Analyzing Long-run Performance of Select Initial Public Offerings Using Monthly Returns: Evidence from India

Abstract

The anomalies in respect to initial public offering (IPO) performance in literature have stimulated multitude of research works in explaining critical issues, such as under-pricing during short-run and underperformance phenomena in the long run as is observed globally. This study endeavours to look into the long-run performance of select Indian IPOs using monthly returns following event study methodologies. Besides parametric tests, it has also applied wealth relative as a measure of performance of those IPOs. The article documents positive long-run average abnormal returns for Indian IPOs, unlike other countries. However, statistically we fail to reject the null hypothesis of zero abnormal return and therefore conclude existence of no significant long-run underperformance or over-performance in the Indian IPO stocks. In exploring the possible factors which may have bearing in determining the longrun performance of the IPOs, it is observed that book-to-market value (BMV) ratio, age of the IPO firm and aftermarket corporate measures, such as bonus issue and stock split, have significant predictive power in explaining the long-run performance.

Keywords

Underperformance Market Efficiency CAR BHAR Wealth Relatives

Introduction

Researchers have mixed observations on the long-run performance of the initial public offerings (IPOs) in different countries of the globe. Levis (1993) and Espenlaub, Gregory and Tonks (1998) documented the existence of long-run overpricing with limited explanations for the existence of this phenomenon in UK. Aggarwal and Rivoli (1990), Ritter (1991) and Loughran and Ritter (1995, 2000) also observed the same in US IPOs. Study of IPOs in Australia, on the other, provides further evidence on the poor long-run performance (Lee, Taylor & Walter, 1996). According to Hwang and Jayaraman (1992) and Kim, Krinsky and Lee (1995), performance of IPO stocks in Japan, Korea, Spain and Malaysia is better than, or equal to, non-IPO stocks in the 2–3-year post-issue period. Factors identified in respect to non-performance include underwriters’ reputation, ownership structure and bad luck (Brav et al, 2000; Carter, Frederick & Singh, 1998; Jain & Kini, 1994; Michaely & Shaw, 1994). Other financial researchers have observed that the IPOs performance is generally under-priced in the short run, though some issues tend to be overpriced. According to them, IPO underpricing has been found in nearly every country in the world. While there is a consensus that average initial under-pricing does exist in the IPO market, the aftermarket performance provides contradictory findings. An article by Ibbotson (1975) reported a negative relation between initial return and long-run share performance of IPO in the USA.

To evaluate the long-run performance of IPO, the standard literature and theory excessively rely on event study methodology, it receives its theoretical impetus from the efficient market hypothesis (EMH) as espoused and introduced by Fama, Fisher, Jensen and Roll (1969), to produce useful evidence on response of stock prices to information. According to Kothari and Warner (2005), event studies serve an important purpose in capital market-related research as the principle means of testing market efficiency. According to them, ‘systematically non-zero abnormal security returns that persist after a particular type of corporate event are inconsistent with market efficiency’. Keeping this in the backdrop, theoretical backbone of the present study is also the semi-strong form of EMH simply because we have made use of the information that is available in the public domain. However, while measuring the performance of any investment, one has to keep in mind two dichotomous aspects associated to every investment: (i) the return generated and (ii) the risks associated in the process. Hence, the evaluation in respect to the performance of equity issue, especially for IPO issue, is called a risk-adjusted measure which refers to adjusted raw return of the selected companies and the corresponding benchmark return to proxy the market return. Likewise, two most promising evaluation methods of event study are employed here in capturing the long-run IPO price performance by calculating monthly abnormal returns after adjusting for important corporate actions, such as payment of dividend, stock split and bonus issue into consideration, and ascertaining thereby the cumulative abnormal returns. In addition, the study also attempts to employ wealth relatives being adjusted with Nifty 500.

