Performance of IPOs of Indian Companies Backed by Private Equity

Abstract

We examine 616 Indian initial public offerings (IPOs), including 116 IPOs backed by private equity (PE), between 2000 and 2016, to test whether PE-backed IPOs perform better than non-PE-backed IPOs in the short run as well as in the long run in terms of cumulative abnormal returns (CARs). We also examine the impact of the PE firm nationality on post-IPO performance. Consistent with the existing literature, we find underperformance for all IPOs, on an average, within 1 year. However, PE-backed IPOs have lower degree of underperformance than non-PE-backed IPOs. We also find that size, liquidity and leverage have a positive impact on the post-IPO performance after the financial crisis, whereas issue amount and capital issue year are negatively correlated to CARs before and during the crisis. We also find significant effects of PE firm nationality on CAR development. IPOs backed by India-dedicated PE firms perform best, while those backed by foreign PE firms perform worst and even underperform non-PE-backed IPOs. IPOs by foreign PE firms perform better if they co-invest with India-dedicated PE firms.

Keywords

Private Equity IPOs IPO Performance India

Introduction

Private equity (PE) companies can exit portfolio companies primarily in three ways: the company can go public via an initial public offering (IPO), be acquired or simply fail. Linking post-IPO performance in India to PE ownership and investor’s nationality is important for at least three reasons.¹

At first, India’s PE landscape is very vibrant. The PE industry has invested more than USD 103 billion between 2001 and 2014 in the country (Sheth, Singhal, Rajan, & Bhaskaran, 2016). India has become one of the leading destinations for PE with venture capital investments being the fastest growing PE destination worldwide between 2004 and 2008 (Annamalai & Deshmukh, 2011; Smith, 2015). Second, exit by PE via IPO is one of the most popular routes adopted by venture capitalists, amounting to 20 per cent in 2013 and 39 per cent between 2009 and 2012 (Pandit, Tamhane, & Kapur, 2015). Third, the IPO market also provides an appealing setting for examining the behaviour of PE sponsors as it involves higher information asymmetrics compared to other exit strategies Michala, D. (2016). In the fourth quarter of 2016, India’s IPO deal volume made it to the fourth position worldwide, after China, United States of America and Japan (Pinelli, Kelley, & Choi, 2015). Fourth, foreign investors account for 35–40 per cent of the total PE deals in India and thus, support much of India’s growth.

Using a data set of 616 Indian IPOs including 116 PE-backed ones, we examine the 1-year CARs of IPOs going public from 2000 to 2016. The data includes: The IPO price and share price development, issue volume, incorporation year, capital issue year, investors’ nationality, industry type, and general financial results such as sales, debt-to-equity ratio, current ratio, and detailed shareholdings at the IPO date and 1 year after.

Despite the very well-researched theoretical foundations of post-IPO performance and the role of PE, little is known about the impact of PE ownership on post-IPO performance in India. Prior research for the Indian market yields valuable insights but has mostly focused on venture capital. Thus, there is a dearth of studies, which cover PE-backed IPO performance in India. In addition, to the best of our knowledge, no research has been conducted that examines the impact of PE’s nationality on post-IPO performance. To shed some light on this topic, our comparative study examines the performance of PE companies in a relevant emerging market.

This article investigates the first year aftermarket IPO performance of PE companies in India. In the beginning, we test whether PE-backed IPOs perform better than non-PE-backed IPOs and, therefore, whether PE investors work as a quality certification for IPOs. Thereby, we compare the CAR of PE-backed IPOs to that of non-PE-backed IPOs. Further, we examine the impact of the PE nationality on the IPO performance of PE-backed IPOs.

The results of this study demonstrate underperform-ance for all IPOs in the first year after going public. First, PE-backed IPOs have lower degrees of underperformance than non-PE-backed IPOs. Capital issue year post-financial crisis (2013–2016), size, liquidity and leverage, among other factors, have a significant positive impact on post-IPO performance, whereas issue amount and capital issue year before (2005–2008) and during financial crisis (2009–2012) are negatively related to CARs. Second, by accessing the PE subsample by investors’ nationality (foreign, domestic and co-investment), we find significant effects of nationality on CAR development. IPOs backed by PE co-investments outperform the benchmark index. IPOs backed by domestic PE companies have lower degrees of underperformance than foreign PE companies that perform the worst in our sample. However, regression analysis reveals that India-dedicated IPOs are, to a higher degree, negative correlated to CAR development than foreign IPOs.

The article is organized as follows: The second section gives an overview of the literature and the Indian PE industry. The third section introduces the data and methodology. The fourth section presents our results. The fifth section concludes the study.

Literature Review

IPO Performance

The theoretical foundation of IPO performance is very well-researched and divided into underpricing as short-term effect and underperformance as long-term effect.

First, on the first days, IPO underpricing is well documented by literature (Chi & Padgett, 2005; Ibbotson, Sindelar, & Ritter, 1994; Purnanandam & Swaminathan, 2004; Ritter & Welch, 2002). Stocks on the IPO date are offered at below the market price. Short-term underpricing can be explained by taking information asymmetries and signalling them into consideration. The best firms, having superior internal information about their growth prospects, use underpricing to communicate their type as they can redeem the costs of underpricing from subsequent issues. Thus, first-day underpricing is used to signal quality (Allen & Faulhaber, 1989).

