evidence from Chinese public offerings

China Journal of Accounting Studies

ISSN: 2169-7213 (Print) 2169-7221 (Online) Journal homepage: http://www.tandfonline.com/loi/rcja20

Long-run performance of SEOs regulated by profitability thresholds: evidence from Chinese public offerings Wei Du, Xin Chen & Tianxi Zhang To cite this article: Wei Du, Xin Chen & Tianxi Zhang (2017): Long-run performance of SEOs regulated by profitability thresholds: evidence from Chinese public offerings, China Journal of Accounting Studies, DOI: 10.1080/21697213.2016.1252089 To link to this article: http://dx.doi.org/10.1080/21697213.2016.1252089

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Date: 13 January 2017, At: 04:39

China Journal of Accounting Studies, 2017 http://dx.doi.org/10.1080/21697213.2016.1252089

Long-run performance of SEOs regulated by profitability thresholds: evidence from Chinese public offerings* Wei Du, Xin Chen and Tianxi Zhang Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai, China

ABSTRACT

The Chinese government strictly regulated seasoned equity offerings (SEOs) by setting profitability thresholds and other administrative requirements. We examine the long-run performance of Chinese listed firms making public offerings from 1998 to 2010. Inconsistent with commonly observed patterns of underperformance in mature markets, we do not find any material evidence of post-SEO underperformance compared with matching benchmarks. In addition, we show that the post-SEO abnormal return has a strong positive association with the prior roe of SEO firms, implying that roe-thresholds based on return on equity can be sound tools to identify SEO firms of high quality.

KEYWORDS

SEO; underperformance; public offerings; Chinese listed firms

1. Introduction The long-run underperformance of firms that raise capital through seasoned equity offerings (SEOs) is an important anomaly of financial markets. Loughran and Ritter (1995) find that the average annual stock return of issuing firms is approximately 8% less than that of non- issuing firms over a five-year period after offerings, which is known as the new issues puzzle. This finding has been confirmed by several later studies (e.g., Gokkaya & Highfield, 2014; Jegadeesh, 2000; Jung, Kim, & Stulz, 1996; Spiess & Affleck-Graves, 1995; Teoh, Welch, & Wong, 1998), especially among small, high-growth firms and in heavy issuance years (e.g., Fama, 1998; Loughran & Ritter, 1995). The underperformance of stock returns in the period after SEOs has been recorded not only in the United States, but also in the UK (Slovin, Sushka, & Lai, 2000), Japan (Cai & Loughran, 1998; Kang, Kim, & Stulz, 1999), France (Jeanneret, 2005), Germany (Stehle, Ehrhardt, & Przyborowsky, 2000) and Australia (Allen & Soucik, 2008). However, the literature is mostly based on mature markets. The long-run performance of SEOs in the Chinese market has generally been overlooked, arguably due to China’s transitional environment. Already the largest emerging market and the second largest economy worldwide, China has gradually been opening its capital market to global investors since 2002 through mechanisms like the qualified foreign institutional investor (QFII) and the RMB QFII. By the end of August 2014, a total quota of nearly US$60 billion had been granted to 254 QFIIs. On CONTACT Xin Chen [email protected] *Paper accepted by Tong Yu. © 2017 Accounting Society of China

