Earnings Management and IPO Anomalies—Evidence from Indian Stock Market

Abstract

This study examines the relationship between the extent of earnings management in a firm, the level of underpricing during an initial public offering (IPO), and their long-term performance. Earnings management has been acknowledged as a matter of concern during IPOs since long; however, its relationship with underpricing and long-term returns remained inconclusive in emerging markets like India. Using a sample of Indian IPO firms, this study finds that firms that manage accruals aggressively in the pre-IPO period have high initial returns and subsequently low stock returns in the post-IPO period. This study also observed that firms that have used abnormal accruals more conservatively while reporting earnings have better returns in the third year after IPO compared to the firms that reported more aggressively. The results are in consonance with the theory of information asymmetry and suggest that valuation of an IPO firm becomes ambiguous with high level of earnings management, which leads to higher underpricing.

Keywords

Initial public offering (IPO)earnings management discretionary accruals underpricing long-term performance

Introduction

Going public or initial public offering (IPO) is an important stage in the life of a young company. It opens arms to public equity capital and reduces the cost of funding the company’s operations and investments. An IPO facilitates a platform for trading the company’s shares, giving an opportunity to its existing shareholders to diversify their investments and elucidate their capital gains and making the company considerate for venture capitalists. It brings the company into public attention, which also brings indirect benefits, namely, enhancing a distinct character of the manager. Also, the company becomes accountable to a larger group of shareholders and has new assignments in the form of transparency and disclosure requirements. Numerous studies show that IPOs are eventually underpriced, which means that the price at which stocks are offered to the public is lower than its actual value (Hanley, 1993; Ibbotson, 1975; Ritter, 1984). An important explanation for the underpricing of IPOs is the information asymmetry between informed and uninformed investors given by Rock (1986). Informed investors are more aware of the new firm’s prospects than uninformed investors. Therefore, informed investors are more likely to invest in profitable new issues, leaving the unprofitable ones for uninformed investors. Contemplating this possibility, uninformed investors will participate only if shares are offered at a lower price than their actual intrinsic value in return for expected losses on less attractive issues. Beatty and Ritter (1986), Koh and Walter (1989), and others provide empirical evidence consistent with Rock’s (1986) model.

Information asymmetry during IPOs gives the management an opportunity to involve in earnings management practices and realize gains attached to an IPO. When a firm decides to raise an IPO, no predetermined market price is available before the shares are sold to the potential investors. Therefore, to fix an offer price and to regulate the demand issuers and investors must use the available nonprice information about the IPO firm. One of the sources of information used by underwriters and investors to fix an offer price is the financial statements reported in the prospectus at the time of IPO. It is considered to be the most cost-effective and convenient way of gaining information about an issuing firm. Mostly, IPO firms are small and no historical information is readily available to investors from other sources. Hence, they have to largely depend on the financial statements reported in the prospectus to evaluate the firm. The requirement to fix an offer price before the share starts to trade in the aftermarket and the role of accounting information in setting an offer price give an opportunity to issuers to adopt earnings management practices before an IPO. Empirical evidence reveals that earnings reported in the pre-IPO period have its significant effect on the initial valuation of the firms (Ball & Shivakumar, 2008; DuCharme et al., 2001; Erickson & Wang, 1999; Friedlan, 1994; Gramlich & Sørensen, 2004; Morsfield & Tan, 2006; Teoh et al., 1998a,b). The offer price at which the equity is offered to the public directly impacts the wealth of the issuing firm.

Furthermore, several studies also observed that IPO firms that managed earnings in the pre-IPO period had seen a low stock performance in the post IPO period (Goergen, 1998; Schaub & Highfiled, 2004; Teoh et al., 1998a). Firms tapping the IPO market may get into a tendency of reporting relatively inflated earnings, as compared to the actual, by adjusting the “discretionary accruals.” Investors with less information may not be able to detect these adjustments and may end up paying a higher price for the shares. However, sooner, these inflated earnings would be discovered by the analysts and revealed to the market at large. By that time, the shares would be trading at much below the offer price, and the investors would realize that the post-IPO performance of the firm has been below their expectations. Hence, the investors will lose their optimism leading to diminishing returns in the post-IPO period (Teoh et al., 1998b).

Although, it is a well-established phenomenon that IPO firms manage earnings before raising an IPO. However, the relationship of underpricing and post IPO returns with pre-IPO accruals management in an emerging market like India is being largely ignored. Several studies from developed markets showed that, generally, pre-IPO earnings management had a positive association with initial returns (Friedlan, 1994; Nagata, 2013; Nagata & Hachiya, 2007). However, studies such as Francis et al. (2012) and Armstrong et al. (2009) found the contradictory results. Hence, using the data from an emerging market like India, this study aims to examine two issues: to investigate the relationship between pre-IPO earnings management and underpricing, and to examine whether firms that previously managed earnings in the pre-IPO period have subsequent low market returns in the post-IPO period. It would be interesting to examine the IPO firms from an Indian market because the degree of information asymmetry is high in emerging markets and ownership of public firms is largely concentrated in the hands of the founder family. Furthermore, codified rules such as corporate and labor laws as well as financial market regulations are created with the intent to complement market mechanisms. However, due to weak institutional and legal setup, these rules are mostly confined to the books. Hence, although Indian institutions are built on the foundation of market mechanisms, the effectiveness of these mechanisms in the country is not even close to that of developed nations (Motohashi, 2015). Hence, it is expected that IPO firms in India will try to gain the greatest offer price as it will directly impact the wealth of the founder family. Also, to achieve this, IPO firms are more likely to involve in earnings management practices as the institutional and regulatory environment is weak. Later, in the post-IPO period, when the investors’ optimism will decrease, they will adjust the inflated earnings resulting in poor stock performance.

