of Mysore,

Karnataka

Email: maheshrajgopal@yahoo.com

Mob No: 09886639536

Ms. Melyawaty S. D. MBA,

Administrative Officer, On Track Wealth

Service and Wealth Axis, Indonesia

Email: melya.bluerose@gmail.com &

s.melyawaty@yahoo.com

ABSTRACT

Keywords: Under-pricing, Debt to Equity Ratio, Return on Assets, Earning per Share, Company size,

Company Age, and Initial Public Offer

JEL Classification Code: G10, G20, G23, G24

Introduction

Initial Public Offering is expected to make a better prospect for the company for the expansion being

done. The betterment of the future prospect of the corresponding company will increase the share price.

Performance of the company before IPO is the information for the investors about the progress the company

could make after the IPO. Investors’ expectation is that the company’s performance after IPO could be even

better.

The problem which often arises from IPO is under-pricing which indicates that the price of the share

during the IPO is relatively lower than the price of the share when traded in the secondary market. When a

company is going for an IPO, the share price which is sold in the primary market is determined on the

agreement between the company which is going for listing and the underwriter, while the price in the

secondary market is determined by the existing market mechanism through the demand and supply of the

shares in the capital market. If the determination of share price on the IPO is significantly lower than the price

in the secondary market on the first day of listing, then we could say that the share is under priced (Kim,

Krinsky, and Lee, 1995).

This condition could happen because the company which is going for listing for the first time and the

underwriter together arrange for an agreement in deciding the price of the initial share but both of the parties

are having different necessity to be fulfilled. As the party who needs more funding, the company wants a high

initial share price as high price will generate expected fund, but it is not the same for the underwriter.

Underwriters will try to minimize the risk by determining the price which is reasonable for the investors. By

deciding on a relative price which is acceptable by the investors, underwriters expect to sell all the shares

which come under its guarantee.

Underpricing condition creates loss for the companies which are going public because the fund

obtained will not be in the maximum level. On the contrary, if there is an overpricing condition, then investors

Under-pricing is the condition of the pricing of an initial public offering (IPO) which

is below its market value. When the offer price is lower than the price of the first trade, the stock is

considered to be under-priced. A stock is usually only under-priced temporarily because the laws of

supply and demand will eventually drive it toward its intrinsic value.

The objective of this research is to analyse the variables (factors) that affect the under-pricing

level in the companies listed in the Bombay Stock Exchange based on the CRISIL rating for the period of

2007-2011. Those factors are categorized into two parts – financial and non-financial factors. The

financial factors are Debt Equity Ratio, Return on Asset, and Earnings per Share. The Non-financial

factors in this study are the data of Company size, Company Age, and Net-Issue of the Shares to the

Public. During that 5-year period, there are 27 companies which fulfil the criteria for the study of

under-pricing issue.

This study is using Generalized Linear Model (GLS) of Multiple Regression and it could be

concluded that in this case none of the factors being analysed has positive significant effect towards the

under-pricing level.

All the variables being under study are not having any influence towards the under-pricing

level. In other words, the under-pricing level of these companies which are studied under specified time

and selection criteria are not determined by all those factors.

will be the party who lose because they will not get positive initial return. Initial return is the profit that the

shareholders get on the margin between the shares purchased in the primary market and the shares sold in the

secondary market. The company owners will try to minimize underpricing situation because underpricing will

cause transfer of wealth from owner to investors (Beatty, 1989).

Study on the underpricing level and the price of the shares which is connected to the information on

the prospectus is an interesting subject for a financial researcher to evaluate investors’ behavior empirically in

taking investment decision in the capital market. Previous researches regarding to financial and non-financial

information towards initial return or underpricing have been done extensively in various stock exchanges all

over the world but study on this issue is still considered an interesting venture as there has been inconsistency

in the findings of the research and most of the studies are focused on the non-financial information while there

is possibility that financial information such as financial ratios could affect the underpricing level as well.

