Does Regulation Impact the Initial Returns of Initial Public Offerings? Evidence from Indian Capital Market

Abstract

Regulations are put in place in the capital markets to protect the interests of investors while promoting companies to actively participate in the capital markets. This multidisciplinary study concentrates on analysing the impact of one such regulation, based on entry norms, on the initial returns of book-built IPOs in the presence of firm-related and issue-related control variables, thereby facilitating decision-making to issuers and investors.

Using various parametric and non-parametric tests on 259 IPOs issued on Indian Stock Exchanges during financial year 2009–2010 to 2019–2020, it can be concluded that Indian IPOs are underpriced on an average, irrespective of the entry norm followed. Further, firms entering through either of the entry routes, that is, profitability route or Qualified Institutional Buyer Route have significant differences in age, listing delay, type of sale, rank of lead managers and industry. Entry norm is established to have a negative yet insignificant role in determining the initial returns, while oversubscription is the only variable with a positive and significant impact on the initial returns of the issue.

JEL Codes: G10, G14, G18, G23, K22

Keywords

Initial return initial public offerings entry norms profitability route qualified institutional buyer route information asymmetry

I. Introduction

Initial Public Offering (IPO) is raising funds from the public by issuing a firm’s shares for sale for the first time. It is considered one of the most crucial decisions during the lifecycle of the firm (Latham & Braun, 2010). Various theories explain the motivation of firms to go public—windows of opportunity theory (Ritter & Welch, 2002), life cycle theory (Rajan & Zingales, 1995), refinancing theory (Rock, 1986) and market timing theory. Generally, IPOs are priced either of two ways—fixed price offer method or book building method. The former involves setting a fixed issue price after evaluating the financial performance of the firm and the market conditions of the economy. While the latter involves setting the price after receiving bids from the investors.

In India, the IPO market is well-established and is regulated by the Securities and Exchange Board of India (SEBI). Issuers, along with hired lead managers, decide on several aspects of the IPO such as pricing, preparation of documents and prospectus, listing information and allotment of shares to different sets of investors, namely—retail individual investors (RII), non-institutional investors (NII) and qualified institutional buyers (QIB). Rock (1986) and Welch (1992) bifurcates these investors into informed (i.e., QIBs) and uninformed investors (i.e., RIIs). It causes information asymmetry and the tendency of the uninformed investors to follow the informed investors hoping for better returns. In terms of performance, India’s IPO market trends have been satisfactory compared to other parts of the world. It was ranked sixth worldwide in terms of quantum of IPO issued in 2019 (Ernst & Young, 2020). Figure 1 depicts the quantum and volume of IPOs issued in the last decade in India.

Figure 1.

Source: The authors.

One of the reasons for the substantial volume raised is the regulations implemented by the SEBI for smooth markets’ administration. Regulations mandate adherence to the issuing and listing norms by the market participants to ensure transparency, uniformity and protection. Regulations that govern the Indian primary market, majorly, are the SEBI (Issue of Capital and Disclosure Requirements) Regulations, 2009 (SEBI (ICDR), 2009), (SEBI, 2018) and SEBI (Listing Obligations and Disclosure Requirements) Regulations, 2015. These regulations instil the investors’ faith in the Indian primary market, leading to high volume and subscription levels. But it also poses an intriguing research question—‘Do regulations impact the initial returns of the initial public offerings issued in the Indian primary market?’ The present study examines the same.

Literature on regulation and its impact on IPO performance is scarce. Our study is the first of its kind and fills this research gap. It carries out an extensive empirical analysis to discover the effectiveness of the regulations. Moreover, it examines the underpricing decadal trends post the Financial Crisis of 2007–08. The study also attempts to test the information asymmetry theory and cascade theory, arising out of different share allocation ratios to investors.

II. Theoretical Framework

Indian IPO market operates, primarily, within the regulation as stipulated under SEBI (ICDR) Regulations, 2009. Introduced with 12 chapters and 21 schedules, SEBI (ICDR) Regulation, 2009 focuses on various aspects (such as eligibility requirements, stock allocation, pricing norms, the obligation of underwriters and issuers and disclosure manners) of each type of public issue (IPO, Right Issue, Preferential Allotment, FPO, IDRs, Bonus Issue, SME Listing). One such regulation relates to the entry norms for book-built equity issues and is specified in Regulation 26, read along with Regulation 41 and 43, of SEBI (ICDR) Regulations 2009. According to the regulation, issuers fulfilling the minimum assets, revenues and profit criteria can enter the primary markets through Profitability Route; else they can opt for QIB Route.