Second, in the long term, IPOs underperform compared to their benchmark (Loughran & Ritter, 1995; Ritter, 1991; Schultz, 2003). Associated empirical evidence from some other countries confirms poor long-run performance after IPO (Aggarwal, Leal, & Hernandez, 1993; Bergström, Nilsson, & Wahlberg 2006; Loughran, Ritter, & Rydqvist, 1994; Minardi, Ferrari, & AraújoTavares, 2013).

Ritter (1991) shows that worst IPO performance is concentrated on young growth companies. Gompers and Lerner (2003) add that IPO performance is associated with small and high-growth companies. Brav, Geczy, and Gompers (2000) find underperformance is concentrated principally in small issuing businesses with low book-to-market ratios. They conclude that the stock returns subsequent to equity issues reflect a more persistent return pattern in the vast set of publicly listed companies. Hoechle and Schmidt (2008) find IPO underperformance within 1 year after IPO but do not find similar underperformance thereafter. They find that differences in firm characteristics between IPO and mature companies account for IPO underperformance. In other words, underperformance is not specific for IPOs and affects small and growing firms in general. In the long run, underperformance might result from inefficient markets. Investors might buy stocks overpriced beyond their true fundamental value due to over-optimism (Bergström et al., 2006; Ritter, 1991). Underperformance might result from IPO market timing. Ritter and Loughran (1995) argue that favourable market conditions are an important factor for going public. Thus, companies take advantage of high valuations in hot markets when the stock market is substantially overvalued. Hence, underperformance is the result of inefficient markets (Loughran & Ritter, 1995; Ritter & Welch, 2002). However, Schultz (2003) characterizes this phenomenon as ‘pseudo’ market timing. Firms cannot predict market peaks and just follow peers that go public at high valuations. Alti (2005) argues that high offer price realizations trigger a large number of subsequent IPOs. Hoechle and Schmidt (2008) argue that IPO underperformance is the result of different characteristics between IPO and mature companies. In line with existing evidence, we expect to find systematic underpricing and underperformance in this study of Indian IPOs.

Private Equity Impact on IPO Performance

Various studies on the performance of venture capital/PE-backed IPOs exist with different geographical foci. In the short term, with regards to the signalling theory, PE-backed firms show less underpricing as PE sponsors reduce information asymmetries between investors and issuing firms. Thus, PE backing signals high-quality companies (Megginson & Weiss, 1991). In the long term, Bergström et al. (2006) show underperformance for PE and non-PE-backed IPOs. They find that PE-backed IPOs outperform their benchmark across all time horizons. Likewise, Jain and Kini (1994) find superior post-IPO operating performance for PE-backed firms. Similarly, Minardi et al. (2013) find that PE-backed IPOs have higher average CAR than non-PE-backed IPOs in Brazil. Moreover, a study by Brav and Gompers (1997) shows that PE-backed IPOs outperform non-PE-backed IPOs over 5 years in the US market. According to Katz (2009), PE-backed companies experience stronger long-term stock price performance after IPO as compared to non-PE-backed firms. Superior performance is achieved through professional ownership, tighter monitoring and reputation of the PE firms. According to Beatty and Ritter (1986), Titman and Trueman (1986), Carter, Dark, and Singh (1998), Jain and Martin (2005), Carter and Manaster (1990), Michaely and Shaw (1995) and Minardi et al. (2013), the existence of prominent and high standing underwriters diminishes information asymmetry and therefore diminishes underpricing in PE-backed firms which in turn raises the success rate of IPOs.

In terms of PE firm’s nationality, foreign and local PE firms have different investment outcomes because of the differences in the legal environment as well as cultural and geographical differences. Dai, Jo, and Kassicieh (2011), in his study based on Asian PE markets, finds that joint ventures of foreign and local PE firms are about 5 per cent more likely to exit successfully. She, thus, concludes that the presence of foreign investors helps to monitor the managers, which leads to better corporate performance. Similarly, Humphery-Jenner and Suchard (2013) find in a Chinese context that the presence of a syndication partner increases the likelihood of corporate success. Unlike the previous studies, for example, Dai et al. (2011) and Humphery-Jenner and Suchard (2013), we do not find any study in the emerging markets like India.

PE-backed IPOs in India

In the short term, Clarke, Khurshed, Pande, and Singh (2016) find that underpricing of Indian IPOs averages 23 per cent. This degree of underpricing is in line with Pande and Vaidyanathan (2009). Moreover, Pande and Vaidyanathan (2009) show declining degrees of underpricing over their study’s time horizon. According to Sahoo and Rajib (2010), Indian IPOs are underpriced by 47 per cent. In contrast, Sehgal and Singh (2008) reveal a very high level of underpricing of Indian IPOs with initial returns of 99 per cent. According to Gohil and Vyas (2015), PE-backed IPOs are characterized by a lower degree of underpricing than non-PE-backed IPOs. However, the difference is not significant.

In the long term, Sahoo and Rajib (2010) find evidence in favour of IPO underperformance. IPOs significantly underperform the market benchmark within the first year; however, they do not find underperformance thereafter. Their findings are in line with Gohil and Vyas (2015). They show that PE-backed IPOs show less underperformance than PE-backed IPOs within the first 180 days, as well as within 1 year. After two years of an IPO, PE-backed IPOs still perform better than non-PE-backed IPOs; however, the difference is not significant.

According to Raghupathy and Thillairajan (2016), venture-capital-backed IPOs perform better than non–venture-capital-backed IPOs. Also, in the regression analysis, Raghupathy and Thillairajan (2015) cannot attribute the performance to the involvement of VC companies.