Objectives

The objectives of this study in analyzing the factors affecting the underpricing level of the companies going

for Initial Public Offering are as follows:

To understand what an IPO is and the mechanism to apply for an IPO in Indian Market

To acquire a basic knowledge in understanding the methods used in determining the IPO price.

To analyze the significant level of the factors influencing the underpricing level of a company’s initial

return after the IPO. The factors analyzed in this study are:

a. Debt Equity Ratio

b. Earnings per share

c. Return on Investment

d. Size of the company

e. Age of the company

f. Net Issue (Percentage of shares issued) to the public

Research Hypotheses

1. H1: DER has positive significant effect towards the level of under-pricing.

2. H2: ROA has negative significant effect towards the level of under-pricing.

3. H3: EPS has negative significant effect towards the level of under-pricing.

4. H4: Age of the company has negative significant effect towards the level of under-pricing.

5. H5: Size of the company has negative significant effect towards the level of under-pricing.

6. H6: Share offering percentage to the market has negative significant effect towards the level of under-

pricing.

Scope of the Study

The scope of the study is limited to the analysis of the companies’ performance of past 5 years which

have been listed in the stock exchanges.

The study will be conducted for different ratings given to the IPO (IPO rating) based on CRISIL

(Credit Rating Information Services of India Limited).

CRISIL rating which is used in this study is the rating of 1 to 5, which indicates as follows:

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

The companies analysed are taken from the national level stock exchanges instead of the regional stock

exchanges.

Research Methodology

The research methodology use in this study is analytical and exploratory research. Historical data

which has been collected will be used to develop the research and to make inferences about the factors

affecting the condition of under-pricing in the companies being under study.

Analytical Research is the method used when the researcher has to use facts or information already

available, and analyze these to make a critical evaluation of the material.

Exploratory Research is investigation into a problem or situation which provides insights to the

researcher.

Sampling Technique

Sampling techniques employed for this research study will be Deliberate Sampling as it will be

chosen as per the CRISIL rating criteria without any further stratification in the sample classification.

Sample Selection

The selection of the samples in this research will be selecting ten (10) companies representing each

rating in the IPO Rating by CRISIL.

Data Collection

A list of companies which had gone public in India from 2007 – 2011 would be obtained from

websites providing IPO details and stock market performance.

Data Analysis

Corresponding factors which are believed to be part of the significance in the underpricing condition

will be obtained and regressed to see if there is statistically any correlation between each of those factors

toward the underpricing level of the IPO. Multiple Regression technique would be used for the data analysis

process.

Limitation

The study shall focus only on the selected companies for the period of 5 years in Indian context with

the highest capitalization based on the IPO ratings and not other than those due to the limited time.

The study will not analyze the other companies’ valuation techniques and the correlation with the

pricing condition and hence it might not reveal the true inference of the overall analysis.

Literature Review

Ardiansyah (2004), in his research paper explains that EPS and economic condition have significant

effect towards initial return and return of 15 days post IPO. Financial leverage has significant effect towards

15 days post IPO return. Company’s size effect towards initial return and return of 15 days post IPO could not

be decided.

Beatty – 1989, explains that certain auditors reputation, underwriter reputation, age, kinds of contract

have significant negative association with initial return. While share allocation percentage and indicators and

certain auditors reputation have significant positive association with initial return.

Chandradewi (2000), in her article reveals that simultaneously all independent variables have

significant effect on share price, while EPS has significant effect on share price partially.

Christy (1996), indicates that Underwriters competition has negative relation with return of 15 days

post IPO. Standard deviation of return, and company’s age have positive relation to the return of 15 days post

IPO. The rest of the variables do not have significant relation toward return of shares of 15 days post IPO.

Daljono (2000), in his article says only underwriter’s reputation and financial leverage have

significant effect on initial return.

Durukan (2002), in his research paper illustrates that company size and share issuance methods have

significant negative effect on initial return while age has significant positive effect towards initial return. The

other variables do not have any significant effect.