Figure 2 elucidates the regulation in depth. In essence, for a firm to enter through the profitability route, it must have a minimum average pre-tax operating profit of Rs. 150 million, net tangible assets of at least Rs. 30 million and net-worth greater than Rs. 10 million in each of the preceding three full years. Firms, which are unable to satisfy these preconditions, enter through the alternative route—the QIB route.

Figure 2.

Source: Regulation Adopted from Securities and Exchange Board of India (Issue of Capital and Disclosure Requirements) Regulations, 2009.

Further, allocation criteria to each investor category differ in both the entry routes. The ratio of allocation of shares to RII, NII and QIB shall be ≮35: ≮15: ≯50 respectively in the case of the profitability route and ≯10: ≯15: ≮75 respectively (earlier ≯35: ≯15: ≯50) in the case of the QIB route (Figure 3). The ratio of allocation has modified over the years with several amendments as per market requirements. The amendments that brought about a substantial change in the regulations, pertinent to the study, are—SEBI (ICDR) (Fourth Amendment) Regulations, 2012 and SEBI (ICDR) (Third Amendment) Regulations, 2014.

Figure 3.

Source: Regulation Adopted from Securities and Exchange Board of India (Issue of Capital and Disclosure Requirements) Regulations, 2009.

The alternative entry route allows genuine firms, which do not conform to the financial standards set by SEBI, to raise funds in the primary market. On the other hand, it also ensures retail investors’ protection by allocating a higher proportion of the net offer to the informed investors. Apart from the allocation proportions, the major differences between both the routes arising due to regulation are as follows:

Unlike profitability route firms, QIB route firms have to compulsorily price their issue with the book-building method.

QIB Route firms have to compulsorily allot 75 per cent of the net offer to QIBs, failing which the full subscription amount shall be refunded. While in case of under-subscription in the QIB category of profitability route firms, the allotment shall be made to the remaining investor categories.

Objective and Rationale

The study aims to deconstruct IPO entry norms. The unique idea of permitting the less-profitable firm to enter IPO markets while providing credibility through the involvement of more informed investors has led to exploring underpricing in the context of entry norms. Keeping in mind the regulatory innovation in practice, the following objectives are drafted: (a)

To examine underpricing trends.

(b)

To empirically examine the differences between firms issuing IPO through profitability route and QIB route;

(c)

To analyse the impact of different entry routes on listing day returns of book-built IPO listed on Mainboards of Indian Stock Exchanges (NSE & BSE) from 2009 to 2019.

To formulate hypotheses, the theory is divided into three sections to study each aspect of the research question individually.

Initial Performance

The performance of an IPO can be judged with the help of various parameters such as very short-term returns, short-term returns, medium-term returns, long-term returns and survivability of listed firms. But the most debated phenomenon in IPO literature is the underpricing anomaly apparent in the very short-term. An issue is said to be underpriced when the closing price on the listing day is higher than the offer price. Listing day price is the price determined by the market while offer price is set by the company in consultation with underwriters. So, underpricing is, basically, the difference in the company’s expectation and the market’s actual response due to uncertainty or information asymmetry. Loughran et al. (1994) collected 25 countries’ data to analyse average underpricing among different countries to show that underpricing is a global phenomenon. Initial return deliberated by various past scholars is cited in Table 1. Several authors have posited theories that explain the underpricing. Information asymmetry theory, by Ritter and Welch (2002), states that there is a difference in the information held by different kinds of investors and issuers. Rock (1986) connoted issuers as the uninformed participant and underpricing acts as an effective way of compensating the informed investors (particularly institutional investors) of the market information provided by them. While Negative Cascade Theory by Welch (1992) suggested that investors rely on the judgment of other investors.

Table 1.

Studies on Underpricing in IPO Markets.

