Final Report

On

Factors Affecting First Day returns from

an IPO

In partial fulfillment of course

Econometrics for Finance

SUBMITTED TO

Prof. Mallikarjun

SUBMITTED BY:

Hiren Haria (061114)Ajay Hingane (061204)Mohit Bansal (061228)

Nikhil Gore (061232)

Introduction

The number of IPOs that are flooding the streets are increasing day by day. In spite of

this they are commanding a substantial price premium on their offer price. The first day

returns of IPOs like Vishal Retail, RPL and DLF are no long history. The investors are

taking out money from the secondary markets and are investing into the primary markets.

These IPOs suck a lot of liquidity from the markets, for ex. the recent IPOs of DLF and

ICICI Bank suck nearly Rs. 9000 cr. each from the market. In spite of all this investors

are showing tremendous faith in them and the result is the over-subscription rates and the

listing prices.

Whether the premium that these IPOs command is because of the under-pricing of the

IPOs or some other factors is yet to be determined. In fact SEBI is thinking of putting a

cap on the prices of these IPOs on the listing days to have a control on the volatility. In

order, to understand the factors that influence the first day returns of an IPO, number of

theories has been put forward. The literature that is cited below is one amongst them

which tries to determine the factors that influence the price premium of IPOs in the

Medical Diagnostics and Device industry.

We would like to extend this study to the Indian markets covering a sample of all the

industries and companies which have come up with an IPO since 2005, as it marks the

boom phase of the Indian markets. Also, some additional variables which were not

covered under this literature have been checked to determine whether they influence the

price premiums these IPOs command.

Literature Review

The study examines the factors that influence the extent of price premium over book

value in Initial Public Offerings (IPO). The study focuses on the Medical Diagnostics and

Devices industry only. The author has cited two reasons for limiting himself to this

industry only. First, there has been increasing awareness in recent years of the need to

control for industry effects in management research. And secondly, there is often a

significant association between industry affiliation and initial returns in IPOs.

According to the author the variables that affect the price premium on the IPO are

basically dependent on the risk related factors of the company going for an IPO. Factors

such as the Debt to Equity ratio of the company (D/E ratio), promoter holdings in the

company prior and after the IPO, underwriter reputation, stage of development of the

company and number of risk related factors mentioned in the prospectus affect the price

premium.

The findings of the study state that there is positive correlation between firm’s stage of

development and price premium, whereas, there is negative relationship between leverage

i.e. the D/E ratio, and the extent of management’s reduction in stock holdings. Also, as

per the findings the relationship between underwriter’s reputation and price premium is

not significant due to the low t-value.

Methodology

We took a simple random sample of 50 from all the companies that have come up with an

IPO since 2005. Four dependent variables have been identified to test the model. Closing

price is chosen as it takes into account the sentiments of the investors who have been

denied the shares in the initial allotment. The independent variables in our case are D/E

ratio, reduction in Promoter’s holding, underwriter reputation, age of the company,

number of uses of proceeds and issue size.

Out the sample 50 firms selected for developing the model, 9 firms had to be dropped.

The reason was non-availability of data for those firms. The reasons are enumerated as

follows:

The company is yet to be listed.

The issue was a follow-on issue.

The company was de-listed.

The financial leverage for Deccan Aviation was negative.

Therefore new 9 firms were randomly selected from the population (excluding firms

those were already selected) to account for loss of data.

A multiple regression was run to develop a model and determine how much these factors

influence the premiums that the IPOs command. Each dependent variable was separately

regressed against the independent variables.

Variables

Dependant Variables: Four variables have been identified as follows:

(Listing Price/Issue Price)

(Closing Price on the Day of Listing/Issue Price)

(Listing Price - Issue Price)/Issue Price

(Closing Price on the Day of Listing - Issue Price)/Issue Price

The data for listing price, issue price and the closing price on the first day has been

obtained from the NSE website.

Independent Variables:

Underwriter Reputation: It is treated as a categorical variable. The reputation of the

underwriter has been decided on the basis of the credit rating given by leading credit

rating firms like CRISIL and S&P for Indian and Foreign firms respectively. The rating

criteria for CRISIL and S&P have been assumed to be same as CRISIL is part of S&P. If

the rating of some firm was not available, it was judged on the basis of its past record like

loan defaults. The firm with bond rating of AAA has been assigned a value of ‘1’, below

AAA has been assigned a value of ‘0’ and the firms having more than one lead

underwriter have been assigned a value on the basis of the underwriter with higher credit

rating.

Financial Leverage: The Debt-Equity ratio as on the date of filing the prospectus has

been calculated from the information available in the prospectus.