Imam Ghozali, in his article indicated that ROA has significantly negative effects towards the level

of underpricing at significant level 5%, while underwriter reputation & financial leverage has significant

negative effects to underpricing at significant level 10%. The rest of the variables do not have significant

effects.

Kim, Kish & Vasconcellos (2002), in their research paper reveal that share issuance method, beta

risk, subscription have significant positive effect towards initial return. Share allocation percentage,

underwriter’s quality, new share substitute and premium have significant negative relation towards initial

return. While size, age, shares standard error, risk free interest rate, dilution and covariance do not have any

significant effect.

Trisnawati (1999), in his article say company’s age is positively related to initial return and financial

leverage is positively related to return of 15 days post IPO.

Yolana and Martani (2004), in their article examine Index and ROE have significantly positive

effect toward initial return. Type of industry, company’s size has significantly negative effect towards initial

return. Underwriter’s quality does not have any significant effect towards initial return.

Techniques Of Analysis

Hypotheses testing in this study has been done in multiple regression model in order to have overall

picture about the influence among dependent variable (under-pricing) and the independent variables (ROA,

DER, EPS, age of the company, size of the company, and percentage of share allotment to the public by the

company). In order to fulfill the requirements to get good regression analysis, it would be necessary to test if

there are any violations toward the Classical Test Assumption.

1. Classic Test Theory (CTT)

Classical test theory (CTT) has been the foundation for measurement theory for over 80 years. The conceptual

foundations, assumptions, and extensions of the basic premises of CTT have allowed for the development of

some excellent psychometrically sound scales. Classical theory may be regarded as roughly synonymous with

true score theory. Generally speaking, the aim of classical test theory is to understand and improve the

reliability of psychological tests. The Classic Test Theory being used in this study is as follows:

a. Normality Test

In statistics, normality tests are used to determine whether a data set is well-modeled by a normal

distribution or not, or to compute how likely an underlying random variable is to be normally

distributed. The test being used in order to see the normality of the data is Kolmogorov-Smirnov (K-

S) test. The K-S test is distribution free in the sense that the critical values do not depend on the

specific distribution being tested. The hypothesis would be:

H0: The data follow a specified distribution.

Ha: The data do not follow the specified distribution.

The distributed data is showing that the significant level is greater than 0.05 with the regression plot

as follows:

Chart 1

Table 1

One-Sample Kolmogorov-Smirnov Test

UP Ratio

N 27

Normal Parametersa,b

Mean .3733

Std. Deviation .44070

Most Extreme Differences Absolute .215

Positive .215

Negative -.198

Kolmogorov-Smirnov Z 1.116

Asymp. Sig. (2-tailed) .165

a. Test distribution is Normal.

b. Calculated from data.

This normality test is indicating that the distribution of the residual data is already not perfectly normal. This is

shown by the PP Plot which is showing that the individual data (dots) are deviating from the diagonal line. It

indicates that there is no huge anomalies from the distributed data to the expected distribution of the data but

the anomalies are quite significant. Other than that, the Kolmogorov-Smirnov test is giving the value of 1.116

which is greater than the table value of 0.22898 (with alpha = 5% and N = 27). Since the calculated value

(1.116) is greater than the table value (0.22898), the null hypothesis is rejected which means that the data is

not normally distributed.

b. Multicollinearity Test

Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple

regression model are highly correlated. In this situation the coefficient estimates may change erratically in

response to small changes in the model or the data. Basically there are two way of detecting multicollinearity

in data being studied, which are:

a. computing correlations between all pairs of predictors. If some R are close to 1 or -1, remove one of the

two correlated predictors from the model.

b. calculating the Variance Inflation Factors (VIF) for each predictor in the data. This could be done by :

Where R2 is the coefficient of determination of a regression of explanator j on all the other

explanators. A tolerance of less than 0.20 or 0.10 and/or a VIF of 5 or 10 and above indicates a

multicollinearity problem.