Study	Sample	Sample Period	Underpricing (%)
International Studies
Ibbotson (1975)	One IPO selected at random for each month	1960–1969	11.4
Welch (1989)	1,028 U.S. IPOs	1977–1983	26
Loughran and Ritter (2002)	3,025 U.S. IPOs	1990–1998	14
Islam (2014)	105 IPOs listed on Dhaka Stock Exchange	2003–2013	263.90
Ekstrand and Zachrisson (2018)	110 Western European IPOs	2010–2018	1.65
Killins (2019)	73 IPOs in the Canadian stock market	2010–2017	1.45
Rattanavijai (2018)	128 IPOs listed on the Stock Exchange of Thailand (SET)	2006–2016	25.85
Indian Studies
Singh and Maurya (2018)	45 NSE IPOs	2012–2017	10.27
Dhamija and Arora (2017b)	399 IPOs	2005–2015	22
Shah and Mehta (2015)	97 NSE IPOs	2010–2014	7.19

Source: Compilation done by authors.

Determinants of underpricing: Several factors determine the level of initial returns from an IPO. A study by Sehgal and Singh (2008) exhibited that age, issue size, listing delay and offer price were negatively related to underpricing and oversubscription and market conditions were positively related to initial returns. All the results were found to be statistically significant. Singh and Kumar’s (2019) exploratory data analysis on a sample of 437 IPOs from 2003–2018, revealed that more book-running lead managers were employed by large firms, although they had a significant negative relationship with underpricing of medium-sized firms. Killins (2019) deliberated on the short-term performance of Canadian IPOs. While age and earnings were found to bear a negative relationship; technology firms had a positive relationship with the level of underpricing.

Impact of Entry Norms on IPO Performance

Johan (2010) highlighted that regulation in the nature of listing standards signals the quality of the firms to the investors and eliminates the less prepared firms from going public. Thus, it might impact the IPO performance more significantly than the firm and industry-related factors. The entry routes differ from each other in two aspects majorly: (1) in terms of the financial position of the firms; and (2) in terms of the percentage of net offer reserved for QIBs. Thus, the impact of entry norms on initial returns can be hypothesised by studying both these aspects individually: (a)

Impact of company’s financial position on IPO performance: Scholars, in the past, have tested the financial position of the company and its impact on the IPO performance. Some opine that profits and assets have a negative relationship with the initial day returns (Lin & Tian, 2012), while others have established that profitability results in high-class reputation of the firm and affects the initial day returns positively (Johan, 2010). Another school of thought provided evidence of more important factors affecting returns, like IPO volume and issue size; implying that return on assets has no significant impact on listing gains (Baba & Sevil, 2020). Keeping in mind all arguments, no concrete conclusion can be drawn regarding the impact of the company’s financial position on IPO’s initial return.

(b)

Impact of presence of QIBs on IPO performance: QIB route issues involve allocation of at least 75 per cent of the net offer to the QIBs, while their share is not more than 50 per cent for profitability route issues. Thus, examining the impact of the QIBs was of utmost importance. Bubna and Prabhala’s (2011) analysis affirmed the theory of QIBs acting as information providers and assisting underwriters as effective price discoverers in book-built issues. Amihud et al. (2003) and Ekstrand and Zachrisson (2018) extended the idea and highlighted that despite being perceived as more informed investors; institutional investors’ participation did not impact the IPO underpricing. A similar study by Khurshed et al. (2009) provided conclusive evidence of underpricing as a result of the unsatisfied demands of NII & RII, rather than QIBs. QIB’s reaction to an IPO was merely an assessment criterion for NII & RII, thereby removing the issues related to the winner’s curse assumption. A contradictory view was presented by Aggarwal et al. (2002). Using a sample of 174 IPOs, they established a positive relationship between allotment of shares to institutional participants and underpricing. According to them, implicit private information is conveyed through the ratio of institutional investors’ allocation. Refuting the claims of past scholars, a work by Che Yahya and Abdul-Rahim (2014) investigated that a greater allotment to QIBs suggests less flipping and thus, low underpricing.

These conflicting studies led to the formulation of the following hypotheses:

H₀₁: Entry norms do not significantly impact the initial returns of an IPO.

H₀₂: There is no significant difference in means of issue-related and firm-related characteristics of profitability route firms and QIB route firms.