For long-run price performance, the literature is virtually divided into two most well-known abnormal return models: (i) cumulative abnormal return (CAR) and (ii) buy-and-hold abnormal return (BHAR). There has been significant debate on whether researchers should use CAR or BHAR method of calculating abnormal returns when conducting event studies. Lyon, Barber and Tasi (1999) documented that when the research question is whether or not investors earn abnormal stock return over a particular time horizon, BHAR model should be used to answer this question. The CAR approach, on the other hand, is to be employed to answer questions such as whether sample firms persistently earn abnormal return. Mitchell and Stafford (2000) strongly emphasize that BHAR method has poor statistical properties and often produces biased statistics in random samples. They raise an important observation regarding the long-term abnormal return subsequent to major corporate events. According to them, the long-run event studies can possibly help in identifying systematic mispricing of securities up to 5 years following major corporate decisions. These findings strongly negate the notion of stock market efficiency. Kothari and Warner (2005) and Ritter (1991) appreciate the use of both the CAR and BHAR method for calculating the excess return under market-adjusted return method. However, Fama (1998) is a strong supporter of CAR approach over BHAR simply because of the latter’s poor statistical properties in explaining the long-horizon performance. The advantage of using the former is that the terminal value of investing in both the IPOs and the benchmark was compared (Bessler & Thies, 2007). Barber and Lyon (1997), Kothari and Warner (1997) and many other prominent researchers have also used the BHAR methodology over CAR method. On the contrary, Fama (1998), Mitchell and Stafford (2000) and others have favoured the use of CAR method over BHAR method to analyze the long-run performance of IPOs. Ritter (1991) has argued that both the CARs and BHARs should be used to answer different questions. Following Ritter (1991), we have used both the methodologies in our study.

The existing literature on event study format warranted that we need to ascertain the abnormal or excess return first. There are many ways to calculate the excess return such as (i) mean-adjusted return model, (ii) market-adjusted return model, (iii) market model or ordinary least square (OLS) market model, (iv) capital asset pricing model (CAPM)-based abnormal return model and (v) Fama–French multifactor model. The market-adjusted return model popularly referred to as the market-adjusted excess return (MAER) model is a simple and intuitive model (Brown & Warner, 1985; Kothari & Warner, 1997) based on the abnormal return. Brown and Warner (1980) in their study have concluded that ‘a simple methodology based on the market model is both well specified and relatively powerful under a wide variety of conditions’. The problem with the market model especially in case of IPO is very acute since the IPOs do not have any information on earlier prices; the OLS parameters (α and β) are usually difficult to estimate for security over the pre-event window. For this reason, some studies have not considered the pre-event window, rather they compute market model parameters from the post-event window. This has been done by Sapusek (2000) in his study.

There has been prolonging debate on the issue of what return (daily or monthly) should be considered, especially for long-run performance analysis. Brown and Warner (1985) concluded that individual security returns suffer from certain limitations and are likely to be less normal. The daily stock return for an individual security exhibits substantial departure from normality that is not observed with monthly data. In addition, the estimation of parameters from daily data is complicated by non-synchronous trading, a complication identified by Scholes and Williams (1997) as ‘especially severe’.

In the Indian context, very few studies have been conducted for long-run performance analysis and most of them have taken a 3-year period after listing. Shah (1995) has documented a phenomenal excess return over the offer price, while Madhusoodanan and Thiripalraju (1997) in their study on IPOs listed in Bombay Stock Exchange during 1992–1995, showed that IPOs in the long run yielded higher returns compared to the negative returns recorded in its global counterparts. Sahoo and Rajib (2010) in their study during 2002–2006 reported that on an average, Indian IPOs are under-priced and long-run underperformance has also been reported. Based on the above discussions, the study has identified the following objectives: (i) to evaluate the long-run performance of those IPOs using various event study methodologies as well as wealth relatives and (ii) to identify the probable factors for explaining the long-run performance of the Indian IPO stocks.

Research Methodology

Literature on IPO emphasizes on calculating returns as the basis of performance study. There are numerous methods advocated by the researchers on how returns should be calculated. To be consistent with literature, the study has employed the methodology coined by Aggarwal, Leal and Hernandez (1993) for measuring return. Following this, monthly return for stock ‘i’ is calculated as:

R_{i t} = \frac{P_{i 1}}{P_{i o}} - 1

(1)

where R_it_{denotes monthly return for stock ‘i’, P_i₁ is the closing share price of the stock ‘i’ at the end of a month and P_i_o is the closing price of the previous month. The corresponding monthly return for the market index is similarly calculated as follows.}

R_{m t} = \frac{P_{m 1}}{P_{m o}} - 1

(2)

where R_mt_{is the monthly return for the market benchmark, P_m₁ is the closing value of the market index at the end of a month and R_mo is closing value of the market index of the previous month. While calculating the monthly returns for long-run performance of IPO, we have excluded the listing day return as has been done by Ritter (1991) in order to find the long-run performance sans any abnormal initial listing gain.}