The aim of testing the multicollinearity is to know if each independent variable is related to each

other linearly or not and VIF (Variance Inflation Factor) is indicating how much the variance is inflated.

Table 2

Variables Collinearity Statistics

Tolerance VIF

DER .638 1.567

EPS .312 3.205

ROA .251 3.978

Company Age .424 2.360

Net Issue to Public .482 2.073

Company Size .263 3.802

The result of the test is showing that there is no variable is having VIF value greater than 5. This

indicates that the dependent variables model (predictors) being used in this study do not show any

multicollinearity sign in the regression model.

c. Heteroskedasticity

In statistics, a collection of random variables is heteroskedastic, or 'heteroscedastic, if there are sub-

populations that have different variabilities than others. Here "variability" could be quantified by the

variance or any other measure of statistical dispersion. Thus heteroscedasticity is the absence of

homoscedasticity.

The possible existence of heteroscedasticity is a major concern in the application of regression

analysis, including the analysis of variance, because the presence of heteroscedasticity can invalidate

statistical tests of significance that assume the effect and residual (error) variances are uncorrelated and

normally distributed.

A good regression model is the one which is homoscedastic or which is not heterescedastic.

In order to detect if there is heteroscedasticity or not, it could be done by seeing the graphical plot

between the corresponding variable prediction with its residuals to know if there are any specific patterns or

not. The patterns which could emerge are:

a. If there is any specific pattern, where the data plot a specific arranged pattern (wavy, widespread and

becoming narrowly-spread), then there is heteroscedasticity.

b. If there is no specific pattern and the data is spread above and below 0 (zero coordinate) and Y axis,

then there is no heteroscedasticity.

Chart 2

Chart 3 The histogram of the heterskedasticity

From the scatterplots, it could be seen that the dots are distributed randomly below coordinate of 0

(zero) on the Y axis. From the Glejser test, it is indicating that there is no single independent variable which is

statistically significant in influecing the absolute unstandardize residual variables.

d. Autocorrelation

Autocorrelation refers to the correlation of a time series with its own past and future values.

Existence of autocorrelation could be tested using Durbin Watson test (DW – test). This test is designed to

identify the statistical significance of any positive serial correlation within the regression residuals. The

Durbin-Watson test statis tic tests the null hypothesis that the residuals from an ordinary least-squares

regression are not autocorrelated against the alternative that the residuals follow an AR1 process. The

Durbin -Watson statistic ranges in value from 0 to 4. A value near 2 indicates non-autocorrelation; a value

toward 0 indicates positive autocorrelation; a value toward 4 indicates negative autocorrelation.

Certain conditions to be followed in determining whether there is autocorrelation in a series of data

or not are as follows:

1. If DW value lies between the upper bound (dU) and (4-dU), it indicates that autocorrelation

coefficient is equal to zero, which means there is no autocorrelation.

2. If DW value is lower than the lower bound (dL), it indicates that autocorrelation coefficient is

greater than zero, which means there is positive autocorrelation.

3. If DW value is larger than (4-dL), it indicates that autocorrelation coefficient is less than zeore,

which means that there is negative correlation.

4. If DW value lies between lower bound (dL) and upper bound (dU) or lies between (4-dU) and (4-

dL), it indicates that the results of the test are inconclusive at the given level of significance. In

other words, when the Durbin-Watson statistic lies between the two critical values, then we can

neither support nor refute the claim that no statistically significant serial correlation exists within

the regression residuals.

Table 3

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 .625a .390 .208 .39231 1.490

a. Predictors: (Constant), Company Size, EPS, DER, Net Issue to Public, Company Age, ROA

b. Dependent Variable: UP Ratio

From the Durbin Watson significance table for 6 independent variables (k=6) and 1 dependent

variable with number of sample (N= 27) for 5% significant point is as follows:

dL = 0.925 and dU = 1.974

(4 – dU) = 2.026

This study is giving a result of DW coefficient = 1.49 which lies between the lower bound and the

upper bound which means that the autocorrelation existence is undecided or inconclusive at the particular

significance level.