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November 16, 2014, the Chinese government approved a programme to link the Hong Kong and Shanghai stock exchanges, to further open up its stock markets to overseas investors. This programme allows cross-market stock investment by mainland and Hong Kong investors. Therefore, understanding SEOs of Chinese listed firms is becoming increasingly important to the international investment community and academia. In this paper, we shed light on the mechanisms underlying Chinese SEOs by examining the long-run performance of firms making public offerings. China has distinct institutional features that may affect SEO decisions and the post-offering performance of listed firms. It has a weak legal and institutional environment, resulting in lower-quality investor protection, corporate governance, accounting standards and government administration than most countries examined in the literature (Allen, Qian, & Qian, 2005). In this environment, both the agency problem between managers and shareholders (Jensen & Meckling, 1976) and the agency problem between controlling and minority shareholders (La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 2000) tend to be more severe. Existing studies already find that both types of agency conflicts are relevant to SEO decisions. For example, Jung et al. (1996) argue that managerial discretion explains corporate equity issuance behaviour. If a Chinese listed firm, typically state-controlled, has entrenched management, its SEO decisions are more likely to be driven by managerial discretion and the post-SEO performance of the firm may be negative. Issuing firms may inflate their value by managing earnings upward, a trick the market fails to identify (Rangan, 1998; Teoh et al., 1998). Such pre-SEO earnings management often leads to a later gradual correction of firm performance, driving post-SEO underperformance of stock returns. In the US, some studies (e.g., Pastor-LLorca & Poveda, 2005; Zhou & Elder, 2004) find evidence of earnings management around offerings and show that investors seem to fail to recognise this earnings management before SEOs. Unlike listed firms in the United States, Chinese listed firms typically have one ultimate controller holding an unchallengeable percentage of shares. The existence of controlling shareholders may exaggerate earnings management before SEOs at the expense of minority shareholders. For example, Chinese listed firms are often found to manipulate earnings (Yu, Du, & Sun, 2006) or to prop up the performance of listed firms using related-party transactions (Chen, Lee, & Li, 2003) to meet the profitability requirement for issuing SEOs. These findings confirm the lack of protection for minority shareholders in China. In this situation, the long-run performance of SEOs may be worse due to the natural reversion of manipulated earnings. In contrast to the practices in mature markets, the Chinese government regulates its equity issuance market using a merit-based system. In the early 1990s, the China Securities Regulatory Commission (CSRC) started a quota system for allocating new shares issued to ministries and local governments. Although the quota system was abolished in 2001, the CSRC still maintains a review system for equity issuance. As the price of initial public offerings (IPOs) and the total volume of capital raised are often administered by the CSRC (Chan, Wang, & Wei, 2004), desires for SEOs often arise shortly after firms are listed. Strict rules have been established to guide firms’ eligibility for seasoned equity issues. For example, in 1994 the CSRC required a minimal three-year average return on equity (roe) of 10% for firms to be eligible for rights issues. Even if all requirements are satisfied, an approval from the CSRC is not guaranteed. Such constraints in equity issuance cause the capital structure of Chinese listed firms to rely heavily on their own profitability (Chang, Chen, & Liao, 2014). To avoid the abuse of SEOs by listed firms, the CSRC regulated the equity issuance market to select firms


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of good quality, for example by imposing accounting thresholds to applicants, so that minority investors were better protected. If the CSRC was indeed able to disqualify bad firms in the process of examination and approval, then we might expect SEO firms to perform better than their peers in the long run. Firms also issue equity to finance their future growth (Myers, 1977). In a sample of 38 countries, Kim and Weisbach (2008) show that firms raise capital through public equity offers for investment in R&D and capital expenditure. In the context of the Chinese market, administrative constraints often prevent firms from getting adequate capital during the IPO process. Thus, Chinese listed firms are more likely to issue SEOs to finance their future investments. However, Bo, Huang, and Wang (2011) only provide weak evidence supporting the theory of financing for investment and growth. If by conducting SEOs, Chinese listed firms are able to finance their growth opportunities better than their non-SEO competitors, we expect the long-run abnormal return of SEO firms to be positive. Market timing theory is one of the most prevalent explanations for post-SEO underperformance. Loughran and Ritter (1995) postulate that managers know more than the market about the true value of a firm, so firms conduct SEOs when the market grossly overvalues their stocks. Consistent with this theory, Baker and Wurgler (2002) document that the market timing of an equity offer is an important determinant of firms’ observed capital structure. Henderson, Jegadeesh, and Weisbach (2006) show that market timing is also an important consideration in the world markets for seasoned equity offerings. If overvaluation is a major motivation for firms to issue SEOs, then the long-run underperformance of SEO firms is merely a consequence of the gradual adjustment of formerly overoptimistic expectations. Chinese stock markets are not efficient, as their prices and investor behaviour do not reflect the fundamental value of their listed firms (Allen et al., 2005). According to Morck, Yeung, and Yu (2000), Chinese stock markets have the second highest price synchronicity in a sample of 40 countries, including both emerging markets and mature markets. The firm-specific information compounded in stock prices is therefore insufficient. Chinese listed firms have generally enjoyed higher valuation since the establishment of the stock markets at the beginning of the 1990s. High market valuation is thought to be one of the reasons why Chinese listed firms seem to prefer equity financing to other ways of financing (Zou & Xiao, 2006). Given the general inefficiency of stock prices and high market valuation, managers of Chinese listed firms are more likely to find severe overvaluation opportunities to issue SEOs. Consistent with evidence find in the U.S. and other mature markets, Bo et al. (2011) find strong evidence that Chinese listed firms take advantage of market overvaluation in their SEO decisions. If this overvaluation before SEOs does reverse gradually over time, the long-run performance of SEO firms will tend to be more negative than mature markets. As stated above, the Chinese institutional features, in particular strong government intervention, may profoundly influence the mechanisms of SEO market. Some features can lead to worse long-run performance of SEO firms, while some can drive SEO firms to perform better in the long-term. To shed light on the debate on the costs and benefits of government intervention in SEO market, we examine the long-run performance of Chinese listed firms making public offerings from 1998 to 2010. We use the traditional method of matching firms’ ex-ante characteristics (e.g., size and industry). We therefore compare the ex-post return of SEO firms to non-SEO firms with similar ex-ante characteristics. In general, we do not find any material evidence of post-SEO