Market-adjusted Excess Return Model

The model defines abnormal returns as the excess or surplus return on a security, adjusted for the return on the Nifty 500 index over the same period of time. The MAER model has become the obvious choice because of its simplicity in implementation and interpretation. The equation for the MAER is:

M A E R_{i t} = R_{i t} - R_{m t}

(3)

where MAER_it is the market-adjusted excess return on security i over time t; R_it is return adjusted for dividend and other corporate actions, such as bonus and stock split, on security ‘i’ during month ‘t’; and R_mt_{is return on Nifty 500 index during month ‘t’, which is used as proxy for market return.}

Market Model Abnormal Return Model

The other model we have used to analyze the post-event monthly abnormal returns is based on the market model. Following Sapusek (2000), we have estimated alpha and beta values for the stock ‘i’ over its post-issue period so that we can calculate the abnormal returns of the stock, that is, the empirical deviations of the stock returns from the returns calculated using the estimated regression coefficients. The formula for market model abnormal return (MMAR) can be written as follows:

M M A R_{i t} = R_{i t} - α_{i} + β_{i} R_{m t}

M M A R_{i t} = R_{i t} - E (R_{i t})

(4)

where R_it_{is the adjusted raw return of the security i during time t and E(R_it)¹ denotes expected rate of return for stock i during period i. Conventionally, abnormal returns calculated with the above two models (MAER and MMAR) are operationalized using the following methods.}

Cumulative Abnormal Return Method

Ascertainment of abnormal returns is central for measuring long-run aftermarket performance. The study has incorporated the abnormal return following MAER and MMAR models in order to estimate security-specific abnormal returns. Using the monthly returns following both the equations (3) and (4), we calculate the average abnormal return (AAR) as follows:

A A R_{t} = \frac{1}{n} \sum^{​} ​ A R_{i t}

(5)

The cumulative average abnormal return (CAAR), that is, aftermarket performance from event month q to event month s, is the summation of the average benchmark-adjusted abnormal return.

C A R_{q, s} = \sum^{​} ​ A A R_{t}

(6)

Fama (1998) justifies the use of CAR method for its statistical properties. Ritter (1991) has also used this methodology, and in fact. it is the most popular method in the literature.

Buy-and-hold Abnormal Return Method

Since we are employing two abnormal return models with and without adjusting for market coefficient, that is, beta and alpha, we have two different sets of BHARs. Subsequently, BHAR equation using MAER model for measuring long-run performance is given below.

Figure 1.

Source: Authors’ own.

B H A R_{i t} = \prod^{​} ​ [1 + R_{i t}] - \prod^{​} ​ [1 + (R_{m t})]

(7)

where R_it_{and R_mt are calculated using equations (1) and (2), respectively.}

Following Barber and Lyon (1997), we have computed the BHAR using the market model parameter alpha and beta and named it as MMAR BHAR. The following formula is used for evaluating long-run performance of select IPOs.

B H A R_{i t} = \prod^{​} ​ [1 + R_{i t}] - \prod^{​} ​ [1 + E (R_{i t})]

(8)

where R_it_{and R_mt are calculated using equations (1) and (4), respectively.}

The following diagram (Figure 1) will give an insight of the methods used in the study for measuring long-run performance of the Indian IPOs using adjusted monthly returns.

Methods of Wealth Relative

The performance of IPOs can also be evaluated by using the concept of wealth relative as has been done by Levis (1993) and can be measured at different time intervals. Generally, a wealth relative of greater than one indicates better performance of an IPO over the market index, while the wealth relative less than one indicates underperformance. We have used the method to ascertain the long-run performance of IPOs in India. Wealth relative can be calculated using simple average return during 60 months and corresponding 60-month average benchmark (market) return. Following is the formula for wealth relative using simple average return.

W R_{i t} = \frac{1 + a v e r a g e 5 - y e a r r e t u r n o n I P O s}{1 + a v e r a g e 5 - y e a r r e t u r n o n m a r k e t (i n d e x)}

W R_{i} = \frac{1 + \frac{1}{60} \sum^{​} ​ R_{i t}}{1 + \frac{1}{60} \sum^{​} ​ R_{m t}}

(9)

where WR_i is the wealth relative of the company i over a 5-year period. R_it_{and R_mt are calculated using equations (1) and (2), respectively. The above formula of wealth relative is based on simple return as has been employed by Levis (1993).}