Issue size: The issue size ‘in Rs. million’ has been obtained from the red herring

prospectus of each firm.

Age of the company: The age of the company in years is the number showing the

difference between the year of listing and the year of founding. The data has been

obtained from the red herring prospectus.

Number of uses of proceeds: It has been obtained from the red-herring prospectus.

Change in the stock ownership of the promoter: It is calculated as the ratio of

promoters’ holding after and before the issue of the IPO. The pre-IPO and post-IPO

promoters’ holdings have been obtained from the red herring prospectus.

Hypothesis

Let β0 = Constant term in the regression (c)

βi = Coefficients of independent variables, i = 1,2,3,4,5,6.

The hypothesis that we are testing in this paper are:

H.1a) Ratio of first day closing price to issue price depends upon all the factors detailed

above.

i.e. All βis are not equal to zero

H.1b) Ratio of first day closing price to issue price depends upon one or more of the

factors detailed above.

i.e. At least one βi is not equal to zero

H.2a) Ratio of listing price to issue price depends upon all the factors detailed above.

i.e. All βis are not equal to zero

H.2b) Ratio of listing price to issue price depends upon one or more of the factors

detailed above.

i.e. At least one βi is not equal to zero

H.3a) Percentage change from issue price to first day closing price depends upon all the

factors detailed above.

i.e. All βis are not equal to zero

H.3b) Percentage change from issue price to first day closing price depends upon one or

more of the factors detailed above.

i.e. At least one βi is not equal to zero

H.4a) Percentage change from issue price to listing depends upon all the factors detailed

above.

i.e. All βis are not equal to zero

H.4b) Percentage change from issue price to listing depends upon one or more of the

factors detailed above.

i.e. At least one βi is not equal to zero

Regression Run for testing of above mentioned hypothesis

Independent Variables:

Age of the Company (age)

Financial Leverage i.e. Debt/Equity Ratio (FL)

Underwriter’s Reputation (gradin)

Issue Size in Rs. Million (IS)

Number of Uses of Proceeds (NOUP)

Ratio of Promoter’s holding after IPO/ Promoter’s holding before IPO (PSAPSB)

Model I

Dependent Variable: First Day Closing Price/Issue Price of the IPO (CPIP)

For this regression CPIP is first regressed with all the independent variables that are age of the company, financial leverage, size of the issue, grading of the underwriter, objects of the issue, and the ratio of promoters holding before and after the issue.

The results of this regression are shown in Table 1- Run 1. One can clearly see that the results of this step are not significant statistically.

Further we dropped variables one-by-one on the basis of their t-values to get a better picture. Even this exercise doesn’t yield any statistically significant independent variable till Run 5.

Run 6 that take only underwriters grading as an independent variable produces t-stat that is statistically significant and also F-stat for the model comes out to be statistically significant. It must be noted that underwriter’s reputation in our case is a

dummy variable and is used as ‘one’ for reputed underwriter and ‘zero’ for not very reputed underwriter. Hence, we reject the null hypothesis H1a and accept the null Hypothesis H1b.

Table 1:Regression Run For Dependent variable CPIP

  VARIABLE C AGE FL GRADIN IS NOUP PSAPSB R-squared Adjusted R-squared F-statistic

Run 1

COEFFICIENT 1.2246 0.0022 -0.0161 -0.4253 0.0000 0.0278 0.2694 0.1239 0.0016 1.0133

STD. ERROR 0.7150 0.0104 0.0373 0.1907 0.0000 0.0486 0.8422    

T-STAT. 1.7128 0.2146 -0.4318 -2.2303 -0.1696 0.5723 0.3198    

Run 2

COEFFICIENT 1.2573 0.0012 -0.0174 -0.4296   0.0285 0.2411    

STD. ERROR 0.6808 0.0082 0.0361 0.1869   0.0479 0.8163 0.1233 0.0237 1.2375

T-STAT. 1.8468 0.1418 -0.4834 -2.2990   0.5951 0.2953    

Run 3

COEFFICIENT 1.2544   -0.0174 -0.4284   0.0287 0.2634    

STD. ERROR 0.6731   0.0357 0.1846   0.0474 0.7922 0.1229 0.0449 1.5762

T-STAT. 1.8637   -0.4881 -2.3203   0.6051 0.3325    

Run 4

COEFFICIENT 1.4620   -0.0167 -0.4262   0.0268      

STD. ERROR 0.2493   0.0353 0.1827   0.0466   0.1207 0.0634 2.1054

T-STAT. 5.8641   -0.4744 -2.3324   0.5760      

Run 5

COEFFICIENT 1.4407     -0.4387   0.0275      

STD. ERROR 0.2432     0.1793   0.0462   0.1164 0.0788 3.0966

T-STAT. 5.9236     -2.4461   0.5961      

Run 6

COEFFICIENT 1.5529     -0.4326          

STD. ERROR 0.1530     0.1778       0.1098 0.0912 5.9175

T-STAT. 10.1513     -2.4326            

 

Variable have been dropped

Model II

Dependent Variable: Listing Price of the IPO/Issue Price of the IPO (LPIP)

For this regression LPIP is first regressed with all six independent variables.