Similar to previous empirical evidence, this study found a positive relationship between underpricing and earnings management. This study also observed declining returns in the post-IPO period for firms that aggressively managed earnings in the pre-IPO period. This study is one of the few studies (Gao et al., 2015; Mangala & Dhanda, 2019; Nagata, 2013; Purayil & Jijo Lukose, 2019) that examined the association between earnings management and underpricing and subsequent returns among IPO firms in the context of an emerging market. The rest of this article is organized as follows. The second section reviews the literature and develops the hypotheses. The third section discusses the methodology. The fourth section describes the descriptive statistics. The fifth section presents and discusses the empirical results. The sixth section presents an additional analysis. Finally, the seventh section concludes this study.

Literature Review and Hypothesis Development

IPO has been acknowledged as a classic opportunity for managers to involve in earnings management practices. Specifically, a firm that is raising an IPO is surrounded by a lot of uncertainties. The insiders of the firm are well aware of the prospects of an IPO firm; however, prospective investors and other stakeholders have to depend upon the accounting information provided in the prospectus, which makes it difficult for them to value the firm (Roosenboom et al., 2003). Hence, during an IPO, the uncertainty about the issuing firm is high, which provides the issuers with an opportunity to indulge into earnings management practices (Aharony et al., 1993; Darrough & Rangan, 2005; DuCharme et al., 2001; Friedlan, 1994; Roosenboom et al., 2003; Teoh et al., 1998, 1998a,b). Issuing firms have great opportunity to draw high proceeds from investors and create a positive image of the firm’s prospects by using aggressive earnings management policies (Aharony et al., 2000; Brau & Fawcett, 2006; Gramlich & Sorensen, 2004). In reaction to this positive picture, investors who are less informed will more actively bid for IPO shares, pushing the stock prices beyond rational short-term valuation. The literature evidenced a positive relationship between pre-IPO earnings management and underpricing. Studies like Bhattacharya et al. (2003), in their cross-country analysis, found a positive relationship between a country’s overall level of earnings management and the value of equity in a country’s stock market. Similarly, Boulton et al. (2011) showed that countries that provided high-quality financial reports during an IPO were found to be less underpriced. Several studies from the US, UK, and Japan (Friedlan, 1994; Nagata & Hachiya, 2007) also observed similar results. However, few studies like Francis et al. (2012) and Armstrong et al. (2009) found a negative relationship between the two.

While firms raising an IPO have large initial returns but in the long run, the performance of their stock diminishes. Studies argue that in the post-IPO period, more information about the firm is revealed to the investors, leading to decreased optimism, which was created in the pre-IPO period through earnings management. Thereby, investors then start to mark down the value of the firm more aggressively, resulting in poor post-IPO stock performance (Bhatia & Singh, 2009; Ritter & Welch, 2002; Teoh et al., 1998a,b). Teoh et al. (1998b) in their study evidenced that IPO firms have high reported earnings and abnormal accruals in the pre-IPO period, but later, their long-term performance of the stock is not good. Similarly, Teoh et al. (1998a) found that IPO firms that conservatively used abnormal accruals for managing their reported earnings have better stock returns in their third year after an IPO as compared to firms who used accruals more aggressively. Furthermore, Venkataraman et al. (2008) found in their study that pre-IPO accruals are negative and less than post-IPO accruals. Similar results have emerged from the United States (DuCharme et al., 2001; Teoh et al., 1998a), The Netherlands (Roosenboom, et al., 2003), and China (Aharony et al., 2010; Gao et al., 2015; Shen et al., 2014). Some related studies in India also found a negative relationship between pre-IPO earnings management and long-term performance of the firm’s stock (Mangala & Dhanda, 2019; Purayil & Jijo Lukose, 2019). However, studies like Ball and Shivakumar (2008) and Fan (2007) found a contradictory result. Hence, although the relationship between underpricing and long-term performance of IPOs with earnings management has been well evidenced in the developed economies, it remained inconclusive in the emerging markets like India and needs further investigation. Therefore, based on the review of extant literature, the following hypotheses have been formulated.

H₁: There exists a positive relationship between underpricing and earnings management.

H₂: Firms that conservatively use abnormal accruals to report earnings will have better long-term performance than the firms that use abnormal accruals aggressively.

Methodology

Data and Sample Description

The IPO sample consists of 165 Indian offers from 2007 to 2015. The data were obtained from the Thomson Reuter, Prowess, and Prime database. The study period of 2007–2015 was selected because for analyzing the pattern of accruals earnings management, the study required at least two years prior and two years subsequent data. The sample included IPOs as issues of common stocks by Indian issuers listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). However, the sample excluded:

IPOs that lack annual financial statement data for the year prior to the IPO filing date, the offer year, and the subsequent two years;

spin-offs;

reverse leveraged buyouts;

closed-end funds, unit investment trusts, real estate investment trusts, and limited partnerships;

rights and standby issues;

simultaneous or combined offers of several classes of securities, such as the unit offer of stocks and warrants;

nondomestic and simultaneous domestic–international offers.

The study restricted the sample to all nonfinancial firms with available data. At least eight observations in each two-digit National Industrial Classification (NIC) grouping per year were required to estimate the model. This study ensured that each firm-year observation had the required data needed to calculate the discretionary accruals a proxy for accruals earnings management.