Data and Methodology

Sample and Data Sources

To test the impact of PE on post-IPO performance, we collect data from four main sources. First, we identify IPOs through the Venture Intelligence database over the period 1998–2016 to create a novel data set of the near universe of PE investments in India. Second, we use the Prowess database to collect operating performance parameters and additional IPO information. Third, we merge data on stock returns from the Bombay Stock Exchange (BSE; https://www.bseindia.com/) with the data set. Fourth, we extract market capitalization data from Capitaline.²

The study comprises of 616 IPOs from 2000 to 2016 corresponding to a total issue volume of ₹1,145 in billion. We use a data set of 500 (81%) non-PE-backed and 116 (19%) PE-backed IPOs listed on the BSE. The 116 PE-backed IPOs are made by PE firms with different nationalities: co-investment 35 (30%), foreign 32 (28%) and India-dedicated 49 (42%). Table 1 provides the sample distribution by IPO type and investor’s nationality; Table 2 provides descriptive statistics.

Table 1.

Sample Distribution by IPO Type and Investor Nationality

	Total	Non-PE	PE	PE Subsample
				Co-investment	Foreign	India-dedicated
n	616	500	116	35	32	49
in %	100	81	19	30	28	42

Source: The authors.

Note: This table provides an overview of the sample by IPO type (PE versus non-PE) and PE sponsor nationality in the PE subsample.

Table 2.

Descriptive Statistics

	Issue Amount	Age	Sales	Debt-to-Equity Ratio	Current Ratio	Total Assets
Panel A: Total Sample
Mean	1,971	14	3,541	1.08	6.92	19,250
Min	13	0	0	0.01	0.01	12
Max	98,040	107	212,586	17.22	485.00	770,112
Panel B: PE Subsample
Mean	3,231	14	5,916	0.88	2.07	12,876
Min	27	2	20	0.01	0.09	117
Max	32,172	70	45,358	4.83	17.75	180,798

Source: The authors.

Note: This table provides descriptive statistics for the total sample and the PE subsample. We performed regression which has taken care of all extreme values. However, in the descriptive analysis, we have presented the outliers as it is. The aim of presenting descriptive analysis is to identify data structure as there is a possibility of the presence of extreme values in the data set. Two companies, namely Kirloskar Multimedia Ltd and VCU Data management Ltd, were found with extreme values.

Overall, PE-backed IPOs are large in terms of issue volume by 87 per cent and sales by 92 per cent; nevertheless, non-PE-backed IPOs have, on an average, a higher asset base by 59 per cent.

The total issue volume peaks in 2010 with a total of ₹166,051 million and is mainly concentrated between 2006 and 2010. PE-backed IPOs are larger in terms of issue amount with a mean of ₹3,231 million compared to ₹1,724 million for non-PE-backed IPOs. The PE mean issue amounts by PE firm’s nationality are as follows (in ₹in million): co-investments 5,269, foreign 2,604 and India-dedicated 2,308. In absolute terms of total issues’ volume, PE co-investments have more than twice the size with about ₹142,276 million of PE foreign investments with ₹67,712 million. Total issue amount of PE India-dedicated offers is ₹96,970 million.

PE-backed IPOs have, on an average, higher sales with ₹5,916 million compared to non-PE-backed IPOs amounting to ₹3,079 million. The mean total assets is ₹19,249 million. Total assets value ranges from ₹12 million to ₹770,112 million. Non-PE-backed companies have a higher mean total asset base with ₹20,532 million than PE-backed companies with ₹12,875 million. PE co-investments have ₹18,911 million a higher mean total asset base than PE foreign companies with ₹11,355 million and PE India-dedicated ones with ₹9,865 million.

We have complete data on 581 of the 616 IPOs between 2000 and 2016. In 2007, the number of companies going for IPO was highest with 89 IPOs in 2007 including PE and non-PE backed IPOs. The number of IPOs for PE-backed firms reached a high in 2010 with 19 firms going public. Incorporation year ranges from 1939 to 2010. On an average both PE- and non-PE-backed IPOs are 14 years old at the IPO date. Age is a variable accounting for the time until a company goes public. It is calculated as the difference between incorporation year and capital issue year.

The data set comprises 510 (83%) companies from a non-finance background, 56 (9%) companies from a non-banking finance industry sector and 13 (2%) companies with a banking background. A total of 37 (6%) IPOs are not attributed to one of the following three industries.

PE-backed IPOs have with a debt-to-equity ratio of 0.88 lower leverage than non-PE-backed IPOs with a ratio of 1.08. PE-backed IPOs show with 2.07, on an average, a lower current ratio than non-PE-backed IPOs with 6.92. The high current ratio of over 3 of non-PE-backed firms might indicate an inefficient use of current assets, bad financing or worse management of working capital.

CAR Calculation

The objective of this study is to compare the performance of IPOs backed by PE against non-PE-backed companies. We measure the performance within 1 year after the IPO by calculating CARs as strongly recommended by Fama (1998) and Mitchell and Stafford (2000). CARs are the sum of abnormal returns (ARs). ARs are calculated on a daily basis and are cumulatively aggregated for the first year corresponding to 250 trading days. CARs are calculated according to Equation (1):

C A R_{i, T} = \sum_{t = 0}^{T} A R_{i, T}

(1)

ARs are the excess return of a single stock compared to its benchmark index. They are estimated according to Equation (2). We use the Bombay Stock Exchange Sensitivity Index (BSE Sensex) as the benchmark index. ARs, on individual stock-level, are calculated as the difference of the daily stock and the BSE Sensex return (Minardi et al., 2013).