2. Multiple Regression Analysis

Multiple Regression is a statistical method used to examine the relationship between one dependent

variable Y and one or more independent variables Xi. The regression parameters or coefficients bi in

the regression equation:

Are estimated using the method of least squares.

Table 4 Result of Classic Test:

Classic Test Finding

Normality test Data is not normal

Multicollinearity test No multicollinearity

Heteroskedasticity test Homoskedastic / Not Heteroskedastic

Autocorrelation test Inconclusive

If all the classic test findings are resulting in desired result which are:

- Data is normally distributed,

- No multicollinearity,

- No heteroskedasticity (data is homoskedastic),

- No autocorrelation among the data,

Multiple regressions using OLS (Ordinary Least Square) method could be used but since the data has

violated some of the classic tests, the multiple regression should use GLS (Generalized Least Square) method

in order to give a better interpretation towards the overall data.

In this study we are using Generalized Linear Model as it is a flexible generalization of ordinary

linear regression that allows for response variables that have other than a normal distribution.

Table 5 Result of Generalized Linear Model

Value Std. Error t-Value p-Value

(Intercept) 4.361208 1.4207883 3.069569 0.0060

DER -0.031241 0.0489515 -0.638203 0.5306

EPS 0.000112 0.0002111 0.530007 0.6019

ROA 0.009847 0.0054917 -1.793129 0.0881

Age -0.000011 0.0050198 -0.002150 0.9983

Net Issue -2.883211 0.8156287 -3.534955 0.0021

Size -0.136824 0.0616106 -2.220782 0.0381

There is no significant linear correlation between the DER and the undepricing level because the

calculated t-value is less than the table value (0.638203 < 2.086) and hence, H0 is accepted. ROA does not have significant effect on the underpricing level or in other words, there is no

significant linear correlation between the ROA and the underpricing level because the calculated t-

value is less than the table value (1.793129 < 2.086) and hence,H0 is accepted

EPS does not have significant effect on the underpricing level or in other words, there is no

significant linear correlation between the EPS and the underpricing level because the calculated t-

value is less than the table value (0.53 < 2.086) and hence, H0 is accepted.

Age of the company does not have significant effect on the underpricing level or in other words, there

is no significant linear correlation between the Age of the company and the underpricing level

because the calculated t-value is less than the table value (0.002150 < 2.086) and hence, H0 is

accepted.

Net Issue of shares to the public does not have negatively significant effect on the underpricing level

or in other words, there is no significant negative linear correlation between the Net Issue of share to

the public and the underpricing level because the calculated t-value is more than the table value

(3.534955 > 2.086) and hence, H0 is rejected.

Size of the company does not have negatively significant effect on the underpricing level or in other

words, there is no significant negative linear correlation between the Size of the company and the

underpricing level because the calculated t-value is more than the table value (2.220782 > 2.086) and

hence, H0 is rejected.

Table 6 Descriptive Statistics

Summary of

Descriptive Statistics N Min Max Mean Std. Deviation

DER 27 0.0000 8.9033 1.1959 1.9559

EPS 27 0.3200 3328.5800 206.9805 677.6470

ROA 27 1.1567 132.2467 23.0781 28.3136

Company Age 27 2.0000 102.0000 23.4074 22.7093

Net Issue to Public 27 0.0900 0.6302 0.2622 0.1346

Company Size 27 18.2062 27.0630 21.8280 2.3644

UP Ratio 27 0.0008 1.8200 0.3730 0.0849

The company which has the highest underpricing level is 1.82 or 182% on Religare Enterprises Limited and

the lowest underpricing level is on Niraj Cement Structurals Ltd for 0.0008 or 0.08%.

The highest DER is 8.9033 on Muthoot Finance Ltd and the lowest DER is 0.00 on eClerx Services

Limited.