Alternatively, wealth relative can also be calculated using holding period return (HPR). Ritter (1991) used this HPR, defined as $\prod^{} (1 + R_{i t})$ . Similarly, return on market is defined as $\prod^{} (1 + R_{m t})$ . The formula employed for measuring wealth relative using HPR is given below:

W R_{i t} = \frac{1 + \frac{1}{60} \prod^{​} ​ R_{i t}}{1 + \frac{1}{60} \prod^{​} ​ R_{m t}}

(10)

where WR_it is the wealth relative of the company i for period t. R_it_{and R_mt are calculated using equations (1) and (2), respectively.}

Testing of Various Abnormal Return Models and Their Statistical Significance

Before we proceed to frame our hypotheses, we must throw some light on the test statistic that we wish to employ in our study. The test statistic employed in our study is one sample Student’s t-test. We framed the following null and alternative hypotheses using CAR for both the MAER and MMAR models to test the statistical significance, if any, of the long-run aftermarket price performance of the Indian IPOs.

H₀ : There exists no abnormal return in the long run, that is, CAR_(q,s) = 0.

H₁ : There exists abnormal return in the long run, that is, CAR_(q,s) ≠ 0.

To test the hypotheses, we compute the associated t-statistic as follows:

t = \frac{\overset{─}{C A R}}{\frac{S {(C A R)}_{(q, s)}}{\sqrt n}}

(11)

where S is the cross-sectional standard deviation of CAR_(q,s) across companies.

Similarly, to test the BHAR using both the MAER and MMAR models, following null and alternative hypotheses have been constructed:

H₀ : There exists no abnormal return in the long run, that is, BHAR = 0.

H₁ : There exists abnormal return in the long run, that is, BHAR ≠ 0.

To test the hypotheses, we compute the associated t-statistic as follows:

t = \frac{\overset{─}{B H A R}}{\frac{S (B H A R_{i, t})}{\sqrt n}}

(12)

Data and Study Period

This study examines the long-run price performance (from 1 to 5 years) of selected Indian companies which went public for the first time in the primary market for raising their corpus during the study period of 1999–2007. The study analyzes post-IPO performance over the 5-year period up to 2012. The reason for opting the year 1999 is important because it was from this very year a new mechanism for pricing of new issue called book building was introduced.

Selection of Sample IPO Companies Total number of companies gone through IPO process during 1999–2007 (including FPO and OFS) 183 Less: FPO and OFS (offer for sale) (29) Eligible companies gone through exclusive IPO process 154 Less: Exclusion of IPO firms due to non-availability of reliable secondary market data (18) Remaining companies 136 Less: IPOs excluded on account of non-availability of complete information relating to IPO characteristics (21) Companies finally available for selection 115 Final number of selected companies using SRSWOR 47

The CNX Nifty 500 is considered as our benchmark index which comprises a well-diversified portfolio representing different sectors of Indian industry. It is to be noted that to calculate the parameter of market model, the application of broad-based stock index (such as S&P 500) is preferred to proxy the market returns (Mackinlay, 1997). For these reasons, we preferred CNX Nifty 500 as our benchmark index instead of other popular index maintained by National Stock Exchange (NSE).

Table 1.

Descriptive Statistics along with t-test (using monthly CAR under MAER model)

Return Window Parameter	12 Months	24 Months	36 Months	48 Months	60 Months
Mean	9.57	1.59	0.67	9.20	24.99
SE of Mean	9.57	12.53	13.95	15.84	16.33
5% Trimmed Mean	4.28	–2.61	–4.01	7.82	24.46
Median	–4.77	–1.58	1.81	9.60	20.22
Standard Deviation	65.39	85.95	95.65	108.64	111.97
Skewness	1.25	.824	0.88	0.265	0.00
Kurtosis	2.49	2.315	2.30	0.152	0.40
Computed t-value	1.003	0.128	0.049	0.581	1.530
p-value	0.321	0.899	0.961	0.564	0.133

Source: Authors’ own.

Aftermarket Performance of IPOs: Empirical Findings

CAR Method

As per the convention, in the application of MAER model, the abnormal return is calculated using the difference between the return observed for individual company with the corresponding market return. The market return here means the return generated by Nifty 500. It is used as proxy to mirror the market return.