H₀₃: There is no significant difference in proportion of issue-related and firm-related characteristics of firms entering through either of the entry routes.

III. Data and Methodology

The data relates to 259 firms listed on the main-board of the national stock exchanges of India, that is, National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) from the financial period 2009–2010 to 2019–2020 (Table 2). The information regarding variables was collected from several secondary sources (Table 3). The data was winsorised at 1st and 99th percentiles (Wilcox, 2005) and log-transformed to minimise skewness and variability evident in the data (Feng et al., 2014).

Table 2.

Sample Considered.

	Number of Companies
Total number of firms issuing equity shares on mainboard of Indian stock exchange from FY 2009 to FY 2019	282
Number of issues withdrawn	16
IPO issuing through fixed price offer	4
Number of INViTs and REITs IPO issued	3
Finalised Sample	259

Source: The authors.

Table 3.

Identification and Transformation of Research Variables.

Variable	Meaning	Source of Data Collection	Symbol	Supporting Literature
Dependent Variable
Listing day returns	Difference between the issue price and closing price on the first trading day on BSE over issue price. $U P = (\frac{P_{1} - P_{0}}{P_{0}}) * 100$	Authors’ own calculation	Log_UP	Singh and Maurya (2018);Killins (2019)
Independent Variable
Entry route	Whether the firm satisfies eligibility conditions specified in Reg 26(1) or Reg 26(2) of SEBI (ICDR) Regulation, 2009.Dummy variable value:1 – Profitability route firm0 – QIB Route firm	Handpicked from prospectus of each firm	D_ER	Aggarwal et al. (2002)
Control Variable 1: Firm Related Characteristics Variables
Age	Age of the firm since its incorporation	Handpicked from prospectus of each firm	Log_Age	Killins (2019); Sehgal and Singh (2008)
Industry	Dummy variable value: 1 – if the firm belonged to the technology sector0 – otherwise	CMIE Prowess IQ	D_Tech	Killins (2019)
Control Variable 2: Issue Related Characteristics Variables
Issue size	Amount raised by firm.	Prime Database	Log_IS	Islam (2014); Sehgal and Singh (2008)
Subscription ratio	Number of times a share is subscribed by all investor	Basis of Allotment document extracted from the www.chhitorgarh.com	Log_OS	Islam (2014); Sehgal and Singh (2008)
Listing delay	Number of days when an issue is closed but not listed.	Author’s own calculation	Log_LD	Sehgal and Singh (2008)
Lead manager ranking	Ranking of lead manager in terms of market share.Dummy variable with value: 1 = issue handled by top 10 lead managers by market share0 = otherwise	Indian Capital Market League Tables, Bloomberg and Prime Database	D_LM	Singh and Kumar (2019)
Number of lead managers	Lead managers who are handling all issues related to issue of IPO	Handpicked from prospectus of each firm	Log_NLM	Singh and Kumar (2019)
Type of sale	Whether the sale is through fresh issue of capital or offer for sale by the company.Dummy variable for each type of sale (OFS & FC) created and values given:1 = if firm issued shares through specified sale type0 = otherwise	Handpicked from prospectus of each firm	D_OFSD_FC	Dhamija and Arora (2017a)

Source: The authors.

Exploratory data analysis was conducted to figure out patterns and inferences with the given dataset.

Descriptive statistics, like mean, median, standard deviation has been used along with Welch’s t-test and chi-square analysis to analyse significant differences among the metric and categorical characteristics of companies entering via either route respectively.

Further, multiple regression analysis was run to predict the model for returns. Provided the assumptions of ordinary least squares regression analysis are met; it produces the best, linear and unbiased estimators and it is also established to be as effective as machine learning techniques like random forest sampling.

For the finalised model, except for normality and heteroscedasticity, all the assumptions were duly met. Robust Standard error t statistics were used to deal with the problem of heteroscedasticity, while normality assumption was relaxed due to sufficiently large sample size.

All the data gathered was analysed using R Studio v1.3.1056, Microsoft Excel (2010) and IBM SPSS Statistics 23.