A R_{i, T} = R_{i, T} - R_{B S E, T}

(2)

We calculate individual stock returns (Ri, t) on a daily basis according to Equation (3). Pi, t represents the closing price of the stock and Pi, t–1 represents the stock closing price the prior day. Returns are respectively calculated for the BSE Sensex. Here, i represents the return on ith stock.

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

(3)

Methodology

To analyse whether or not the presence of PE investors influences CAR, this study uses three regression models with CAR as the dependent variable. IPOs in the total sample are equally weighted.

In the first stage (Equation (4)), we use the basic model in which we adjust for fixed effects with the following variables: PE as dummy variable, three dummy variables for capital issue year grouped, and the square root of age.³ In the second stage (Equation (5)), we adjust the model for the following performance indicators: logarithm of total assets, asset growth within 1 year, logarithm of current ratio, logarithm of sales, logarithm of debt-to-equity ratio and logarithm of issue amount. In the third stage (Equation (6)), we adjust the regression model for years in which the market is ‘hot’ and the industry type, which differentiates between banking and non-banking deals.

\begin{matrix} C A R i = α + β 1 \times P E i + β 2 \times \\ C a p i t a l i s s u e y e a r (2005 - 2008) \\ + β 3 \times C a p i t a l i s s u e y e a r (2009 - 2012) \\ + β 3 \times C a p i t a l i s s u e y e a r (2013 - 2016) \\ + β 4 \times A g e + ε i \end{matrix}

(4)

\begin{matrix} C A R i = α + \dots + β 5 \times T o t a l a s s e t s \\ + β 6 \times A s s e t g r o w t h + β 7 \times C u r r e n t r a t i o \\ + β 8 \times S a l e s + β 9 \times D e b t t o e q u i t y r a t i o \\ + β 10 \times I s s u e a m o u n t + ε i \end{matrix}

(5)

\begin{matrix} C A R i = α + \dots + β 11 \times H o t y e a r \\ + β 12 \times N o n b a n k i n g & f i n a n c e i n d u s t r y \\ + ε i \end{matrix}

(6)

For the PE-backed subsample, the following three regressions, which are weighted by market capitalization, are estimated. CAR is included as depended variable. In the first stage (Equation (7)), we use the basic model with the following independent variables: PE foreign and PE India dedicated are dummy variables with value one, if there is an investment by a foreign or India-dedicated PE company, and zero otherwise. Age is a variable accounting for the time until a company goes public. We use the square root of age. In the second stage (Equation (8)), we add the logarithm of the debt-to-equity ratio and the square root of the promoters’ total share. With the promoters’ total share’, we adjust for the proportional ownership share of the PE investor. In the third stage (Equation (9)), we account for different industry characteristics by adding five dummy variables for various sectors.

\begin{matrix} C A R i = α + β 1 \times P E i f o r e i g n \\ + β 2 \times P E I n d i a d e d i c a t e d \\ + β 3 \times A g e + ε i \end{matrix}

(7)

\begin{matrix} C A R i = α + \dots + β 4 * D e b t t o e q u i t y r a t i o \\ + β 5 * T o t a l p r o m o t o r s c o r e + ε i \end{matrix}

(8)

\begin{matrix} C A R i = α + \dots + β 6 \times E n g i n e e r i n g & c o n s t r u c t i o n \\ + β 7 \times H e a l t h c a r e & l i f e s c i e n c e s \\ + β 8 \times I T & I T E S + β 9 \times M a n u f a c t u r i n g \\ + β 10 \times M e d i a + ε i \end{matrix}

(9)

Equation (4) studies the temporal impact on returns. Equation (5) examines the impact of size, growth, liquidity, leverage and investment. Equation (6) has been structured to study the impact of PE deals and investment in NBFC. We formulate Equation (7) to measure the impact of nationality of PE investment. Furthermore, Equation (8) presents the effects of promoters’ score and leverage. Lastly, Equation (9) examines the effects of industries other than NBFCs. Through Equations (4)–(9), β is the slope coefficient measures the effects of various accounting and other indicators on CARs.

Results

Univariate Analysis: Equal Weighting of IPOs

IPOs have, on an average, a negative CAR development, independently of whether they are PE or non-PE backed. PE-backed IPOs perform better than non-PE-backed IPOs after 1 year of going public. Thus, PE-backed IPOs show underperformance to a lower degree. Our findings are in line with existing literature. First, research concurs that there is long-term underperformance (Loughran & Ritter, 1995; Ritter, 1991; Schultz, 2003). Second, our results document better post-IPO performance for PE-backed IPOs as suggested by Katz (2009), in general, and Sahoo and Rajib (2010) for the Indian market.

Figure 1 shows the CAR development by PE- and non-PE-backed IPOs for the first 250 trading days, which equals a period of 1 year.

Figure 1.

Source: The authors.

PE-backed India-dedicated companies experience, compared to PE-backed, co-investment and PE-backed foreign IPOs, stronger long-term stock price performance within 1 year. Only PE India-dedicated backed IPOs outperform the benchmark index with a positive CAR evolution over 230–250 trading days with 4.16 per cent. PE co-investment and non-PE-backed IPOs have a slightly negative CAR with –5.04 per cent and –7.27 per cent over the range of 230–250 trading days. Foreign PE-backed IPOs have the highest degree of underperformance with negative CAR of –18.49 per cent over the range of 230–250 trading days.