It is observed from Table 1 that the CAAR is positive but varies considerably from first year to next four years. The concept of trimmed mean is important since in this method of averaging it removes largest and smallest values before calculating the mean. The 5 per cent trimmed mean is just 4.28 per cent in the first year and in the following 2 years, it is negative only to revive in the fourth and fifth year with a moderate to high positive return, respectively. We thus find that there is severe underperformance up to 36 months followed by over-performance during the next 24 months. Looking at the t-values and the corresponding p-values, it can, however, be inferred from the MAER model that the null hypothesis of zero abnormal return cannot be rejected in any year. This signifies that we should reject the alternative hypothesis which advocates the presence of significant abnormal return in the long run. Now, we may try to explain the results obtained by employing the MMAR model using monthly returns.

Table 2 shows that average return are positive throughout the time period of 60 months but the standard deviation figure is very high. The median value however portrays mostly negative values. The results depicted in the above Table 2 portray almost similar results under the MAER model using monthly return data. Despite the positive average abnormal return, we cannot reject the null hypothesis (that long-run CAARs = 0) and thereby confirm the presence of no significant abnormal return in the long run.

Table 2.

Descriptive Statistics along with t-test (using monthly CAR under MMAR model)

Return Window Parameter	12 Months	24 Months	36 Months	48 Months	60 Months
Mean	9.57	1.59	0.67	9.20	24.99
SE of Mean	9.57	12.53	13.95	15.84	16.33
5% Trimmed Mean	4.28	–2.61	–4.01	7.82	24.46
Median	–4.77	–1.58	1.81	9.60	20.22
Standard Deviation	65.39	85.95	95.65	108.64	111.97
Skewness	1.25	.824	0.88	0.265	0.00
Kurtosis	2.49	2.315	2.30	0.152	0.40
Computed t-value	1.003	0.128	0.049	0.581	1.530
p-value	0.321	0.899	0.961	0.564	0.133

Source: Authors’ own.

BHAR Method

The BHAR methodology has also been receiving widespread acceptance of late for measuring the long-run performance of IPOs. To reiterate, two sets of BHAR have been calculated in the study. First, we report the results of the one which does not consider the market model parameters alpha and beta; we named the model as MAER BHAR.

The method of BHAR is usually accused of serious statistical deficiencies in its approach as can be seen from the results reported in Table 3. It is to be noted that the BHAR methodology has poor statistical properties than the CAR methodology. Unlike the CAR method, BHAR exhibits substantial positive abnormal return even in the long run. The figure of skewness and kurtosis also exhibits substantial departure from normality. However, if 5 per cent trimmed mean is considered, positive return results only in the first year and in the next 4 years it is all negative. Worse still is the result of median value which is all negative across the 5-year evaluation period. The results thus indicate the presence of aftermarket underperformance over the 5-year period. However, null hypothesis advocating absence of long-run abnormal return cannot also be refuted here under this method implying existence of no statistically significant abnormal return in the long run.

Table 3.

Descriptive Statistics along with t-statistics for Long-run Performance (using monthly BHAR methodology under MAER model)

Return Window Parameter	12 Months	24 Months	36 Months	48 Months	60 Months
Mean	7.81	4.09	0.28	14.46	23.35
SE of Mean	8.83	10.82	12.65	14.60	15.61
5% Trimmed Mean	2.76	0.82	–1.97	13.31	23.44
Median	–6.27	1.64	–9.03	15.34	16.66
Standard Deviation	60.57	74.24	86.77	100.12	107.03
Skewness	1.26	0.688	0.486	0.221	0.08
Kurtosis	2.02	1.11	0.298	–0.61	0.33
Computed t-value	0.891	0.378	0.022	0.990	1.496
p-value	0.378	0.707	0.982	0.327	0.142

Source: Authors’ own.

The other methodology that we have employed to calculate the BHAR is by using MMAR, where we have estimated the market model parameters, alpha and beta, to calculate the BHAR. This BHAR calculation is thus conceptually different from the BHAR reported earlier.

The results seem almost identical with the outcomes under the MAER BHAR method. Moreover, high p-values (even more than 0.10) here again fail to lend statistical support in favour of abnormal returns in any of the different long-term return windows.

Table 4 above shows that there exist positive average abnormal return and an incremental growth in return is also observed which itself suggest that average long-run IPO performance though clearly demonstrates the existence of positive average returns, parametric statistical test fails to accept the hypothesis of non-zero abnormal return and therefore reinforces the conclusion that there exists no significant long-run abnormal return (over-performance or underperformance) in the Indian IPOs. Mean long-run IPO returns under the four alternative methods are depicted in Figure 2.

Table 4.