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underperformance. Instead, we find that to some extent SEO firms perform better than their firms’ characteristics’ matching firms. We also show that the performance of value-weighted portfolios of SEO firms is higher than that of equal-weighted portfolios, suggesting stronger performance by large issuers. In addition, we find that post-offering abnormal returns of SEO firms have a strong positive association with the prior roe, suggesting that roe thresholds can be sound tools to identify good SEO firms. Our regression analysis does not reveal evidence that the post-offering performance of SEO firms is positively associated with the indicators of growth opportunities. Besides, we also find that the extent of pre-SEO earnings management has significantly negative influence on the post-offering performance, implying that the market is fooled by the accounting tricks of SEO firms. As a robust test, we firstly employ an alternative matching method (market value and book-to-market ratio) as suggested by Barber and Lyon (1997). Similar to our previous results, we do not find the evidence of post-offering underperformance. The SEO firms on average outperform their matching firms by about 5% in the subsequent three-year period, although the difference is not statistically significant. Secondly, we estimate the betas of both SEO firms and matching firms using Fama-French three-factor model, to check whether SEO firms have higher market risk than matched firms. We do not find evidence implying our results are driven by difference in market risk. Thirdly, we control the market effect to check whether the result is sensitive to the benchmark, and we find that most of the result remain the same, the finds of this paper are robust. The remainder of the paper is organised as follows. Section 2 describes the institutional background of SEOs in the Chinese stock market, Section 3 explains the data and methodology, Section 4 presents our empirical results, Section 5 provides robustness tests and Section 6 concludes the study.

2. The practice of issuing shares in China Chinese domestic stocks markets were established in the early 1990s in Shanghai and Shenzhen. From the beginning, the major supervisory government agency of the stock markets, the CSRC, has used a merit-based system to regulate the share issuance activities of domestic firms in the markets. As the result, the supplement of new shares is tightly controlled by the government, whose approval is needed for any issuance of new shares. Such heavy intervention is rarely observed in mature market economies. In Chinese stock markets, rights issues (peigu) and SEOs (zengfa) are the only ways to obtain additional equity financing. In rights issues, existing shareholders are granted the priority to subscribe for new shares. In China, SEOs can be divide into public offerings (gongkai zengfa) and private offerings (dingxiang zengfa). In public offerings, new shares are directly issued to both existing and new public investors, whereas in private offerings, new shares are issued only to certain groups, such as major shareholders and institutional investors.