Measurement of Variables

Underpricing

Initial raw returns and market-adjusted initial returns (MAAR) are the widely used techniques to capture underpricing. The only difference between the two methods is that MAAR is adjusted by market index changes on a corresponding day to ward off overvaluation by initial raw returns (Agathee et al., 2012; Jain & Padmavathi, 2012; Purayil & Jijo Lukose, 2019). Since the goal of the study was to examine the association between underpricing and earnings management, the present study has also chosen the initial raw return as a proxy for underpricing to analyze the relationship between underpricing and earnings management. The market-adjusted initial return ( ${M A A R}_{x, i}$ ) was also used to ensure that results are reliable. The study has also calculated ${M A A R}_{x, i}$ for stock “x” for the first trading day, the first year, second year, and third year to analyze the long-term returns of the stock.

$R_{x, i}$ is expressed mathematically as follows:

R_{x, i =} \frac{P_{x, i} ̶ P_{x, 0}}{P_{x, 0}}

(1)

where

$R_{x, i}$ = return on stock “X” at the end of the first day trading day, the first year, second year, and third year.

$P_{x, i}$ = price of stock “X” at the end of the first trading day, first year, second year, and third year.

$P_{x, 0}$ = offer price of stock “X.” i represents the first trading day

The NSE and BSE index was used as the benchmark and was measured as follows:

R_{m, i =} \frac{N_{m, i} ̶ N_{m, 0}}{N_{x, 0}}

where $R_{m, i}$ = market return at the close of the first trading day.

$N_{m, i}$ = the market index value at the end of the first trading day, first year, second year, and third year

$N_{m, 0}$ = the market index value on the offer day of stock x, first year, second year and third year

The market-adjusted abnormal return ( ${M A A R}_{x, i}$ ) for stock “x” after the first trading day, the first year, second year, and third year is mathematically expressed as follows:

{M A A R}_{x, i = 100 ×} \{\frac{(1 + R_{x, i})}{(1 + R_{m, i})} - 1\}

(2)

The market-adjusted model measures the initial trading returns in excess market-return form.

Accrual-based Earnings Management

Earnings management cannot be directly observed from the financial statements of the company it needs to be measured. The study has adopted discretionary accruals as a proxy for earnings management. Different methods have been evolved by the researchers to measure discretionary accruals. One such method is the modified Jones model (1991), which is a widely accepted technique to capture accrual earnings management. Hence, the study has also adopted the modified Jones model (1991) to gauge discretionary accruals.

The model was estimated for every year and industry grouped by its two-digit NIC code. This technique helps to control for changes occurring in the industry’s economic conditions that influence total accruals and also allows the coefficients to differ across time (DeFond & Jiambalvo, 1994; Kasznik, 1999). For measuring discretionary accruals, the following cross-sectional model was estimated for each two-digit NIC-year grouping:

\frac{{T A}_{i t}}{{A s s e t s}_{i, t - 1=}} K_{1} \frac{1}{{A s s e t s}_{i, t - 1}} {+ K}_{2} \frac{{Δ S a l e s}_{i t}}{{A s s e t s}_{i, t - 1 +}} K_{3} \frac{{P P E}_{i t}}{{A s s e t s}_{i, t - 1+}} ε_{i t}

(3)

The coefficients from (1) were used to estimate the firm-specific normal accruals ( ${N A}_{i t}$ )

{N A}_{i t=} K_{1} \frac{1}{{A s s e t s}_{i, t - 1}} {+ K}_{2} \frac{{Δ S a l e s}_{i t} - {Δ R E C}_{i t}}{{A s s e t s}_{i, t - 1+}} K_{3} \frac{{P P E}_{i t}}{{A s s e t s}_{i, t - 1}} {+ε}_{i t}

(4)

where for fiscal year t and firm i, TA represent the total accruals defined as

${T A}_{i t}$ = ${Δ C u r r e n t A s s e t s}_{t}$ – ${Δ C a s h}_{t}$ − ${Δ C u r r e n t L i a b i l i t i e s}_{t}$ + ${Δ S h o r t t e r m d e b t}_{t}$ − ${D e p r e c i a t i o n}_{t}$ .

${A s s e t s}_{i, t - 1}$ represents total assets, ${Δ S a l e s}_{i t}$ is the change in revenues from the preceding year, ${Δ R E C}_{i t}$ is the change in receivables from the preceding year, and ${P P E}_{i t}$ is the gross value of property, plant, and equipment.

{D A}_{i t +} \frac{{T A}_{i t}}{{A s s e t s}_{i, t - 1}} {− N A}_{i t}

(5)

Where ${D A}_{i t}$ is our measure of discretionary accruals, which is the difference between total accruals and the fitted normal accruals.

Empirical Model Specification

The following regression equations were developed to examine the relationship between underpricing and accruals earnings management:

{l o g U P}_{i t =} β_{0 +} β_{1×} {D A C C}_{i t +} β_{2×} {\log l e v}_{i t +} β_{3 ×} {\log M B}_{i t +} β_{4 ×} {\log a g e}_{i t+} β_{5 ×} {\log i s s u e s i z e}_{i t} + β_{6×} {A U D}_{i t +} β_{7 ×} {U W R}_{i t +} β_{8 ×} {C r i s i s}_{i t} {+ β}_{9×} {I n d u s}_{i t +} β_{10} {×Y e a r}_{i t} {+ ε}_{i t}

(6)

{l o g M A A R}_{i t =} β_{0 +} β_{1 ×} {D A C C}_{i t+} β_{2×} {\log l e v}_{i t+} β_{3×} {\log M B}_{i t +} β_{4×} {\log a g e}_{i t +} β_{5 ×} {\log i s s u e s i z e}_{i t} + β_{6×} {A U D}_{i t +} β_{7 ×} {U W R}_{i t} + β_{8×} {C r i s i s}_{i t+} β_{9×} {I n d u s}_{i t+} β_{10×} {Y e a r}_{i t +} ε_{i t}

(7)

Table 1.