Descriptive Statistics along with t-statistics (using monthly BHAR methodology under MMAR model)

Return Window Parameter	12 Months	24 Months	36 Months	48 Months	60 Months
Mean	27.60	39.28	39.43	18.17	69.43
SE of Mean	20.77	35.90	49.18	34.60	76.99
5% Trimmed Mean	4.33	–2.35	–11.38	–14.08	–7.64
Median	–13.15	–13.17	–45.39	–56.67	–59.21
Standard Deviation	142.40	246.17	337.18	237.26	527.81
Skewness	3.39	4.52	5.72	3.07	5.80
Kurtosis	13.07	20.18	36.06	12.22	37.04
Computed t-value	1.329	1.094	0.802	0.525	0.902
p-value	0.190	0.280	0.427	0.602	0.372

Source: Authors’ own.

Figure 2.

Source: Authors’ own.

Wealth Relatives

Here, we have reported wealth relatives of the sample firms over a period of 5 years, that is, for the time interval of 60 months. Table 5 depicts that a sizable proportion of companies exhibit a wealth relative higher than unity. To be specific, as many as 55 per cent (26 companies out of 47) companies reported positive wealth relative greater than one indicating the superior performance corresponding to relative benchmark index using simple average return over a 5-year or 60-month period. While 19 companies have registered wealth relative less than unity indicating that they are unable to beat the market during this period, only two companies have reported to have wealth relative equivalent to unity suggesting the performance of these companies is at par with the benchmark.

Table 5.

Wealth Relative Based on Simple Average Return of the Sample IPO Companies over 5-year Period

Wealth Relative (WR)	WR > 1	WR = 1	WR < 1
No. of Companies	26	2	19

Source: Authors’ own.

Table 6.

Wealth Relative Based on Holding Period Return of the Sample IPO Companies over 5-year Period

Wealth Relative (WR)	WR > 1	WR = 1	WR < 1
No. of Companies	20	0	27

Source: Authors’ own.

The wealth relative picture is different when we incorporate HPR for computation. This model is a multiplicative model and here, the emphasis is on buy and hold experience contrary to the earlier version where only simple average return of the sample companies was emphasized. Table 6, however, depicts an altogether different version. In this model of wealth relative, only 20 companies have been able to beat the market and the rest 27 companies fail to do so.

Factors Influencing the Long-term Underperformance of IPOs

In order to get a possible explanation for the long-run performance of the Indian IPO stocks, we have resorted to multiple regression analysis using the same analogy followed by Ritter (1991) and Kiyamaz (2000). Here, the dependent variable is the 5-year monthly CAR under MMAR model (CAR_60m). Six explanatory variables have been considered based on their theoretical explanation and literature as follows:

Initial Abnormal Return (IR): This variable is used following the existing literature (Ibbotson, 1975; Levis, 1993; Ritter, 1991) and is considered important since we are interested to know whether any relationship exists between initial return and its long-run counterpart or not. Here, the initial listing day return is computed using MMAR.

Log of Issue Size (LOGISSUE): Natural logarithm of the issue size computed by multiplying offer price with number of shares offered through IPO is used to incorporate the size effect (Brav et al, 2000; Fama & French, 1993).

BMV: The ratio is used as a proxy for expected growth potential of the IPO firms (Brav et al., 2000; Fama & French, 1993).

Age (LOGAGE): Natural logarithm of one plus the difference between the year of IPO and the year of incorporation [Log (1+age)] is used similar to Ritter (1991) as an extent of operational maturity of the IPO firms.

Log of Total Assets (LOGASSET): Natural logarithm of total assets at the time of IPO is also considered as a proxy for firm size following Kiyamaz (2000).

Bonus/Split (BONSPLIT): We have used this variable to control the impact of any important corporate action such as issue of bonus share and stock split on the long-run IPO performance. For this, a dummy variable taking 1 if the IPO company issues bonus share to its existing shareholders or split the stock during the post-IPO evaluation period and 0 otherwise is incorporated.

The equation thus can be stated as follows:

\begin{matrix} C A R_{60 m} = α + β_{1} {(I R)}_{i} + β_{2} {(L O G I S S U E)}_{i} + \\ β_{3} {(L O G A G E)}_{i} + β_{4} {(B M V)}_{i} + \\ β_{5} {(L O G A S S E T)}_{i} + \\ β_{6} {(B O N S P L I T)}_{i} + ε_{i} \end{matrix}

(13)

Table 7 give us the basics of correlation matrix, we begin with calculating the Pearson correlation coefficients to study the strength and direction of the association between any two variables. Looking at the correlation matrix, we do find some significant bi-variate correlations between the variables we considered relevant for the study.