2.1. Regulation of IPOs Between 1993 and 2000, the CRSC used a quota system to control the size of the IPO market (Chen & Yuan, 2004), under which a quota was allocated to local governments and the ministries in charge of industries (Bo et al., 2011). These governments mainly selected candidate state-owned enterprises (SOEs) to be listed from their jurisdictions, so that underperforming


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SOEs could receive fresh external financing (Bo et al., 2011). The selected SOEs usually only listed their profitable (at least in accounting) business units and left unprofitable units in the parent firms of the newly listed firms. The CRSC not only limited the number of shares issued in IPOs, but also directly intervened in the determination of the IPO offering prices (Bo et al., 2011), for example, by setting a ceiling for the price earnings ratio. Between 1996 and 1999, the price earnings ratio in IPO price was set in the range of 12–14 (Bo et al., 2011). Although non-quota systems have been used since 2001, the CRSC still controls the pricing of IPOs. For instance, the maximum price earnings ratio was set to 20 between 2001 and 2004. Under this regulatory system, severe under pricing of IPOs occurred (Chan et al., 2004) and the amount of funds raised during an IPO was normally quite limited, incentivising newly listed firms to raise further capital through SEOs.

2.2. Accounting-based regulation of SEOs In the early 1990s, the CSRC allowed Chinese listed firms to issue additional shares through rights offerings (Abidin, Reddy, & Chen, 2012). To prevent listed firms from abusing new share issuance, the CSRC restricted rights issues with a series of regulations after November 1993 (Chen & Yuan, 2004). The regulations required listed firms to show continuous profits and/or a roe greater than or equal to the prevailing bank deposit rates (Chen & Wang, 2007). The initial regulation in 1993 only required listed firms to be profitable in the previous two years. In 1993, the CSRC tightened the accounting requirements to three years’ continuous profits and a threeyear average roe greater than or equal to 10%. The requirements were further tightened in 1996 to roe greater than or equal to 10% in each of the previous three years (Chen & Wang, 2007; Chen & Yuan, 2004). In 1999, to be approved for rights issues, listed firms had to show a threeyear average roe greater than or equal to 10% and roe greater than or equal to 6% in each of previous three years. These regulations created clear patterns of earnings manipulation around the required accounting measures (Chen & Wang, 2007) and received intense public criticism. According to Chinese regulations, the total amount of shares issued through a rights offering cannot exceed 30% of the prior outstanding shares of the listed firm. Rights offerings alone cannot satisfy Chinese listed firms’ need for additional share issuance. After two years of pilot work on conducting secondary issuances,1 the CSRC formally allowed listed firms to use public offerings to raise additional capital in April 2000.2 A summary of the regulations of public offerings is shown in Table 1. As the 2000 regulation on public offerings did not impose strict profitability thresholds (other than the three years’ continuous profits requirement) for initiating a secondary issuance, many firms rushed to announce SEO proposals in the next two years. The market soon faced problems such as the abuse of SEO proposals and high offering prices, so the CRSC again felt the need to tighten regulations (Chen & Wang, 2007). In the 2001 regulation,3 firms proposing public offerings had to satisfy more requirements: a clean audit report and a

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Chinese listed companies began to conduct secondary issuances from June 1998, when five listed textile firms, Shanghai Dragon Co., Shenda Co., Taichi Industrial, Shanghai SanMao and Shen Huizhong, raised funds through public offerings. The new policy for allowing secondary issuances was aimed to support the textile industry, to “solve the financial problem of state-owned enterprises in three years.” 2 The CRSC formally promulgated the "provisional regulations on listed companies offering shares to the public” on April 20, 2000. 3 On March 28, 2001, the CRSC enacted “The Regulation of listed companies issuing new shares.”

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Table 1. China’s regulation of public offerings. Date Apr. 20, 2000 Mar. 28, 2001 Jun. 24, 2002 May 8, 2006