The results of this regression are shown in Table 2- Run 1. One can clearly see that the results of this step are not significant statistically.

Further we dropped variables one-by-one on the basis of their t-values to get a better picture. This exercise doesn’t yield any statistically significant independent variable for all runs. . Hence, we reject both the null hypothesis’ H2a and H2b.

Table 2: Regression Run For Dependent variable LPIP

  VARIABLE C AGE FL GRADIN IS NOUP PSAPSB R-squared Adjusted R-squared F-statistic

Run 1

COEFFICIENT 1.0460 0.0019 -0.0071 -0.1520 0.0000 0.0052 0.2884 0.0553 -0.0765 0.4195

STD. ERROR 0.4669 0.0068 0.0244 0.1245 0.0000 0.0317 0.5500    

T-STAT. 2.2401 0.2774 -0.2907 -1.2208 -0.5517 0.1651 0.5243    

Run 2

COEFFICIENT 1.0733 0.0020 -0.0071 -0.1505 0.0000   0.2794 0.0547 -0.0527 0.5093

STD. ERROR 0.4318 0.0067 0.0241 0.1228 0.0000   0.5412    

T-STAT. 2.4858 0.2927 -0.2950 -1.2255 -0.5739   0.5162    

Run 3

COEFFICIENT 1.0885     -0.1567 0.0000   0.2813 0.0505 -0.0115 0.8149

STD. ERROR 0.4159     0.1195 0.0000   0.5304    

T-STAT. 2.6170     -1.3116 -0.5758   0.5303    

Run 4

COEFFICIENT 1.3024     -0.1572 0.0000     0.0446 0.0040 1.0980

STD. ERROR 0.1008     0.1186 0.0000        

T-STAT. 12.9227     -1.3257 -0.4463        

Run 5

COEFFICIENT 1.3008     -0.1655       0.0406 0.0206 2.0318

STD. ERROR 0.0999     0.1161          

T-STAT. 13.0241     -1.4254            

Model III

Dependent Variable: CPIPIP = (First day closing price – Issue Price) / Issue Price

For this regression CPIPIP is first regressed with all six independent variables. The results of this regression are shown in Table 3- Run 1. One can clearly see that the results of this step are not significant

statistically. Further we dropped variables one-by-one on the basis of their t-values to get a better picture. This exercise yields only gradin as statistically significant independent variable for first five runs. For Run1 to Run 5 coefficient of gradin is statistically significant yet the F-stat for all these runs is not significant.

Run 6, in this case show some relation of independent variable underwriter’s reputation with the dependent variable. Also F-stat for the model comes out to be statistically significant. . Hence, we reject the null hypothesis H3a and accept the null Hypothesis H3b.

Regression Run For Dependent variable CPIPIP

  VARIABLE C AGE FL GRADIN IS NOUP PSAPSB R-squared Adjusted R-squared F-statistic

Run 1

COEFFICIENT 1.2246 0.0022 -0.0161 -0.4253 0.0000 0.0278 0.2694 0.1239 0.0016 1.0132STD. ERROR 0.7150 0.0104 0.0373 0.1907 0.0000 0.0486 0.8422     T-STAT. 1.7128 0.2146 -0.4318 -2.2303 -0.1696 0.5723 0.3198    

Run 2

COEFFICIENT 1.2573 0.0012 -0.0174 -0.4296   0.0285 0.2411 0.1233 0.0237 1.2375STD. ERROR 0.6808 0.0082 0.0361 0.1869   0.0479 0.8163     T-STAT. 1.8468 0.1418 -0.4834 -2.2990   0.5951 0.2953    

Run 3

COEFFICIENT 1.2544   -0.0174 -0.4284   0.0287 0.2634 0.1229 0.0449 1.5761STD. ERROR 0.6731   0.0357 0.1846   0.0474 0.7922     T-STAT. 1.8637   -0.4881 -2.3203   0.6051 0.3325    