Table 7.

Correlation Matrix: IPO Performance and Firm Characteristics

	CAR_60m	IR	LOGAGE	LOGASSET	LOGISSUE	BMV	BONSPLIT
CAR_60m	1
IR	0.009 (0.954)	1
LOGAGE	–0.198 (0.181)	0.321*(0.028)	1
LOGASSET	0.113 (0.449)	–0.119 (0.426)	0.042 (0.780)	1
LOGISSUE	–0.134 (0.370)	–0.247^# (0.094)	0.022 (0.883)	0.675** (0.000)	1
BMV	0.292* (0.046)	–0.243^# (0.100)	0.172 (0.247)	0.523** (0.000)	0.241 (0.102)	1
BONSPLIT	0.262^# (0.075)	0.004 (0.980)	–0.080 (0.595)	–0.011 (0.944)	0.079 (0.598)	–0.163 (0.273)	1

Source: Authors’ own.

Note: (1) P-values are reported in parentheses.

(2) Values marked with ^#, * and ** indicate significance at the 10%, 5% and 1% level.

Cross-sectional Regression Results

Table 8 reports results of the regression equation (13) with 5-year monthly CAR under MMAR model as the dependent variable.

Adjusted R-squared of 0.232 indicates that all the explanatory variables collectively explain 23.2 per cent of the variation in the long-run IPO performance, which seems moderate but higher than the R-squared value (0.216) reported in Sahoo and Rajib (2010). The F-stat with its low p-value of 0.01 indicates that there is a low probability that the variation explained by the model is due to chance, and hence provides sufficient evidence that the regression equation is statistically significant to explain variation in the dependent variable. Variance inflation factor (VIF) value of less than 5 for each explanatory variable is within the acceptable limits, whereas the Durbin–Watson statistic is documented at 1.845 (close to the 2.0), and hence show the absence of multicollinearity and autocorrelation, respectively.

Unstandardized coefficient estimates of the independent variables and their corresponding P-values reported in Table 8 indicate that long-run IPO performance can significantly be explained by BMV, LOGAGE and BONSPLIT at less than 5 per cent level of significance. LOGAGE, however, shows negative relation with the long-run performance meaning greater the magnitude of the operational history of an IPO firm, lower is the return and vice versa. On the other hand, significant positive coefficient of BMV indicates that lower BMV ratio (high growth optimism for IPO) results in long-run underperformance consistent with the hypothesis of overoptimism and fad story (Ritter, 1991).

Table 8.

Regression Results: Long-run IPO Performance and Firm Characteristics

	Unstandardized Coefficients		Standardized Coefficients	t	ρ-value	Collinearity Statistics
	B	Std Error	Beta	t	ρ-value	VIF
Constant (α)	270.45	134.531		2.010	0.051
IR	0.272	0.277	0.149	0.981	0.332	1.375
LOGISSUE	–62.561	38.523	–0.305	–1.624	0.112	2.116
LOGAGE	–58.363	28.511	–0.294	–2.047	0.047	1.239
BMV	183.25	69.668	0.445	2.630	0.012	1.711
LOGASSET	17.498	30.455	0.120	0.575	0.569	2.617
BONSPLIT	90.399	35.566	0.336	2.542	0.015	1.046
R	0.576	R-squared	0.332	Adjusted R-squared		0.232
Durbin–Watson	1.845			F-stat (p-value)		3.315 (0.010)

Source: Authors’ own.

Our model findings also indicate that initial return, LOGISSUE and LOGASSET are not certainly significant in explaining the 5-year monthly CAR. However, looking at the positive coefficient of initial return (0.272), it can be stated that contrary to the literature, long-run underperformance or over-performance dynamics of Indian IPOs are directly related to their short-run overpricing or under-pricing, respectively.