The model tested is as follows:

\begin{array}{l} \log U P = β_{0} + β_{1} (D_{-} E R) + β_{2} (L o g_{I} S) + β_{3} (\log_{-} O S) + \\ β_{4} (\log_{-} L D) + β_{5} (\log_{-} N L M) + β_{6} (D_{-} L M) + β_{7} (D_{-} O F S) + \\ β_{8} (D_{-} F C) + β_{9} (L o g_{-} A g e) + β_{10} (D_{-} T e c h) + ε \end{array}

IV. Findings and Discussion

Initial Returns as an Individual Variable

An issue is said to be overpriced when an investor earns a negative listing day return; otherwise, it is said to be underpriced. For this study, when computed for the whole sample, the average turned out to be 11.51 per cent positive returns in proximity to 10.27 per cent as deliberated by Singh and Maurya (2018) (Table 4). A deeper analysis is done with the help of Figure 4. As can be observed, financial year (FY) 2019–2020 saw the largest level of underpricing (24.19%), while FY 2012–2013 observed the least amount of underpricing (1.40%). Thus, it was confirmed that Indian IPOs are underpriced on average.

Table 4.

Yearly Underpricing Trends.

Financial Year	Underpricing (%)
Financial Year	Mean	Median	Max	Min
2009	7.52	4.97	63.62	–37.21
2010	13.23	7.31	104	–52.86
2011	3.97	–4.47	153.5	–68.92
2012	1.40	–0.02	23.19	–13.09
2013	15.37	15.37	15.37	15.37
2014	20.82	16.25	67.55	–12.19
2015	6.31	1.66	49.85	–21.58
2016	21.93	15.26	114	–16.75
2017	14.79	2.01	139	–20.67
2018	2.50	–0.28	65.01	–12.28
2019	24.19	17.46	128	–9.51
Total	11.51	3.80	153	–68.92

Source: The authors.

Figure 4.

Source: The authors.

Introducing Entry Norm as a Categorical Variable

The average initial level of return turned out to be 11.68 per cent for 176 profitability route firms and 11.15 per cent for 83 QIB route firms. An insight into the data revealed that both the entry norms consisted of overpriced and underpriced issues (Figure 5). An intriguing observation is that the ratio of overpriced issues to underpriced issues in each route remains almost the same (37.5:62.5). The results are consistent with Islam (2014). Some investors made a profit for themselves on the first trading day, while others could not do so.

Figure 5.

Source: The authors.

Yearly analysis of average underpricing by entry routes is depicted with the help of Figure 6. As can be noted, the average yearly underpricing of QIB route firms was inconsistent as compared to that of the profitability route firms. While the range of QIB route firms’ yearly average first-day returns stood between +42.0 per cent (FY 2011) and –7.08 per cent (FY 2018), the range of profitability firms’ yearly average first-day returns stood between +22.28 per cent (FY 2019) and –4.82 per cent (FY 2011). The break-in profitability route firm line suggests that no IPO was issued under that route in FY 2013.

Figure 6.

Source: The authors.

Cross-Sectional Analysis

The emphasis was manifold here. The first question was to analyse the significant differences in characteristics of profitability route and QIB route firms. Secondly, do entry norms influence underpricing? Thirdly, if the entry route affects the underpricing, what is its magnitude? Lastly, how do the coefficients change in the presence of control variables?

Testing for Differences in Means and Proportions

Welch’s t-test was run to analyse significant differences in characteristics of profitability route and QIB route firms. The results, tabulated in Table 5, revealed that the difference in mean underpricing level was insignificant at various significance levels, suggesting both entry routes had the same level of underpricing and entry routes do not have an impact on underpricing. Further, except log of listing delay and log of age, none other variables’ mean difference was found to be statistically significant for both the entry routes. It implied that profitability route firms are, on average, older than QIB route firms, and QIB route firms have more listing delays as compared to the profitability route firms. Mean issue size, mean oversubscription rate and mean number of lead managers were found to be similar for both the entry routes, suggesting that firms can raise equivalent funds from an IPO and can have similar subscription patterns, irrespective of the entry route. The findings were found to be significant at a 5 per cent level of significance.

Table 5.

Output Obtained for Testing of Mean Differences Using Welch’s t-Test.