Description of Dependent and Independent Variables.

Variables	Description
Dependent variable
${l o g U P}_{i t}$	Measured as the difference of closing price on the first day of trading and the offer price for IPO firm i
${l o g M A A R}_{i t}$	Market-adjusted abnormal return ( ${M A A R}_{x, i}$ ) for stock “x” after first trading day, first year, second year, and third year
Independent variable
${D A C C}_{i t}$	Discretionary accruals for year t measured through the modified Jones model (1990)
${l o g l e v}_{i t}$	Firm’s leverage for year t, measured by the ratio of debt to total assets
${\log M B}_{i t}$	Natural logarithm of firm’s market-to-book ratio for year t, measured by the ratio of market value to book value of equity
${\log a g e}_{i t}$	Age of the firm at the time of IPO measured as firm’s incorporation year minus date of listing
${\log i s s u e s i z e}_{i t}$	Natural logarithm of gross proceeds collected in an IPO by firm i in crore rupees
${A U D}_{i t}$	Dummy variable which takes the value 1 if an IPO firm i is audited by big four auditors in India; otherwise zero (0)
${C r i s i s}_{i t}$	Dummy variable which takes the value 1 if the IPO firm i is listed in the year 2008; otherwise zero
${U W R}_{i t}$	The amount of capital raised divided by the number of lead underwriters involved in the issue.
${I n d u s}_{i t}$	Dummy variable takes the value 1 if the IPO firm i belong to particular industry; otherwise zero
${Y e a r}_{i t}$	Dummy variables take the value 1 if the IPO of firm i is raised in a particular year; otherwise zero

Source: The authors.

Equations (6) and (7) test the relationship between underpricing and discretionary accruals. Both $l o g U P$ and $l o g M A A R$ were used as a proxy for initial returns (underpricing). However, to check the relationship between discretionary accruals and the long-term returns of the firm, the study used $M A A R$ calculated for the first, second, and third years. To control for other cross-sectional effects, some control variables were included in the regression models (Table 1). Prior IPO literature on underpricing evidenced that auditor reputation is negatively related to the level of underpricing. Hence, big four auditors were recognized using the prime database. The auditors’ ranks were adopted from the Prime database group. The auditors’ ranks provided in the prime data base group were based on number of companies audited by an auditor, on the basis of market capitalization of companies audited and ranking by audit fee on standalone basis. The present study adopted the ranks based on market capitalization of companies audited (Agathee et al., 2012; Balvers et al., 1998; Gao et al., 2015). The study also included underwriter’s reputation ${U W R}_{i t}$ as a control variable as it has been identified in the literature as an important factor which impacts the level of underpricing (Arora & Singh, 2019; Dimovski et al., 2011; Kirkulak & Davis, 2005). Carter and Manaster (1990) and Carter et al. (1998) observed that underwriters with high reputation facilitates certification of superiority and were also related to new issues, which were less underpriced. Literature identified two proxies to measure underwriter reputation, which includes measure by Carter and Manaster (1990), which made use of relative positioning in stock offering tombstone announcements, and measure by Megginson and Weiss (1991), which used the relative market share of the underwriters based on their share of IPO capital raised. Previous studies that used these two proxies for measuring underwriter reputation evidenced an inverse relationship between an underwriter reputation and underpricing (Beatty & Ritter, 1986; Johnson & Miller, 1988; Reutzel & Belsito, 2015; Sundarasen et al., 2018). However, some further studies also evidenced a positive association between underwriter reputation and underpricing (Arora & Singh, 2019; Dimovski et al., 2011; Gao et al., 2015; Liu & Ritter, 2011). Therefore, similar to Megginson and Weiss (1991) market share approach, the present study calculated the reputation proxy as the proportion of the amount of capital raised by each underwriter. Where there are several lead underwriters sharing the same issue, the amount of capital raised is divided by the number of lead underwriters involved in that issue. Top 10 underwriters identified during the study period (2007–2015) were Kotak Mahindra Company, Emam Financial Consultants Pvt. Ltd., ICICI securities & finance co. Ltd., Edelweiss securities Pvt. Ltd., DSP Merrill Lynch, Anand Rathi securities, Karvy Investors services Ltd., IL & FS Investmart Ltd., and Citi group global markets Pvt. Ltd.

Similarly, leverage (loglev), Market to book ratio (logMB) (Pham et al., 2003; Zingales, 1995), firm age (age), issue size (logissuesize) (Chalk & Peavy, 1990; Clarkson & Merkley, 1994; Ritter, 1987), financial year crisis (Crisis) (Gao et al., 2015), and industry (Indus) and year (Year) dummies were used to control for industry- and yearwise fluctuations.

Multiple ordinary least squares regression (OLS) was used to test the above equations. It was observed that heteroskedastic errors render OLS estimators inefficient and induce bias in the corresponding standard errors (Miller & Startz, 2018); hence, the study used the white’s robust standard errors to avoid the problem of heteroskedasticity. Tolerance and the variance inflation factor were used to check multicollinearity. Therefore, the assumptions of OLS were confirmed.