Main requirements Companies with continuous profits in the previous three years can apply to the CRSC to conduct additional offerings Clean audit report and three-year average roe ≥6% (not definite) Three-year average roe ≥10% and roe ≥10% in the previous year Three-year average roe ≥ 6%. The limitation on fund-raising was cancelled

three-year average roe greater than or equal to 6%.4 These requirements for issuing new shares were again tightened up in the 2002 regulation,5 which specified that secondary issuers had to maintain a three-year average roe greater than or equal to 10%, a roe greater than or equal to 10% in the previous year and a higher asset-liability ratio than the average level in the same industry, among other requirements. The reform of non-tradable shares6 beginning from 2005 allowed non-tradable shares held by controlling holders and other entities to gradually become tradable in the markets. In exchange, non-tradable shareholders were required to negotiate with tradable shareholders a certain payment in cash or stocks as compensation for obtaining tradability. This reform dramatically reshaped the incentives for controlling shareholders with regard to stock prices. Soon after the reform, the CSRC relaxed the restrictions on public offerings. The 2006 regulation7 required a three-year average roe greater than or equal to 6% and cancelled the fund-raising limitation. The 2006 regulation also introduced the concept of non-public shares and allowed listed firms to issue new shares through private offerings targeted at no more than ten investors. The rigid thresholds for profit or roe were removed for private offerings. Instead, the CSRC restricted the offering price to no less than 90% of the average price over the 20 trading days before the price benchmark and placed lock-up periods for investors participating in private offerings. Since this regulation was announced, private offerings have been widely recognized for their loose regulatory requirements and have become more popular than public offerings. Given the institutional environment in China, firms conducting SEOs may have quite different characteristics from those in mature market economies. Investigating the long-run post-offering performance of SEO firms in China contributes to understanding the mechanisms of SEOs under government intervention.

3. Data and methodology 3.1. Data To be comparable with previous findings in the SEO literature, we focus our analysis only on the long-run performance of public offerings. Thus, we exclude both rights issues and private offerings from our sample. We obtain our data of public offerings and the financial items of 4

The roe requirement was not definite. Companies not meeting the threshold could still qualify if the management and underwriter provided a detailed explanation showing the healthy condition of the company. 5 The CRSC issued “the notice on further regulating listed companies issuing new shares” on June 24, 2002. 6 Before 2005, a Chinese listed firm typically had as much as 60% non-tradable shares, which were mostly held by the state or state-related legal persons. Non-tradable shares could not be traded in the open market, but could be transferred through auction or private negotiation, causing deeply discounted transfer prices compared with market prices. This dual-class share structure did not incentivize controlling shareholders to maximize stock prices, but rather created a tendency for them to expropriate minority shareholders. 7 On May 8, 2006, the CRSC issued “The Regulations of issuing securities by listed companies.”


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Table 2. Industry distributions of public offerings from 1998 to 2010. Industry code A B C D E F G H J K L M Total

Industry description Agriculture Mining Manufacture Utilities Construction Transportation IT Wholesale and retail trade Real estate Service Media Others

No. of issues 1 3 114 10 4 6 10 6 21 4 1 2 182

As % of total issues 0.55 1.65 62.64 5.49 2.20 3.30 5.49 3.30 11.54 2.20 0.55 1.10 100.00

Note: The industry classification is based on the CSRC industry classification code.

listed firms in A-share markets from the China Stock Market & Accounting Research (CSMAR) Database. We start with 216 SEO observations of public offerings between 1998 and 2010. The sample period covers almost the majority of seasoned public offerings in the Chinese market, enabling us to draw a complete picture. We impose the following inclusion criteria on the observations: (1) All of the accounting data for the previous year are available. (2) If a firm offers multiple issues during a 36-month period, only the first issue is included, as overlapping returns can lead to mis-specified test statistics (Lyon, Barber, & Tsai, 1999). (3) SEO firms are not included in the financial industry or growth enterprise market. After imposing these criteria, 182 observations of seasoned public offerings by 173 firms remain, among which 165 firms make only one equity issue during the sample period, seven firms make two issues and one firm makes three issues. Table 2 reports the industry distributions of the public offerings during the sample period. Issues in the manufacturing industry account for 62.64% of the sample. Issues by firms from the agriculture and media industries have the lowest frequencies, with only one issue in each industry during our sample period. In Table 3 we summarise the characteristics of the public offerings. The aggregate gross proceeds in our sample period is 244.37 billion Yuan and the average proceeds per issuance is 1.34 billion Yuan. There is material variation in the number of issues and the proceeds each year. In particular, 2001, 2002, 2007 and 2008 have excessive numbers of issues (104), which account for 57.14% of the total number of issues, whereas 1999, 2005 and 2006 have relatively few offerings (13), which only account for 7.14% of the total number of issues. Chinese listed firms conducting SEOs in 2005, 2007 and 2008 raise approximately half (49.58%) of the total gross proceeds, whereas the gross proceeds in 1998, 1999 and 2006 from SEOs only account for 5.45% of the whole sample.