Figure 2 shows the CAR development by non-PE-backed IPOs to PE-backed IPOs by investors’ nationality.

Figure 2.

Source: The authors.

Table 3 supplements this graphical analysis with univariate difference tests.

Table 3.

Univariate Difference Tests (Equal Weighting)

Range	Non-PE (%)	PE (%)	All Samples (%)	Non-PE (%)	PE Co-investment (%)	All Samples (%)
0–20	2.41	3.96	2.70**	2.41	2.45	2.41
90–110	–2.17	1.72	–1.40***	–2.17	7.55	–1.53***
230–250	–7.27	–5.00	–6.79	–7.27	–5.04	–7.11
Range	Non-PE	PE Foreign	All Samples	Non-PE	PE India-dedicated	All Samples
0–20	2.41	4.85	2.55*	2.41	4.46	2.59**
90–110	–2.17	–8.24	–2.57***	–2.17	4.47	–1.54***
230–250	–7.27	–18.49	–8.09***	–7.27	4.16	–6.08***

Source: The authors.

Notes: 1. This table depicts CARs in different time periods (measured in days) and the results of two sample t-tests with unequal variances.

2. All IPOs are equally weighted in this analysis.

3. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels for two-tailed t-tests, respectively.

Univariate Analysis: Market Capitalization Weighting of IPOs

Figure 3 presents the CAR development by PE- and nonPE-backed IPOs for the first 250 trading days, weighting the IPOs by market capitalization.

Figure 3.

Source: The authors.

Table 4 shows results of univariate difference tests in the same setting.

Table 4.

Univariate Difference Tests (Weighting by Market Capitalization)

Range	Co-investment (%)	Foreign (%)	All Samples (%)	Co-investment (%)	India-dedicated (%)	All Samples (%)
0–20	3.69	11.25	7.30***	3.69	0.96	2.10***
90–110	12.83	8.13	10.38***	12.83	2.46	6.53***
230–250	21.75	6.95	14.15***	21.75	17.97	19.32
Range	Foreign	India-dedicated	All Samples
0–20	11.25	0.96	5.03***
90–110	8.13	2.46	4.67***
230–250	6.95	17.97	13.41***

Source: The authors.

Notes: 1. This table depicts CARs in different time periods (measured in days) and the results of two sample t-tests with unequal variances.

2. All IPOs are weighted by their respective market capitalization in this analysis.

3. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels for two-tailed t-tests, respectively.

First-day market capitalization affects the post-IPO performance in the PE subsample negatively. Firms with lower market capitalization within the 0th–33th percentile outperform the BSE Sensex. They show stronger post-IPO performance than companies with market capitalization between 33th–66th and 66th–100th percentile that underperform the benchmark index.

The mean first-day market capitalization differs among investors’ nationality. As described in section ‘Sample and Data Sources’, PE co-investments have, on an average, more than twice the market capitalization than that of PE foreign-backed and PE India-dedicated IPOs. We find that PE India-dedicated IPOs perform better than PE co-investments and PE foreign-backed IPOs by equally weighted CAR calculation. However, the poor performance of PE co-investment can only be attributed to the co-ownership structure as high market capitalization affects returns negatively. CAR weighted by market capitalization gives stocks with higher market capitalization greater influence over the performance of the index and vice versa. Weighted by market capitalization, co-investments experience stronger performance than foreign and India-dedicated IPOs (Figure 4). Co-investments outperform the BSE Sensex CAR by 21.75 per cent over 230–250 trading days, whereas India-dedicated PEs outperform by 17.97 per cent. Foreign-backed PEs outperform by 6.95 per cent over the same period.

Figure 4.

Source: The authors.

Univariate Analysis: Firm Characteristics

Hoechle and Schmidt (2008) argue that IPO underperformance results from different firm characteristics between IPO and mature companies. Thus, we perform univariate tests on relevant firm characteristics to examine their effect on CARs. We also use these variables in our multiple regression analysis in the following subsection.

Capital Issue Year

First, IPOs of PE and non-PE-backed companies with capital issue year post-financial crisis between 2013 and 2016 outperform the BSE Sensex by 66.91 per cent after 230–250 days in terms of CAR development. Second, IPOs, pre-financial crisis with capital issue year between 2000 and 2004, outperform as well the BSE Sensex by 11.30 per cent after 230–250 days. Third, and in contrast, IPOs with capital issue year between 2005–2008 and 2009–2012 reveal underperformance in the first year after IPO with –20.37 per cent and –24.25 per cent. We draw the conclusion that IPOs are more affected by the crisis than the BSE Sensex. The findings are in line with Minardi et al. (2013). She finds that Brazilian IPOs, as an example of emerging market, are on an average more affected by economic crisis than their benchmark index. During financial crisis, investors prefer safe and diverse assets and thus, avoid smaller assets that feature higher risks.

Between 2000 and 2012, PE-backed IPOs show higher CAR evolution than non-PE-backed IPOs, whereas non-PE-backed IPOs perform better between 2013 and 2016. Results are statistically significant. We argue that PE-backed IPOs are considered as relatively safe investments and, thus, perform better during the financial crisis. Essentially, due to PE ownership, better monitoring and corporate governance mechanism are in place.