Furthermore, for additional analysis of long-term returns of IPO firms, the sample IPO firms were further divided into two groups based on IPO-year discretionary accruals. The subsample with DACCs below or equal to the median value of discretionary accruals were called as “conservative” in comparison to the subsample with DACCs above the median value that were supposed to manage earnings “aggressively.” Then, raw returns and MAAR for the first trading day, first year, second year, and third year were compared for both the groups.

Descriptive Statistics

Table 2 reports the descriptive statistics for the underpricing of sample firms by IPO years and industry distribution.

Table 2.

Descriptive Statistics for Underpricing Year- and Industry wise.

	N	Mean	Min.	Med.	Max	Std.Dev.
Underpricing (165)
IPO year distribution
2007	30	02.87%	–29.88%	–02.89%	62.45%	23.80%
2008	21	07.80%	–89.22%	–11.55%	151.21%	57.59%
2009	6	03.66%	–50.20%	–03.32%	80.00%	43.44%
2010	26	08.29%	–37.03%	01.57%	66.87%	25.31%
2011	15	00.67%	–51.36%	–04.70%	76.41%	47.24%
2012	6	02.17%	–04.97%	01.08%	09.96%	04.97%
2013	8	02.43%	–04.75%	04.45%	05.00%	03.76%
2014	16	00.58%	–08.91%	00.34%	05.53%	04.35%
2015	37	02.39%	–06.06%	04.93%	12.31%	04.81%
Industry distribution
Chemical	32	10.19%	–51.36%	–0.17%	92.38%	36.22%
Construction materials	4	30.20%	–29.88%	–0.25%	151.21%	81.98%
Consumer goods	5	1.49%	–2.37%	0.89%	6.36%	3.42%
Food and agro	9	8.69%	–17.50%	4.95%	46.33%	23.32%
Machinery	22	10.51%	–49.61%	2.97%	81.70%	29.815
Metal and metal products	28	–0.40%	–89.22%	–1.96%	66.87%	33.05%
Miscellaneous Manufacturing	37	–4.46%	–50.20%	1.32%	47.26%	23.01%
Textile	19	5.01%	–28.40%	0.71%	62.45%	26.94%
Transportation equipment	9	–6.71%	–42.21%	–4.61%	5.53%	16.34%
Total	165	3.89%	–89.22%	0.71%	151.21%	31.07%

Source: The authors.

Note: The table presents the descriptive statistics for underpricing for both year- and industrywise. The table reports the mean, medium, maximum, minimum and standard deviation of underpricing occurring in different year as well as industry.

The table shows that annual averages of ${U P}_{t}$ were all positive, and the level of underpricing varied during the sample period. The year 2015 had the highest number of IPOs (37). The highest average underpricing was detected in 2010 (8.29%), and the lowest average underpricing was observed in 2014 (0.58%).

The table also divides sample firms into different industry groups classified by its two-digit NIC code. Sample firms from all industries were underpriced except for metal and metal products, miscellaneous manufacturing and transportation equipment industries. IPO firms from the chemical, construction materials, food and agro, and machinery had high levels of underpricing, with average initial raw returns ranging from 8.69% to 30.20%. Firms from consumer goods, metal and metal products, miscellaneous manufacturing, textile, and transportation equipment had low levels of underpricing, with average initial raw returns ranging from –6.71% to 5.01%. The vast majority of sample firms were from miscellaneous manufacturing with a low level of underpricing –4.46%.

Empirical Evidence on Accruals Earnings Management Around IPOs

Table 3 presents the time series of accruals earnings management of sample IPO firms by IPO years and industry distribution. It reports the median discretionary accruals scaled by total assets at the beginning of the period, for years –1 to +2 corresponding to the year of the IPO. The differences in medians were tested using the Wilcoxon Signed Rank test.

Table 3.

Time Series Profile of Accruals–based Earnings Management from IPO year –1 to +2.

Year	–1	0	+1	+2
Discretionary accruals
2007	16.94	12.73 (0.65)	12.56 ( 0.94)	04.08 (0.12)
2008	00.02	00.25 (0.01)^***	00.09 (0.27)	00.04 (0.21)
2009	00.04	00.04 (0.75)	00.00 (0.34)	–00.04 (0.91)
2010	00.06	00.08 (0.36)	00.13 (0.07)^*	00.01(0.00)^***
2011	00.03	00.54 (0.01)^***	00.06 (0.03)^**	00.03 (0.82)
2012	00.38	00.26 (0.46)	00.12 (0.34)	00.01 (0.46)
2013	00.24	00.28 (0.57)	00.05 (0.32)	00.12 (0.88)
2014	00.00	00.03 (0.04)^**	-00.01 (0.16)	00.01 (0.91)
2015	00.04	00.09 (0.86)	00.03 (0.04)^**	–00.00 (0.66)
Industry
Discretionary accruals
Chemical	00.03	00.08 (0.20)	00.03 (0.83)	00.03 (0.25)
Construction materials	00.10	00.31 (0.07)^*	00.06 (0.07)^*	00.17 (0.50)
Consumer goods	–00.01	00.06 (0.50)	00.04 (0.50)	00.03(0.89)
Food and agro	00.23	00.15 (0.44)	00.04 (0.37)	00.08 (0.59)
Machinery	–00.04	00.08 (0.05)^**	00.09 (0.48)	00.02 (0.59)
Metal and metal Products	00.11	00.10 (0.87)	00.05 (0.16)	00.03 (0.58)
Miscellaneous Manufacturing	00.08	00.13 (0.33)	00.10 (0.60)	00.04(0.01)^***
Textile	00.08	00.18 (0.22)	00.11 (0.24)	–00.01 (0.14)
Transportation Equipment	00.11	00.11 (0.76)	00.09 (0.31)	–00.01 (0.59)

Source: The authors.