3.2. Matching methods A common method in examining long-run event abnormal returns is to compare ex-post returns of event firms with those of non-event firms matched by ex-ante firm characteristics.

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Table 3. Summary of public offerings in China (1998–2010). Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Sum

No. 7 3 13 21 27 15 9 5 5 22 34 13 8 182

SHSE9 5 2 6 11 9 9 8 5 4 12 18 7 4 100

SZSE10 2 1 7 10 18 6 1 0 1 10 16 6 4 82

Gross proceeds (bn. Yuan) 5.12 3.97 12.03 18.48 15.99 8.45 14.23 27.88 4.23 35.64 57.64 16.27 24.45 244.37

Average proceeds (bn. Yuan) 0.73 1.32 0.93 0.88 0.59 0.56 1.58 5.58 0.85 1.62 1.70 1.25 3.06 1.34

2006 5 5

2009 13 13

Note: The issue year is the year of the announcement. 9 SHSE: Shanghai Security Exchange 10 SZSE: Shenzhen Security Exchange

Table 4. Distribution of firm characteristics matching. SEO MF

1998 7 7

1999 3 3

2000 13 13

2001 21 20

2002 27 27

2003 15 15

2004 9 9

2005 5 5

2007 22 21

2008 34 34

2010 8 8

Total 182 180

Note: MF = matching firms.

Following the matching method in Spiess and Affleck-Graves (1995) and Allen and Soucik (2008), we use two-dimensional matching with industry and firm size. Our SEO sample includes 182 public offerings. We identify a non-SEO matching firm for each SEO firm that: (1) is in the same industry (by CSRC classification) of the SEO firm; (2) has the closest total book assets to the SEO firm’s assets in year t − 1, within the range of −95% to 105% of the SEO firm’s total assets; (3) has all of its accounting data available in year t − 1; (4) does not conduct an equity offering from year t − 3 to t + 2. These criteria result in matched firms for 180 observations. Two observations are dropped due to a lack of suitable matching firms. The detailed matching results are reported in Table 4. The literature argues that the traditional matching methods are unable to balance ex-ante variables well (Li & Zhao, 2006), so the evidence of underperformance may be attributable to variables not considered within the traditional matching rules, rather than the effect of the SEO activity itself. Nevertheless, we do not attempt to examine the effects of SEO activity itself on the long-run performance of SEO firms. Instead, we are interested in whether and why SEO firms can earn abnormal returns in the long run compared with non-SEO firms of similar characteristics. For this purpose, the traditional matching method generates economically meaningful results.


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Table 5. Definition of variables. Variables BHARi,t ABHARi,t Sizei,t Roei,t DAi,t Agei,t Growthi,t Investi,t Leveragei,t BMi,t

Definition The buy-and-hold return of firm i (either an SEO firm or a matching firm) in year t The BHAR of an SEO firms minus that of its matching firm in year t The natural logarithm of the total assets of firm i in year t The difference between firm i’s roe and the average roe of all firms in the same industry, excluding firm i, in year t DA (Discretionary Accruals) is calculated according to Dechow, Sloan, and Sweeney (1995), Jones (1991) The number of years from the time a firm listed The difference between firm i’s annual growth rate of sales and the average growth rate for all firms in the same industry, excluding firm i, in year t The change in fixed assets scaled by the total assets of firm i in year t minus the average of the measure for all firms in the same industry, excluding firm i, in year t The difference between firm i’s leverage and the average leverage of all firms in the same industry, excluding firm i, in year t The difference between firm i’s book-to-market ratio and the average book-to-market ratio of all firms in the same industry, excluding firm i, in year t