Variables	Profitability Route Analysis (N = 176)				QIB Route Analysis (N = 83)				Levene’s Test for Homogeneity of Variance	Mean Diff.
Variables	Mean	Median	SD	Max	Mean	Median	SD	Max	F-Statistic	Mean Diff.
Log_UP	4.35	4.33	0.31	4.88	4.33	4.29	0.28	4.88	2.305	0.0133
Log_Age	2.75	2.77	0.62	3.97	2.53	2.56	0.74	3.97	5.013*	0.2154*
Log_IS	10.37	10.66	1.42	13.5	10.53	10.57	1.20	13.50	7.061*	–0.1620
Log_OS	1.86	1.52	1.55	5.11	1.86	1.62	1.57	5.11	0.278	–0.0019
Log_NLM	0.82	0.69	0.67	2.56	0.88	1.10	0.63	2.20	0.506	–0.0584
Log_LD	2.53	2.56	0.31	3.21	2.65	2.67	0.30	3.21	1.304	–0.1146*

Source: The authors.

Note: * denotes statistical significance at 5% significance level.

The table describes summary statistics, that is, mean, median, standard deviation (SD), maximum and minimum values for each transformed metric variable under each route. Levene’s test for homogeneity of variance is conducted to see whether the data assumed equal variance or not. F-statistic of Levene’s test with p > .05 implies that null hypothesis of equal variance is retained. Further, the mean differences column consists of calculated differences in means of variables of both the entry route. When p < .05 (significance level), then it means that the null hypothesis, mean difference is statistically equal to 0, is rejected for that particular variable and is denoted by an asterisk (*).

For categorical variables, like industry, rank of lead manager and type of sale, the chi-square test was used to evaluate significant differences in proportions. The output is tabulated in Table 6. It was evident that the variables, namely technology sector, rank of lead manager, fresh issue capital and offer for sale type of sale have a significant relationship with entry norm. Alternatively stated, QIB route firms preferred high-ranking lead managers as compared to profitability route firms and this relationship was found to be significant at a 5 per cent level (χ² = 4.569 with df = 1 and p-value = .026 < .05). Phi’s value was indicated to measure the strength of the relationship between the variables. In our case, it was calculated as +0.133, which implied positive and low association. Similar conclusions could be drawn for OFS, technology and fresh issue capital too. The relationship between entry route and OFS was found to be significant and negative, implying the proportion of QIB route firms issuing shares as an offer for sale is significantly less as compared to profitability route firms. Further, out of the 33 issues belonging to the technological sector, 18 issues (approx. 55%) entered the market through the QIB route, suggesting that the Indian technology sector firms are not as financially sound as required by the regulation. The reason could be the presence of more intangible assets in the case of technology firms, which are relatively difficult to evaluate and might lead to misvaluation of the net assets.

Table 6.

Chi-Square Output for Categorical Variables (Test of Proportions Differences).

Entry Norm		D_LM		D_OFS		D_FC		D_Tech		Total
Entry Norm		Yes (1)	No (0)	Yes (1)	No (0)	Yes (1)	No (0)	Yes(1)	No(0)
Profitability route (1)	Count	110	66	107	69	135	41	15	161	176
Profitability route (1)	% of row total	62.5	37.5	60.8	39.2	76.7	23.3	8.5	91.5	100
QIB route (2)	Count	63	20	35	48	75	8	18	65	83
QIB route (2)	% of row total	75.9	24.1	42.2	57.8	90.4	9.6	21.7	78.3	100
Total	Count	173	86	142	117	49	210	33	226	259
Total	% of row total	66.8	33.2	54.8	45.2	81.1	18.9	12.7	87.3	100
Test of proportions	Chi-square value with df	4.569 [1]		7.901 [1]		6.858 [1]		8.791[1]		-
	p-Value	0.026*		0.007*		0.020*		0.003*
	Phi’s value	+0.133		–0.175		+0.163		+0.184

Source: Authors’ own computation.

Note: (1) * denotes statistical significance at 0.05 level.

(2) Digit specified in parenthesis () denote the value assigned to each response of variable while running the test.