Run 4

COEFFICIENT 1.4620   -0.0167 -0.4262   0.0268   0.1207 0.0634 2.1054STD. ERROR 0.2493   0.0353 0.1827   0.0466       T-STAT. 5.8641   -0.4744 -2.3324   0.5760      

Run 5

COEFFICIENT 1.4407     -0.4387   0.0275   0.1164 0.0788 3.0966STD. ERROR 0.2432     0.1793   0.0462       T-STAT. 5.9236     -2.4461   0.5961      

Run 6

COEFFICIENT 1.5529     -0.4326       0.1097 0.0912 5.9175STD. ERROR 0.1530     0.1778           T-STAT. 10.1513     -2.4326            

Model IV

Dependent Variable: LPIPIP = (Listing Price of the IPO – Issue Price of the IPO) / Issue Price of the IPO

For this regression LPIPIP is first regressed with all six independent variables.

The results of this regression are shown in Table 4- Run 1. One can clearly see that the results of this step are not significant statistically.

Further we dropped variables one-by-one on the basis of their t-values to get a better picture. This exercise doesn’t yield any statistically significant independent variable for all runs. . Hence, we reject both the null hypothesis’ H4a and H4b.

Regression Run For Dependent variable LPIPIP

  VARIABLE C AGE FL GRADIN IS NOUP PSAPSB R-squared Adjusted R-squared F-statistic

Run 1

COEFFICIENT 0.0460 0.0019 -0.0071 -0.1520 0.0000 0.0052 0.2884 0.0553 -0.0765 0.4195STD. ERROR 0.4669 0.0068 0.0244 0.1245 0.0000 0.0317 0.5500     T-STAT. 0.0985 0.2774 -0.2907 -1.2208 -0.5517 0.1651 0.5243    

Run 2

COEFFICIENT 0.0733 0.0020 -0.0071 -0.1505 0.0000   0.2794 0.0547 -0.0527 0.5093STD. ERROR 0.4318 0.0067 0.0241 0.1228 0.0000   0.5412     T-STAT. 0.1698 0.2927 -0.2950 -1.2255 -0.5739   0.5162    

Run 3

COEFFICIENT 0.0958   -0.0080 -0.1520 0.0000   0.2830 0.0529 -0.0313 0.6279STD. ERROR 0.4206   0.0237 0.1214 0.0000   0.5356     T-STAT. 0.2277   -0.3377 -1.2517 -0.5037   0.5284    

Run 4

COEFFICIENT 0.0885     -0.1567 0.0000   0.2813 0.0505 -0.0115 0.8149STD. ERROR 0.4159     0.1195 0.0000   0.5304     T-STAT. 0.2128     -1.3116 -0.5758   0.5303    

Run 5

COEFFICIENT 0.3024     -0.1572 0.0000     0.0447 0.0040 1.0985STD. ERROR 0.1008     0.1186 0.0000         T-STAT. 3.0004     -1.3257 -0.4463        

Run 6

COEFFICIENT 0.3008     -0.1655       0.0406 0.0206 2.0318STD. ERROR 0.0999     0.1161           T-STAT. 3.0117     -1.4254            

The tabulated values of t-stat and F-stat used for our purpose are given in the table below:

Used Tabulated StatisticsConfidence Interval 95%

K t-Stat F-Stat for n = 501 2.01 4.42 2.01 3.23 2.01 2.84 2.01 2.575 2.01 2.346 2.01  

Conclusion:

After analyzing various factors that might influence the first day returns from an IPO, it

can be safely said that the IPO returns mostly depend on the sentiments of the public and

the market moreover follows a random walk. Though, after running various regression

models, the only variable that had some influence on the returns was the reputation of the

Underwriter responsible for the proceedings of an IPO. The variables such as Financial

Leverage, Age, Issue Size, Number of uses of Proceeds, and the promoter’s stake dilution

has practically no influence on the returns of an IPO in the Indian context for the selected

period.

Due to the time constraint other variables like the state of the market i.e. either a bull or a

bear run were not included in the regression model. For further studies, this aspect can be

taken into consideration by using proxies like the market turnover, F&O turnover, etc for

determining the state of the market. The period for monitoring would be right from the

first day of the issue to the listing day. The effect of inclusion of this variable in the

model should then be studied to see if it influences the returns.

Reference

Research Papers

Abdul M.A. Rasheed, Deepak K. Datta, and Ravi R. Chinta (October 1997).

“Determinants of Price Premiums: A study of Initial Public Offerings in the Medical

Diagnostics and Devices Industry”, Journal of Small Business Management, p11-23.

Websites

www.moneycontrol.com

www.capitalmarket.com

www.nseindia.com

www.site.secuities.com

www.crisil.com