3.3. Measurement of abnormal returns and variables definition To facilitate comparisons with other studies, we examine the long-run performance of SEOs in a three-year (750 trading days) window after SEOs. We calculate the buy-and-hold return (BHAR) for both SEOs and matching firms. We measure the abnormal buy-and-hold return (ABHAR) of each SEO firm by subtracting the BHAR of each matching firms from that of each SEO firm. Following the literature, such as Eckbo, Masulis, and Norli (2000) and Mathew (2002), we take the offering day of the SEO as the beginning of the measurement period. For the main analysis in this paper, we calculate the equal-weighted return for the portfolios of both SEO firms and their matched firms. If an SEO firm is delisted within three years (750 trading days), the BHAR of that firm and its matched firm are truncated on the same day. As some studies report that long-run underperformance tends to disappear once value-weighted (e.g., Fama, 1998; Mitchell & Stafford, 2000), we also report the value-weighted BHAR (by aggregate market value) of SEO firms and their matching firms. In Table 5, we present the detailed definitions of these variables employed in this paper.

4. Post-offering performance of SEO firms 4.1. Descriptive statistics Table 6 provides the results of paired-samples t-tests of firm characteristics for SEO firms and their non-SEO matching firms during the period 1998–2010. From the mean difference tests in the right column, we can see that in our sample, SEO firms have similar total assets, firm ages, growth rates of sales, investment increase rates and financial leverage as their matching non-SEO firms, whereas SEO firms tend to have significantly higher roe, higher extent of earnings management and lower book-to-market ratios.

4.2. Long-run stock performance after SEOs Table 7 shows the summary statistics of the post-offering stock performance of SEO and matching firms.

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Table 6. Comparing firm characteristics of SEO with those of matching firms. Variables

Mean of SEO 21.70 5.23 0.08 0.01 −0.60 0.00 −0.14 0.01

Size Age roe DA Growth Invest Leverage BM

Mean of matching firms 21.67 5.65 −0.06 −0.00 −0.73 −0.01 −0.13 0.06

T-test (SEO-matching firms) 0.217 −1.060 4.732*** 1.203 0.256 1.418 −0.083 −2.422***

Notes: *** Significant at the 1% level, ** significant at the 5% level, * significant at the 10% level (two tailed test), t statistics in brackets; All variables except size are industry-adjusted numbers.

Table 7. Summary statistics of the post-offering returns of SEO and matching firms. SEO firms EW BHAR250 EW BHAR500 EW BHAR750 VW BHAR250 VW BHAR500 VW BHAR750

Mean −0.14 −0.08 −0.04 −0.04 −0.09 −0.10

Median −0.14 −0.15 −0.21 −0.07 −0.10 −0.11

Matching firms Mean −0.21 −0.18 −0.14 −0.10 −0.25 −0.30

Median −0.21 −0.21 −0.20 −0.11 −0.24 −0.31

ABHAR

T-test

0.06 0.11 0.10 0.07 0.16 0.20

(SEO-matching firms) 1.121 1.780* 1.680* 1.930* 2.385*** 2.323***

Notes: *** Significant at the 1% level, ** significant at the 5% level, * significant at the 10% level, two-tailed; EW represent Equal-Weighted portfolio, VW represent Value-Weighted portfolios.

We calculate the ABHAR of the post-SEO stock performance by subtracting the BHAR of the portfolio of matching firms from the BHAR of the portfolio of SEO firms. Using equal-weighted portfolio, SEO firms have a higher mean BHAR than non-SEO matching firms for all three periods. The ABHAR for 250 trading days, 500 trading days and 750 trading days are respectively 6.4, 10.6 and 10.2%. The ABHARs are statistically significant for 500 trading days and 750 trading days. Using value-weighted portfolio, SEO firms also have a higher mean BHAR than non-SEO matching firms for all three periods. The ABHARs for 250 trading days, 500 trading days and 750 trading days are respectively 6.7, 15.7 and 19.9% and are statistically significant for all three periods. Such findings are in sharp comparison with some studies reporting that longrun underperformance tends to disappear once value-weighted (e.g. Mitchell & Stafford, 2000). The ABHARs of the value-weighted portfolios of SEO firms tend to be greater than those of the equal-weighted portfolios, suggesting that large issuers perform better than small issuers. The results are somewhat consistent with the existing evidence from mature markets, e.g., Loughran and Ritter (1995), in that we show that smaller issuers tend to perform worse than larger issuers. Overall, our results indicate no signs that Chinese listed firms making public offerings tend to underperform in the long-run. Rather, they are likely to outperform industry peers with similar size. These findings are inconsistent from that reported in numerous previous studies with similar matching methods, e.g. Spiess and Affleck-Graves (1995), Allen and Soucik (2008), which show evidence of poor post-offering performance by SEO firms. We also plotted the returns of equal-weighted portfolios in Figure 1 and the returns of value-weighted portfolios in Figure 2.