Incorporation Year

Young companies that are incorporated after 2005 outperform the BSE benchmark index 230–250 days after IPO by 22.26 per cent. In contrast, we find IPO underperformance for companies that are incorporated before 2005 by –9.37 per cent. Nevertheless, we document the opposite effect for PE-backed IPOs. PE-backed IPOs that are incorporated before 2005 perform –1.52 per cent better than their peers that are incorporated after 2005 with –13.95 per cent. For non-PE-backed companies, we observe underperformance by –11.17 per cent over 230–250 days after IPO for companies that are incorporated before 2005. Also, non-PE-backed IPOs outperform by 36.30 per cent over 230–250 days. For PE-backed companies, we document underperformance for both, that is, companies incorporated before and after 2005. Nonetheless, PE-backed IPOs incorporated after 2005 have higher degrees of underperformance.

Firm Age

We find that the best post-IPO performance is concentrated in young companies. We find declining degrees of underperformance with increasing age for firms that are more than 7 years old. Companies aged between 0 and 7 years perform better than companies that are older at the IPO date. Young companies aged between 0 and 7 years only slightly underperform the BSE Sensex with –0.38 per cent CAR evolution after 230–250 days. Older companies show higher degrees of underperformance after 230–250 days; however, the degree of underperformance is declining. We find for companies aged between 7 and 15 years underperformance of –9.84 per cent. Established in a period of 15–20 years before the IPO date, firms have –7.81 per cent lower degrees of underperformance, whereas companies aged more than 20 years, with –6.46 per cent, exhibit slightly lower degrees of underperformance.

Issue Amount

We account for the effects of liquidity on post-IPO performance by testing for issue amount. Companies that are characterized by lower offer amount have higher post-IPO performance. Issue amount is negatively correlated with CAR development. Having created a panel and grouped companies by size of the issue amount, we find companies with issue amount between 0th and 33rd percentile outperform by 28.46 per cent after 230–250 days. In contrast, companies with issue amount between 33rd–66th and 66th–100th percentile underperform by –25.52 per cent and –13.87 per cent after 230–250 trading days. PE-backed IPOs outperform non-PE-backed within any time range and independently of the issue amount. Results are significant at 1 per cent and 5 per cent levels except for issue amount in the 66th–100th percentile with time range of 230–250 trading days. These findings are consistent with the notion that PE investors take advantage of asymmetric information. PE investors might offer a smaller company share at the IPO date to the public if PE investors expect positive growth prospects. Thus, companies with low issue volumes tend to experience high post-IPO performance.

Total Assets

We account for the effects of size on post-IPO performance with total assets as an alternative measure. IPOs backed by companies with asset base between the 0th and 33rd percentile outperform the BSE Sensex by 6.77 per cent within the first year. For IPOs backed by companies with asset base between the 33rd–66th and 66th–100th percentiles, we find underperformance by –13.00 per cent and –11.57 per cent within the first year. The findings are in line with the effects of issue amount on CAR evolution. Small companies, characterized by small issue volume and low asset base, perform better.

Industry

We distinguish between three broad types of companies. The sample is divided into non-finance, non-banking finance companies and banking companies. We find superior CAR evolution for companies backed by banking companies. After one year of the IPO, banking companies outperform the BSE Sensex by 3.35 per cent. In contrast, non-finance and non-finance & banking companies underperform at the BSE Sensex by 7.26 per cent and –6.36 per cent, respectively. For the PE subsample, we analyse the CAR performance by the industrial or service sector. The PE subsample is divided into 6 industry groups that are composed of at least 10 stocks. PE CAR performance depends on the industry type. The healthcare and life science (HCLS) sector have the strongest CAR evolution within the first year.

Results and Discussion

All IPOs

We analyse the first-year performance of PE- and non-PE-backed IPOs. Table 5 presents the results.

Table 5.

Regression Analysis for the CAR of All IPOs

Model (Equally Weighted)	1. Basic Model	2. Adjusted for Operating Performance	3. Adjusted for Hot Year and Industry
Dependent Variable Independent Variables	CAR
PE	0.0764*** (14.96)	0.166*** (27.25)	0.168*** (27.61)
Capital issue year (2005–2008)	–0.173*** (–28.97)	–0.109*** (–14.70)	–0.105*** (–13.63)
Capital issue year (2009–2012)	–0.177*** (–28.08)	–0.174*** (–21.58)	–0.159*** (–19.54)
Capital issue year (2013–2016)	0.296*** (43.40)	0.399*** (44.95)	0.418*** (46.82)
Age	–0.169*** (–14.21)	–0.267*** (–17.22)	–0.274*** (–17.66)
Total assets		0.222*** (63.03)	0.234*** (64.61)
Asset growth		0.147*** (51.12)	0.148*** (50.68)
Current ratio		0.0204*** (6.27)	0.0174*** (5.34)
Sales		–0.00765*** (–4.61)	–0.0204*** (–11.11)
Debt-to-equity ratio		–0.0180*** (–8.60)	–0.0191*** (–9.13)
Issue amount		–0.236*** (–66.69)	–0.237*** (–66.50)
Hot year			0.0359*** (7.45)
Non-banking & finance industry			0.215*** (15.63)
Constant	0.0833*** (11.50)	–0.153*** (–9.74)	–0.383*** (–18.29)
N	128,840	98,145	98,145
Adj. R2	0.059	0.122	0.125

Source: The Authors

Notes: 1. This table reports results for the total sample data in the range from 0 to 250 trading days.

2. CAR is the dependent variable.

3. We divide the model into three regressions with ascending number of independent variables.

4. IPOs are equally weighted.

5. First, the basic model accounts for fixed effects. Second, the adjusted model incorporates operating performance and third, we account for hot year and industry.