Note: The table represents the time series of accrual-based and real earnings management proxies from year –1 to year +2 relative to IPOs (year 0). Discretionary accruals are computed using the cross-sectional modified Jones model (1991). Differences in medians are tested using the Wilcoxon Signed Rank test where ***, **, and * denote significantly different from zero at 1%, 5%, and 10% levels, respectively (two-tailed).

The study found that annual averages of ${D A C C}_{t}$ were all positive in the year of the IPO. Also, the overall median percentage of discretionary accruals in the IPO year was substantially increased than one year prior to the IPO year and gradually decreased in the subsequent two years. This indicated the reversal effect of earnings management post-IPO. The year 2007 showed the highest median percentage, that is, 12.73%, and 2014 showed the lowest level of discretionary accruals of IPO sample firms, which was 0.03%.

Table 3 also presents the industrywise averages of

{D A C C}_{t}

. It shows positive discretionary accruals in the year of IPO. Also, the overall median percentage of discretionary accruals in the IPO year was substantially increased than one year prior to the IPO year and gradually decreased in the subsequent two years.

Table 4.

Mean Difference of First Day and First, Second, and Third Year Returns.

Year		Raw Return	MAAR	T-Stats
Raw returns		Mean	Mean
2007	First day	02.87%	03.30%	.
	First year	–07.10%	–06.32%	0.75
	Second year	–66.58%	–66.69%	0.00^***
	Third year	–39.28%	–39.49%	0.00^***
2008	First day	07.80%	07.81%	.
	First year	–54.66%	–54.80%	0.00^***
	Second year	–32.41%	–32.46%	0.10^*
	Third year	–54.77%	–55.23%	0.00^***
2009	First day	03.66%	04.44%	.
	First year	110.02%	110.19%	0.14
	Second year	54.54%	55.15%	0.35
	Third year	34.13%	34.13%	0.56
2010	First day	08.30%	08.07%	.
	First year	02.06%	02.81%	0.65
	Second year	–04.95%	–05.16%	0.56
	Third year	–12.87%	–12.85%	0.75
2011	First day	00.68%	00.705	.
	First year	–11.66%	–11.59%	0.45
	Second year	–55.82%	–55.80%	0.00^***
	Third year	–44.02%	–43.97%	0.09^*
2012	First day	02.17%	02.35%	.
	First year	–09.78%	–09.67%	0.52
	Second year	–04.60%	–04.62%	0.81
	Third year	–10.99%	–11.29%	0.76
2013	First day	02.43%	02.65%	.
	First year	272.99%	274.35%	0.13
	Second year	293.61%	296.10%	0.18
	Third year	162.55%	163.12%	0.03^**
2014	First day	00.58%	00.84%	.
	First year	87.47%	87.79%	0.01^***
	Second year	120.11%	119.75%	0.01^***
	Third year	336.50%	336.41%	0.03^**
2015	First day	02.39%	02.35%	.
	First year	41.95%	42.54%	0.06^**
	Second year	122.80%	122.71%	0.00^***
	Third year	143.45%	143.83%	0.00^***

Source: The authors.

Note: The table represents the time series of initial raw returns and market-adjusted abnormal returns of IPO firms. The table reports the mean values of first day, first year, second year, and third year returns for all the firms that went for IPO during the period 2007–2015. Differences in means are tested using two group mean comparison test where, ***, **, and * denote significantly different from zero at 1%, 5%, and 10% levels, respectively.

Table 4 presents the average raw returns and market-adjusted abnormal return (MAAR) on the first day, first year, second year, and third year for all the firms that raised an IPO during the period 2007–2015. The table presents that most of the firms had positive and high returns on the first day or first year of trading; however, by the third year, the returns reduced or became negative. Differences in the mean between two returns were tested using a two-group mean comparison test.

Results and Discussion

Table 5.

Relationship Between Underpricing and Abnormal Accruals.

	Model I	Model II	Model III
Variables	${l o g U P}_{i t}$ Coefficients (t-value)	${l o g M A A R}_{i t}$ (First day)Coefficients (t-value)	${l o g M A A R}_{i t}$ (Third year)Coefficients (t-value)
${D A C C}_{i t}$	0.10 (2.54)^***	0.06 (1.46)^***	–0.26 (–2.16)^**
${\log l e v}_{i t}$	0.06 (1.20)	0.03 (2.35)	–0.14 (–3.36)^*
${\log M B}_{i t}$	–0.67 (–4.14)^*	–0.26 (–2.24)^*	0.20 (1.42)^**
${\log a g e}_{i t}$	–0.02 (–0.15)	–0.08 (–0.24)	0.06 (0.65)
${\log i s s u e s i z e}_{i t}$	–0.54 (–5.32)^*	–0.54 (–5.24)^**	–0.50 (–2.54)^**
${A U D}_{i t}$	–0.06 (–1.42)^***	–0.06 (–1.12)^***	0.08 (2.26)^*
${U W R}_{i t}$	0.42 (1.24)^**	0.42 (1.10)^**	0.62 (1.16)^*
${C r i s i s}_{i t}$	0.00 (–0.08)	0.00 (–0.06)	0.00 (–0.10)
${I n d u s}_{i t}$	Yes	Yes	Yes
${Y e a r}_{i t}$	Yes	Yes	Yes
R-squared	00.76	00.75	00.84
Adjusted R-squared	00.68	00.67	00.76

Source: The authors.