Multiple Regression Analysis

This section deals with enumerating the impact of entry norms on the initial level of returns of a firm using ordinary least squares regression. The regression output is presented in Table 7. The analysis was conducted in four models, each analysing a different set of variables. The results of Model 1 established clear evidence of the minimal impact of entry route on underpricing (coefficient: 0.013). R-squared of Model 1 was close to 0, while to model was also unfit. Model 2 tested control variables related to the firm, even before its issue. The model’s results disclose that none of the factors were a significant determinant of first-day returns. Also, the model remained unfit (F-stats. = 1.624). Model 3, based on issue-related control variables, indicated that only constant term and log of overall subscription ratio were statistically significant variables with coefficients +4.24 and +0.114, respectively.

The final model, Model 4 incorporated all the variables. The model predicted was, then, tested for assumptions. Linearity assumption was checked by Residual vs Fitted Plot and found to be roughly met. The normality of the error terms was judged using the Shapiro–Wilk normality test. The result statistics (W = 0.97244, p-value = 6.599e – 05) indicated non-normality. But due to sufficiently large sample size, this assumption was relaxed. Non-Constant Variance Score Test statistic (χ² = 5.788, df = 1, p-value = .01613) and Studentized Breusch–Pagan test statistic (BP = 72.993, df = 14, p-value = 5.512e – 10) revealed presence of heteroscedasticity. Log transformation of the variables and Eicker–Huber–White standard errors were used to adjust the standard errors to solve the problem of heteroscedasticity. There was no autocorrelation evident in the data (Durbin–Watson, DW = 1.964, p-value = .347). Since the variance inflation factor (VIF) for all the variables was less than five, the assumption of no perfect multi-collinearity was also met.

Except for issue size, OFS and overall subscription ratio, all other variables exhibited negative coefficients with underpricing. The variable found to be of substantial importance to determine the level of underpricing was the overall subscription rate only. It implies that for a 10 per cent increase in overall subscription rate, underpricing increased by 1.11 per cent, keeping all other things equal. All other variables were found to be statistically insignificant. The negative relationship of entry norm with underpricing, although insignificant, was again consistent with our expectations. The profitability route issues have a sound financial position which reduces the uncertainty among investors, ultimately leading to less flipping and hence lower initial day returns (Lin & Tian, 2012). On the other hand, the higher ratio of allocation to QIBs, in the QIB route issues, implicitly signals private information and thus, influences demand by RIIs and NIIs and shows greater underpricing (Aggarwal et al., 2002; Amihud et al., 2003). This confirmed the existence of cascade theory as propagated by Welch (1992). Issue size did not contribute to the listing day returns. The technological industry had a different sign of coefficient as the expected positive sign as deliberated by Killins (2019). The negative coefficient of D_tech with underpricing is consistent with the results of Ekstrand and Zachrisson (2018). The positive impact of OFS type of sale on underpricing, contradictory to the negative expected sign (Dhamija & Arora, 2017a), could be attributed to the minimum public shareholding norm imposed by the Government of India. The selling of stakes by existing shareholders is not seen as a voluntary measure, rather a regulation that might not impact a firm’s value or returns. The negative coefficient of log of age, although insignificant, was due to the theory propounded in Sehgal and Singh (2008) and Killins (2019). Uncertainty surrounding older firms is less; hence, more accurate price is obtained causing low underpricing. For the variable listing day, a higher listing day reduces information asymmetry and leads to lower underpricing (Sehgal & Singh, 2008). Last, but not least, the variables associated with the lead manager, that is, rank of lead manager and number of lead managers, exhibited a negative yet insignificant impact on initial returns. A large number of reputed lead managers have greater access to resources and market information, thus, minimising efficient pricing and lower level of initial returns (Dhamija & Arora, 2017a).

Table 7.

Regression Output.