6. PE is a dummy variable with value one if there is PE investment and zero otherwise. Capital issue year are three dummy variables grouped by year of going public on BSE. Age is a variable accounting for the time until a company goes public. It is calculated as one divided by the square root of difference of incorporation year and capital issue year. Asset growth is the growth rate of the total asset base within 1 year after IPO. The current ratio and sales are incorporated as the logarithm. We account for leverage by including the logarithm of the debt-to-equity ratio. Debt equity here considers all debt—short term as well as long term. Preference capital or fictitious assets/liabilities are not included in the PE deals. Issue amount is the offer size on IPO date incorporated as the logarithm. Hot year is a dummy variable that equals one if the yearly mean market return is more than 20% and zero otherwise. Total assets are the logarithm of the total asset base at IPO. Non-banking finance industry is a dummy variable that equals one if a company is from mentioned industry sector and zero otherwise.

7. The tolerance and VIF values for all independent variables are within the acceptable limits.

8. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels for two-tailed t-tests, respectively.

First, PE-backed IPOs have a significantly better IPO performance within the first year after the IPO. Also, the presence of PE investors is significantly positively correlated with performance within the three tested time windows of 0–20, 90–110 and 230–250 trading days. Considering the impact of the PE investor, our findings are in line with the literature. Along with PE investment, additional firm characteristics are relevant in explaining post-IPO performance. Capital issue year, post-financial crisis (2013–2016), size in terms of total assets and sales, performance in terms of asset growth, liquidity accounted by current ratio, leverage, hot year and industry background from the non-banking industry are positively related to the one-year stock performance. Capital issue year, pre- and during financial crisis (2005–2008; 2009–2012), as well as the issue amount are negatively related to the first-year CAR development.

The presence of ‘hot’ and ‘cold’ markets suggests that we control for effects post-, during and pre-financial crisis. Capital issue year post-financial crisis (2013–2016) is significantly positively correlated to CAR. In contrast, capital issue year during financial crisis (2009–2012) or pre-financial crisis (2005–2008) is significantly negatively correlated with post-IPO performance. Going public during financial crisis, to a higher degree, is negatively correlated to post-IPO performance than going public pre-financial crisis. Univariate regression reveals that PE-backed IPOs outperform stand-alone firms from 2000 to 2012, whereas non-PE-backed IPOs perform better thereafter.

Age is significantly negatively correlated with CAR development. This is in line with Sahoo and Rajib (2010) who finds a negative but not a significant correlation between age and post-IPO performance. In contrast, Ritter (1991) finds worse IPO performance for young growth companies. Issue amount is negatively correlated with post-IPO performance, which is in line with Sahoo and Rajib (2010) and Clarke et al. (2016). Both find a negative correlation between offer size and performance for the Indian market. Leverage is positively correlated with post-IPO performance, which is also in accordance with existing literature (Eckbo & Norli, 2005; Higson, 2012; Hoechle & Schmid, 2008; Sahoo & Rajib, 2010).

IPOs going public in peak seasons with mean market returns of more than 20 per cent perform better than peers going public in ‘cold’ markets. We account for peak seasons with the dummy variable, hot year. Consequently, we show that market timing is a major factor in going public as it has a significant impact on post-IPO performance (Loughran & Ritter, 1995; Ritter & Welch, 2002).

Moreover, we use industry type from non-banking and finance industry as a dummy variable. T-test indicates that IPOs from non-banking and finance industry underperform the BSE Sensex. Markets can be very optimistic about a particular industry that triggers motivations for going public. So, it is important to include industry dummy variables in regression analysis (Pande & Vaidyanathan, 2009).

PE-backed IPOs

We also examine the subsample of PE-backed IPOs to test for the influence of PE sponsors’ nationality (foreign, domestic and co-investment) on performance. Table 6 presents the results.

Table 6.

Regression Analysis of the CAR of PE-backed IPOs

Model (Weighted by Market Cap)	1. Basic Model	2. Adjusted for Operating Performance	3. Adjusted for Industry
Dependent Variable Independent Variable		CAR
PE foreign	0.0272*** (4.16)	–0.0380*** (–4.69)	–0.0547*** (–6.66)
PE India-dedicated	–0.0538*** (–9.84)	–0.142*** (–21.87)	–0.136*** (–21.88)
Age	–0.955*** (–48.06)	–0.704*** (–24.70)	–0.675*** (–24.18)
Debt-to-equity ratio		–0.0163*** (–6.39)	–0.0575*** (–19.24)
Promoters’ total share		–0.00826** (–2.77)	–0.0376*** (–11.58)
BFSI			–0.249*** (–18.73)
Engineering & construction			–0.334*** (–39.40)
Healthcare & life sciences (HCLS)			–0.0434*** (–3.44)
IT & ITES			–0.425*** (–27.79)
Manufacturing			–0.148*** (–9.54)
Constant	0.373*** (46.31)	0.436*** (15.24)	0.729*** (23.99)
N	22,667	16,723	16,723
Adj. R2	0.102	0.065	0.186

Source: The Authors

Notes: 1. This table reports results for the PE subsample data in the range from 0 to 250 trading days.

2. CAR is the dependent variable.

3. The model is divided into three regressions with ascending number of independent variables.

4. IPOs are weighted by market capitalization.

5. First, the basic model accounts for fixed effects. Second, the adjusted model incorporates operating performance. In the third stage, we include independent variables for different industry types.