The lowest ROA is 1.1567 which is on Punjab & Sind Bank and the highest ROA is 132.2467 which is on

eClerx Services Limited.

The minimum EPS is 0.32 on Religare Enterprises Limited and the maximum EPS is 3328.58 on eClerx

Services Limited.

The youngest company going for IPO is Nu Tek India Limited which is 2 years old and the oldest company

going for IPO is Punjab & Sind Bank which is 102 years old.

The size of the company is determined by the size of the total asset of the company. The average value of

the companies going for IPO under this study is Rs. 52,099,281,481 in the original value and 21.82 in the

natural logarithm.

The lowest percentage of shares offered to public is 9% by Coal India Ltd and the highest percentage of

shares offered to public is 63% by Bhagwati Banquets and Hotels Ltd.

Conclusion

All the variables being under study - Debt Equity Ratio, Earnings per Share, Return on Asset, Age of

the Company, Size of the Company, and Net Issue of the shares to the public – are not having any influence

towards the underpricing level. In other words, the underpricing level of these companies which are studied

under specified time and selection criteria are not determined by all those factors.

Even though on this study it is found out that the financial and non-financial variables being analyzed

are not having any significance toward the underpricing level, the past research on the similar situations have

shown significant findings on the effect of the factors toward the underpricing level and hence it is suggested

for the investors to remain taking into account the factors of DER, EPS, ROA, Age of the company, Size of

the company, and Net Issue of the shares to the public and other significant information provided in the

company’s prospectus before making any decision regarding the shares of the companies going for Initial

Public Offering.

The agenda for further extensive study on this topic for the writer would be:

1. The time period for the study should be extended more than 5 years with expectation to add more

number of samples to get better data distribution to be analyzed.

2. Independent variables to be studied could be added to factors which are external from the companies,

such as Inflation factor, Exchange rate, Interest rate, and other overall macroeconomic factors.

References

Research Articles:

1. Beatty (1989). “Auditor Reputation & Pricing of Initial Public Offerings”, The Accounting Review

Vol. 64, No. 4, October, pp 693-707

2. Chandradewi (2000). http://thesis.binus.ac.id/doc/Bab3/Bab%203_26-12_BI.pdf

3. Christy, M., I. Hasan and S.D Smith, (1996). “A note on Underwriter Competition and Initial public

offering”, Journal of Business and Accounting, 23: pp.905-914

4. Daljono. (2000). "Analisis Faktor-faktor yang Mempengaruhi Initial Return Saham yang Listing di

BEJ." Simposium Nasional Akuntansi 3: 556-572

5. Durukan, M. B. (2002). The relationship between IPO returns and factors influencing IPO

performance: case of Istanbul Stock Exchange, Managerial Finance, 28(2), 18-38

6. Kim, Byung-Ju, Richard J. Kish, and G. M. Vasconcellos, (2002). “The Korean IPO Market: Initial

Returns.” Review of Pacific Basin Financial Markets and Policies (5:2), 219-253

7. Trisnawati, Rina (1999). Pengaruh Informasi Prospektus pada Return Saham Pasar Perdana,

Simposium Nasional Akuntansi II IAI

Text Books:

1) Budi Santosa, P dan Ashari, Analisis Statistik dengan Microsoft Excel & SPSS, Andi, Yogyakarta,

2005.

2) Greg N. Gregoriou, Initial Public Offerings. An International Perspective, Elsevier, Massachuset,

USA, 2006.

3) Joseph Ogden , Frank C. Jen , Philip F. O'Connor, Advance Corporate Finance, Prentice Hall, ISBN-

10: 0130915688 | ISBN-13: 978-0130915689, Edition: 1, September 30, 2002.

4) M.Y. Khan, Financial Services, Tata Mc Graw Hill, 3rd

Edition, New Delhi, 2006

Website:

www.chittogarh.com

www.wikipedia.com

www.sebi.gov.in

www.investopedia.com

www.securities.com

www.crisil.com

www.ssrn.com