Explanatory Variables	DV: Log Underpricing
	Model 1 (Route Variable)			Model 2 (CV 1)			Model 3 (CV 2)			Model 4(All)
	Coeff.	SE	t	Coeff.	SE	t	Coeff.	SE	t	Coeff.	SE	t
Intercept	4.335	0.033	130.87 *	4.206	0.079	53.48*	4.243	0.270	15.71*	4.28	0.289	14.82*
D_ER	0.013	0.040	0.742							–0.008	0.037	–0.224
Log_Age				0.051	0.028	1.802				–0.004	0.026	–0.163
D_Tech				0.007	0.056	0.132				–0.029	0.049	–0.59
Log_IS							0.002	0.021	0.072	0.000	0.022	–0.018
Log_OS							0.114	0.011	10.87*	0.115	0.011	10.72*
D_OFS							–0.001	0.041	–0.029	0.003	0.043	0.073
D_FC							–0.057	0.046	–1.247	–0.058	0.046	–1.244
Log_NLM							–0.028	0.038	–0.740	–0.027	0.038	–0.70
D_LM							–0.057	0.050	–1.150	–0.058	0.051	–1.133
Log_LD							–0.008	0.060	–0.127	–0.011	0.061	–0.175
R	0.021			0.112			0.579			0.580
R sqr	0.000			0.013			0.335			0.336
Adjusted R sqr	–0.033			0.005			0.317			0.309
F-statistic (sig.)	0.109 (0.742)			1.624 (0.199)			18.074 (0.000)*			12.558 (0.000)*
D-W Value	1.872			1.929			1.963			1.964
VIF	<5			<5			<5			<5

Source: The authors.

Note: This table briefly states the regression analysis output of different models executed. Model 1 test for only entry route and its role in assessing the underpricing. Model 2 covers firm related characteristic variables impact’, such as age and industry, on underpricing. Model 3 studies issue related factors affecting underpricing, like issue size amount, listing delay, overall subscription ratio, lead manager reputation, number of lead managers and type of sale of issue. Model 4 combines all the variables from the other models and tests them together to predict the level of underpricing. R² determines the percentage of explanation provided by the specified variables, while adjusted R² gives R² fine-tuned to the number of predictors variables and the sample size. F-statistic describes the goodness of fit of the model, while Durbin–Watson Value between 0 and 2 verify the existence of auto correlation between variables. VIF < 10 implies no multi collinearity in the variables.

* denotes statistical significance at 0.05 level

Further, the R-square and adjusted R-square of the model lay at 33.6 per cent and 30.9 per cent, respectively. The low R-square reconciles with the ex-ante uncertainty hypothesis of underpricing, which refers to the positive relationship between uncertainty and the expected initial return of an investor (Sehgal & Sinha, 2013). Consequently, it signifies the non-predictability of actual returns of IPOs.

V. Conclusions and Implications

Scholars, around the world, have different opinions regarding the issue’s initial performance. For our study, the average underpricing across the sample turned out to be 11.51 per cent. Further, the average underpricing of profitability route issues was not significantly different from the average underpricing of QIB route issues. The differences in only mean age and mean listing delay of profitability and QIB route issues were found to be significant at 95 per cent confidence level. Industry, type of sale and lead manager rank indicated significant association with entry norms. Further, multiple regression analysis revealed the negative, yet insignificant, coefficient of entry norm with the initial level of returns. Among the control variables, issue size, subscription rate and offer for sale were found to have a positive relationship with underpricing, while age, industry, fresh issue sale, lead manager’s rank, number of lead managers and listing delay had a negative relationship with underpricing. Except over-subscription, none of the variables’ relationship was significant.

In summary, the study may be considered promising for all stakeholders. Entry norms appear as a supplementary tool that allows a greater number of firms to raise funds in the IPO market, without affecting its performance or its ability to raise funds. Higher stake to QIB in the alternative route ensures investor protection too. Profits and assets have no longer remained a bar to enter the markets. The findings are consistent with SEBI’s aim of introducing the alternate entry route, ‘…to provide sufficient flexibility and also to ensure that genuine companies are not limited from fund raising on account of strict parameters…’. These claims cannot be refuted empirically. The regulation provides enough flexibility for unlisted companies to issue shares in the primary market.

This study has important implications for all the market participants. It will help issuers in the decision-making process of ‘when to enter’. If issuers wish to plan their IPO and expect QIBs to subscribe to their issue, then they should not wait to list its issues through the profitability route. They can raise an equal amount of funds via the QIB route too. Investors, too, should be indifferent to the entry route while expecting an initial return. Lastly, regulators should bring in more such regulations, so that more genuine firms attain flexibility to raise funds in IPO markets. This regulation could be introduced to SME exchange as well.

Our study, like most, has limitations. Unavailability of investor-wise-trading data restricted the analysis. This work can be further enhanced by incorporating corporate governance variables. Moreover, a similar study can be undertaken for SME and international exchanges too.

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.

ORCID iD

Tripti Goel

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