6. PE Foreign and PE India-dedicated are dummy variables with value one if there is an investment by a foreign or India-dedicated PE company and zero otherwise. Age is a variable accounting for the time until a company goes public. It is calculated as one divided by the square root of difference of incorporation year and capital issue year. The promoters’ total share’ is the ownership share of the PE investor. It is incorporated as the square root. Engineering & construction, HCLS, IT & ITES, and manufacturing and media are dummy variables that equal one if a company is from the respective industry sector and zero otherwise.

7. The tolerance and VIF values for all independent variables are within the acceptable limits.

8. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels for two-tailed t-tests, respectively.

We find a negative correlation between both PE foreign and PE India-dedicated investment on CAR development over the entire period. India-dedicated IPOs, to a higher degree than foreign IPOs, are negatively correlated to CAR development. Our findings are in line with the literature. Associated empirical evidence from Hamao, Packer, and Ritter (2000) show higher performance of foreign-PE sponsors in Japan. Guo and Jiang (2013) report for the Chinese market that foreign-backed PEs have more expertise in monitoring and providing support for their companies. Moreover, Douma, George, and Kabir (2006) find lower degrees of underperformance for foreign-backed IPOs. They attribute the positive effect of foreign ownership to firm characteristics, such as larger shareholding, higher commitment and long-term involvement. Dai et al. (2011) for Asia, as well as Humphery-Jenner and Suchard (2013) for China, find positive effects of local and foreign PE partnerships on successful exits. Collaboration helps to reduce information asymmetries and monitoring problems, and, thus, increases the performance of coinvestments. Moreover, the entry of foreign PE sponsors helps to reduce the costs of learning due to spillover effects (Black & Gilson, 1998; Douma et al., 2006; Wang & Wang, 2011).

After controlling for age, leverage, ownership share of PE investor and industry type, we find negative CAR correlation of foreign and India-dedicated backed IPOs within time windows of 0–20, 90–110 and 230–250 trading days. Age, leverage and ownership share are negatively related to the first-year stock performance. As we weight observations by market capitalization, we no longer include total assets and sales as additional size measures in the model. We find a negative correlation between age and CAR evolution, similar to the results for the whole sample. In contrast to the total sample, we find in the PE subsample a negative correlation between leverage and CAR evolution. The negative effect of leverage on PE-backed firms is reflected in the lower debt-to-equity ratio of PE-backed companies compared to non-PE-backed ones. There is a significant negative correlation between promoters’ total share’ and CAR evolution, meaning that a majority-owned PE firm performs worse than a minority-owned one. Companies belonging to engineering & construction, HCLS, manufacturing and media industry experience a negative impact on post-IPO performance. In contrast, IPOs from the IT & ITES experience stronger CAR performance.

Conclusion

In this study, we examine the 1-year aftermarket performance of PE-backed IPOs in India. We test for the impact of PE ownership on the post-IPO performance and the effect of the PE nationality on post-IPO performance. This study has two main findings.

First, PE-backed IPOs exhibit stronger post-IPO performance than non-PE-backed IPOs as we find CARs of –5.00 per cent for PE-backed IPOs and –7.27 per cent for non-PE-backed IPOs. Thus, we find underperformance for all IPOs within the first year whether they are PE-backed. PE-backed IPOs exhibit lower degrees of underperformance than non-PE-backed IPOs. Pre-IPO-specific characteristics, such as capital issue year post-financial crisis (2013–2016), size, liquidity and leverage have a significant positive impact on post-IPO performance. Issue amount and capital issue year, pre- and during the financial crisis (2005–2008 and 2009–2012), are negatively related to CAR development. The superior performance of PE-backed IPOs is consistent with explanations of long-term effect of tighter monitoring, the professionalization of the management teams and advanced corporate governance mechanisms put in place by PE companies.

Second, we find significant effects of PE firm nationality on CAR development. IPOs backed by PE co-investments outperform the benchmark index in the univariate analysis. Multiple regression analysis shows that India-dedicated PEs, to a higher degree, are negatively correlated to CAR evolution than foreign PEs. The superior performance of co-investments can be explained by successful collaboration between foreign and domestic funds. Collaboration adds value by leveraging the benefits of domestic funds (increased monitoring abilities and reduction of regulatory interference) with the benefits of foreign funds (management professionalization). Additionally, the PE industry may benefit from collaboration due to knowledge spillover effects.

Our study fills the research gap by empirically examining the performance of Indian IPOs backed by PE. First, India’s PE landscape is very vibrant due to its high total issue volume, a high share of PE-backed IPOs and a large proportion of foreign investors. Second, to our knowledge, no research has been conducted that examines the impact of PE nationality on post-IPO performance. Third, our study is the most comprehensive for the Indian market, utilizing a novel data set of 616 IPOs including 116 PE-backed IPOs over a very long measurement period between 2000 and 2016.

The practical/policy implications of our study is of three fold: (a) The regulator (SEBI) should bring some policy framework to promote co-investment in the PE industry as companies which attract fund from co-investors perform better than others. (b) The regulator should also bring some amendments to liberalize exit route for the investors to attract more investment by the PE firm to realize the dream of ‘new India’. It has been observed that in the absence of lucrative exit route, the PE investment has declined in the recent years. (c) The regulator should also bring better corporate governance structure and professionalism in the firms as it will minimize information asymmetry which in turn will help firms to raise more investment from PE funds.

Further research should take the operational results pre-IPO, development of ownership shares after IPO and governance mechanism into account and examine their performance impact on the PE-backed Indian companies along with exit status of PE firms.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

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