Note: The table presents the regression results for dependent variable, that is, underpricing (log UP), market-adjusted abnormal return (logMAAR), independent variable discretionary accruals (logDACC), leverage (loglev), market-to-book ratio (log MB), age of the firm (logage), and issue size (logissuesize). Discretionary accrual is a proxy for earnings management measured through the modified Jones model (1991).

The multivariate regression results from (6) and (7) were reported in Table 5. Model I presents (1). The dependent variable was underpricing and the independent variable was discretionary accruals measured through the modified Jones model (1990). The coefficient value for DACC (discretionary accruals) was found to be 0.10 (t = 2.54) positive and significant at 1% level. Similarly, in Model II, where MAAR was the dependent variable, the coefficient value for DACC was 0.06 (t = 1.46) also found to be positive and significant at 1% level.

The study also estimated the regression model by changing the dependent variable, MAAR calculated for the third year in model III. The coefficient value of DACC was found to be –0.26 (–2.16) negative and significant at 5% level. These results indicate that IPO firms managing accruals in the pre-IPO period will have high initial returns, but in the post-IPO period, the returns will diminish. The plausible reason is that initial investors were overoptimistic about the prospects of the IPO firms but later when the true performance of the firm is revealed the investor’s optimism decreased, and they marked down the value of the firm. The other way to look at this phenomenon, particularly in India, is that firms that engage in aggressive earnings management, prior to IPO, would have to go for a “course correction” and that would eventually reduce the earnings and the price, post-IPO. Similar results were also evidenced in related studies on India (Mangala & Dhanda, 2019; Purayil & Jijo Lukose, 2019; Shette et al., 2016). Shette et al. in their study showed that new issue firms performed poorly in the long run. They also evidenced that abnormal discretionary accruals were higher in the IPO year than the subsequent periods. However, studies like Fan (2007) and Ball and Shivakumar (2008) found a contrasting relationship between underpricing and earnings management.

Four controlled variables were found to be significant in all three models. Precisely, the coefficient value of logMB (market to book ratio) was found to be –0.67 (–4.14) and –0.26 (–2.24) negative and significant in models I and II and 0.20 (1.42) positive in model III. This suggests that high-growth firms will have low underpricing and high post-IPO returns. Similar results were also observed by Zingales (1995) and Pham et al. (2003). They argued that for firms expecting low post-IPO returns, it is more advisable to negotiate with a prospective investor in spite of selling it to a large number of shareholders. Therefore, firms with high-growth prospects would not see any requirement to underprice their shares. Furthermore, the coefficient value of logissuesize (issue size) was found to be –0.54 (–5.32), –0.54 (–5.24), and –0.50 (–2.54) negative and significant in models I, II, and III, respectively. The coefficient value of logissuesize was found as expected. This result indicates that firms with low issue size will have high underpricing and low post-IPO returns. Several studies also evidenced similar results (Chalk & Peavy, 1990; Clarkson & Merkley, 1994; Jog & Riding, 1987; Ritter, 1987). Furthermore, the coefficient value of AUD (auditor reputation) was found to be –0.06 (–1.42) and –0.06 (–1.12) negative and significant at 1% level in models I and II and 0.08 (2.26) positive and significant at 10% level in model III. This result suggests that IPO firms with reputed auditors will have a low level of underpricing and high post-IPO returns. Balvers et al. (1988) in their study also found a negative relationship between auditor’s reputation and the level of underpricing. Quality auditors are very careful about their reputation; therefore, they will not endorse firms whose post-IPO performance will affect their credibility. They will only endorse firms whose prospects are good, resulting in the fact that investors will not ask the issuer to heavily discount the shares. Also, the coefficient value of UWR (underwriter reputation) was found to be 0.42 (1.24) and 0.42 (1.10) positive and significant in models I and II at 5% level and at 1% level in model III. Similar results were also observed in studies like (Arora & Singh, 2019; Dimovski et al., 2011; Kirkulak & Davis, 2005; Liu & Ritter, 2011). The plausible explanation for the positive relationship between an underwriter reputation and underpricing given in the literature is that high-reputation underwriters help in minimizing the information uncertainty about new issues. This provides further information about the quality of the new issue firm to the investors, which results in increased demand for IPO shares on the first trading day resulting in higher closing price and higher underpricing (Arora & Singh, 2019).

Additional Analysis

Table 6.

Median of Differences of First Day and First, Second, and Third Year Returns for Conservative and Aggressive Group.