Conclusion

In this article, we have re-examined the aftermarket performance of the select Indian IPOs over the period 1999–2007 using monthly returns. The study found that long-horizon BHAR is significantly right skewed and much more pronounced than that of CAR. According to the opinion of the savants in this field, CARs help circumvent the problems of extreme skewness and kurtosis relative to BHARs and therefore are helpful in double-checking any conclusion presented by BHAR results. This finding of our study is also consistent with the existing literature. Application of both the techniques also provides a test of robustness for the results, which are critical to understand the performance direction as both the techniques are found to complement each other. However, it may be concluded that long-run price performance is difficult and perfidious as it involves lot of underlying factors which change as the time horizon increases. It is even more difficult because the long-run performance is very sensitive to the choice of methods and the benchmark employed. In this article, we have performed the analysis using a number of alternative approaches, but benchmarked only against S&P CNX Nifty 500 and found positive long-run average abnormal returns for Indian IPOs, unlike most other countries. However, using CAR under both the MAER and MMAR model, underperformance is pronounced only up to 36 months followed by over-performance. Unlike the CAR method, BHAR exhibits substantial positive abnormal return up to 24 months followed by underperformance. Nonetheless, over the 60 months, both the approaches result in substantial over-performance. Unlike international experiences, the Indian markets thus show some resilience in the long run. Moreover, we find that the long-run performance of IPO is very sensitive to the choice of methods and is deceitful.

However, when we use parametric statistical test, we fail to reject the null hypothesis of zero abnormal return and therefore conclude that there is no significant long-run underperformance or over-performance in case of the Indian IPOs. These results in support of zero aftermarket performance shed some light on the informational efficiency of the Indian IPO market. Our study has also employed wealth relatives to ascertain the long-run performance of IPOs and found that the long-run performance of Indian IPOs is not as distressful as reported in the international literature for other countries at least in case of wealth relative involving simple average return.

Finally, it may well be concluded that a firm’s age, BMV ratio, aftermarket corporate actions, such as bonus and splits, can be used to predict the long-run performance of the Indian IPOs. However, initial IPO return, firm size and issue size may not work as guiding factors but long-term IPO investors are advised to analyze overoptimistic BMV ratio at the time of IPO cautiously.

Footnotes

List of the Sample IPO Companies

Sl. No.	Name of Company	Listing Date	Sl. No.	Name of Company	Listing Date
1	Hughes Software	04 November 1999	25	Prithvi Information Solutions Limited	16 November 2005
2	HCL Technologies Limited	06 January 2000	26	Bombay Rayon Fashions Limited	05 December 2005
3	Pritish Nandy Communications Limited	11 December 2000	27	Triveni Engineering and Industries Limited	13 December 2005
4	I-Flex Solutions Limited	28 June 2002	28	Educomp Solutions Limited	13 January 2006
5	Divi’s Laboratories Limited	12 March 2003	29	Royal Orchid Hotels Limited	06 February 2006
6	Maruti Udyog Limited	09 July 2003	30	Gujarat State Petronet Limited	16 February 2006
7	Patni Computer Systems Limited	25 February 2004	31	INOX Leisure Limited	23 February 2006
8	Petronet LNG	26 March 2004	32	GVK Power & Infrastructure Limited	27 February 2006
9	Biocon Limited	07 April 2004	33	Gitanjali Gems Limited	10 March 2006
10	Power Trading Corporation of India	07 April 2004	34	J. K. Cement Limited	14 March 2006
11	Datamatics Technologies Limited	07 May 2004	35	NITCO Tiles Limited	21 March 2006
12	New Delhi Television Limited	19 May 2004	36	Adhunik Metaliks Limited	05 April 2006
13	Tata Consultancy Services Limited	25 August 2004	37	Kewal Kiran Clothing Limited	13 April 2006
14	IndiaBulls Financial Services Limited	24 September 2004	38	Plethico pharmaceuticals Limited	5 May 2006
15	Bharati Shipyard Limited	30 December 2004	39	Emami Limited	3 August 2006
16	Jet Airways (India) Limited	14 March 2005	40	Development Credit Bank Limited	27 October 2006
17	Jai Prakash Hydro-Power Limited	18 April 2005	41	Fiem industries	19 October 2006
18	3i Infotech Limited	22 April 2005	42	Info Edge (India) Limited	21 November 2006
19	Gokaldas Exports Limited	27 April 2005	43	Cairn India Ltd	09 January 2007
20	India Infoline Limited	17 May 2005	44	Power Finance Corporation Limited	23 February 2007
21	Shoppers Stop Limited	23 May 2005	45	SMS Pharmaceuticals Limited	28 February 2007
	HT Media Limited	01 September 2005	46	MindTree Consulting Limited	07 March 2007
23	Sasken Communication Technologies Limited	09 September 2005	47	Mudra Lifestyle Limited	09 March 2007
24	Shree Renuka Sugars Limited	31 October 2005

Note

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