Year		Conservative		Aggressive
		Raw return	MAAR	Raw return	MAAR
2007	First day	–12.96%	–14.45%	04.28%	04.68%
	First year	–56.73%^*	–55.93%^*	–16.48%	–13.99%
	Second year	–76.84%^***	–76.99%^***	–70.34%^***	–69.99%^***
	Third year	–65.33%^*	–65.54%^*	–54.12%^**	–53.98%^**
2008	First day	–27.69%	–26.89%	46.33%	42.93%
	First year	–61.06%^**	–60.62%^**	–75.72%^***	–75.96%^***
	Second year	–57.61%^**	–57.53%^**	–68.48%^*	–68.19%^*
	Third year	–53.85%^***	–53.40%^***	–76.50%^**	–76.46%^**
2009	First day	16.33%	15.77%	–04.71%	–05.24%
	First year	10.53%^*	10.53%^*	33.19%	33.50%
	Second year	19.93%^*	23.35%^*	–19.48%	–18.10%
	Third year	13.13%	14.05%	–59.90%	–60.31%
2010	First day	04.87%	03.18%	–00.91%	–00.30%
	First year	–10.72%^*	–10.75%	–26.29%	–26.12%
	Second year	–46.55%^**	–46.96%	–26.26%	–26.62%
	Third year	–38.07%^*	–38.50%^*	–78.96%	–79.40%
2011	First day	35.65%	34.46%	–36.67%	–36.01%
	First year	–05.89%	–05.46%	–29.92%	–29.87%
	Second year	–86.36%^**	–86.35%^**	–46.18%	–45.54%
	Third year	–88.37%^**	–88.35%^**	–63.00%^**	–62.84%^*
2012	First day	01.05%	01.28%	01.18%	01.40%
	First year	35.52%	35.74%	–35.06%^*	–35.30%^*
	Second year	31.19%	31.86%	–63.66%	–63.47%
	Third year	–32.10%	–32.58%	–61.15%	–60.67%
2013	First day	01.52%	02.13%	04.45%	04.02%
	First year	32.70%	33.03%	455.85%	458.30%
	Second year	54.73%^*	54.72%^*	222.73%	225.64%
	Third year	55.46%^*	57.04%^*	223.06%^*	221.56%^*
2014	First day	–29.06%	–30.06%	–01.61%	–01.75%
	First year	45.83%^*	45.59%^**	93.09%	93.09%
	Second year	57.70%^***	59.70%^*	111.57%	111.42%
	Third year	55.95%	56.00%^**	206.02%^**	206.12%^*
2015	First day	04.98%	03.28%	03.04%	02.70%
	First year	39.52%	39.34%	20.54%	20.72%
	Second year	163.31%^**	163.43%^***	80.63%^**	79.77%^**
	Third year	126.31%^***	127.11%^***	57.40%	58.97%

Source: The authors.

Note: The table represents the time series of initial raw returns and market-adjusted abnormal returns of IPO firms. The table reports the median values of first day, first year, second year, and third year returns for conservative and aggressive earnings management group. Differences in medians are tested using the Wilcoxon Signed Rank test where ***, **, and * denote significantly different from zero at 1%, 5%, and 10% levels, respectively.

For further analysis, IPO companies were divided according to the median value of abnormal accruals in the IPO year. Those with relatively low discretionary accruals were supposed to represent companies, which were not involved in earnings management practices or, if involved, tried to manage earnings more conservatively. On the other hand, companies with high discretionary accruals were perceived as those that managed earnings more aggressively and boosted their accounting profits. Raw returns and abnormal market-adjusted returns of the first day of trading, the first year, second year, and third year of trading was observed for both the groups.

Table 6 presents the median value of raw returns and MAAR. In the year 2007, the median value for a conservative group on the first trading day was –12.96% (raw return) and –14.45% (MAAR) and –65.33% (raw return) and –65.54% (MAAR) in the third year. Similarly, for the year 2009, the return on a first trading day was 16.33% (raw return) and 15.77% (MAAR) and 13.13% (raw return) and 14.05% (MAAR) in the third year. In the year 2007, the median value for the aggressive group on the first trading day was 04.28% (raw return) and 04.68% (MAAR) and –54.12% (raw return) and –53.98% (MAAR) in the third year. Similarly, for the year 2008, the average return on the first trading day was 46.33% (raw return) and 42.93% (MAAR) and –76.50% (raw return) and –76.46% (MAAR) in the third year.

Graph 1.

Source: The authors.

Graph 2.

Source: The authors.

Graphs 1 and 2 present the graphical presentation of the long-term performance of firms from their first day to their third year. The graphs exhibited that firms that did conservative earnings management had either positive or negative returns from the first day of trading till the third year. In some years, it remained negative or gradually improved in the third year, which shows that these firms were not trying to falsely signal their quality of firm and investors can also interpret their long-term performance. However, the firms that did aggressive earnings management had positive initial returns on the first trading day but had negative returns by the end of the third year.

Conclusion

This study examined the association between pre-IPO accruals management and short and long-term returns of 165 IPO firms listed on NSE and BSE of India during the period 2007–2015. Results indicate that firms that were involved in accruals earnings management practices prior to an IPO were likely to have a high level of underpricing. The study also observed a negative relationship between the return in the third year after the IPO and pre-IPO accruals. This suggests that firms that were involved in earnings management practices saw a low stock performance in the post-IPO period. Furthermore, it is also evidenced that firms that have used abnormal accruals more conservatively while reporting earnings have better returns in the third year after the offering compared to the firms that used abnormal accruals aggressively. This observation was similar to studies conducted in China (Aharony et al., 2010; Shen et al., 2014) and India (Mangala & Dhanda, 2019; Purayil & Jijo Lukose, 2019).

The findings of this study have useful implications for different stakeholders who are related to the firm during an IPO process. For instance, results will help the investors to make their investment decisions by evaluating the risk involved with firms who are expecting to raise an IPO. Results also suggest that regulatory authorities at Indian stock exchange should improve their institutional and regulator setup and encourage firms for voluntary disclosure and ask them to provide more credible financial reports so that information asymmetry between the issuer and investors could be reduced.

The major limitation of this study is that it used only discretionary accruals as a proxy for measuring earnings management practices; however, future work should also examine the effect of real earnings management on the level of underpricing, which is still an unanswered question.11

Footnotes

Declaration of Conflicting Interests

Funding

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

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