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Return 0.20 0.15 0.10 0.05 0.00 -0.05

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Figure 1. Post-SEO stock performance for the equal-weighted portfolios of SEO and matching firms. Return 0.30 0.20 0.10 0.00 1

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Figure 2. Post-SEO stock performance for the value-weighted portfolios of SEO and matching firms.

The two figures show that the trend of outperformance of SEO firms is relatively persistent within the first two years after offerings, whereas it flattens within the third year. Therefore, we find no sign of underperformance for Chinese listed firms making public offerings.

4.3. Regression of abnormal returns on firm characteristics The dramatic difference in the post-SEO performance is striking between Chinese listed firms and firms in mature markets. The better performance of Chinese SEO firms in our study could be explained by several arguments. First, Chinese SEO firms may be able to better explore their investment opportunities if they do succeed in issuing new shares. In comparison, non-SEO firms may not satisfy the stringent requirements of the CSRC and face difficulty in raising funds from other channels,

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leading to underinvestment in the following years. In such cases, we expect SEO firms with better investment opportunity to have higher post-offering abnormal returns. Alternatively, the intervention of the CSRC helps to select SEO firms of higher quality. In China, the CRSC directly intervenes in SEOs by imposing accounting thresholds (mainly roe) and other non-financial requirements in hope of preventing firms from abusing SEO. Based on the logic, we expect the pre-SEO profitability is positively associated with long-term abnormal return of SEO firms. Lastly, the high profitability requirement to issue public SEOs may induce Chinese listed firms to engage into earnings management and inflate their roe so that the accounting criteria set by the CSRC are met. Such behaviors can increase these firms’ operational risk and investors can request for higher return if they are able to detect these behaviors at the time of public offerings. To test these possible explanations, we run regressions of post-SEO ABHAR on firm characteristics of the SEO firms. Table 8 reports the regression results of the following model:

ABHARi,t = 𝛼 + 𝛽1 Sizei,t−1 + 𝛽2 Roei,t−1 + 𝛽3 DAi,t−1 + 𝛽4 Roei,t−1 ∗ DAi,t−1 + 𝛽5 Agei,t−1 + 𝛽6 Growthi,t−1 + 𝛽7 Investi,t−1 + 𝛽8 Levi,t−1 + 𝛽9 BMi,t−1 + Controls + 𝜀i,t

(1)

We find that roe has a significantly positive coefficient in all three models and its coefficients increase as the period for measuring ABHAR extends until two years after offering. The findings seem to support the CSRC’s decisions of using roe thresholds as tools to identify good SEO firms, as issuing firms with higher prior roe tend to have better performance afterwards. Table 8. Regressions of ABHAR using control firms matched by industry and size. Intercept Sizet−1 Roet−1 DAt−1 Roet−1*DAt−1 Aget−1 Growtht−1 Investt−1 Leveraget−1 BMt−1 Controls Adj. R2 F-statistic Obs

ABHARt+1 −0.156 −0.017 (−0.23) (−0.02) 0.005 −0.004 (0.16) (−0.11) *** 0.556 0.456*** (3.04) (2.20) −1.021*** −0.765* (−2.60) (−1.94) −2.452 (−0.95) 0.012 0.013 (1.32) (1.29) 0.004 0.186** (1.38) (2.02) −0.231 −0.234 (−0.70) (−0.72) −0.028 −0.005 (−1.10) (−0.19) 0.162 0.186 (1.13) (1.11) Yes Yes 10.09% 13.49% 3.64*** 2.99*** 180 180

ABHARt+2 −0.219 0.501 (−0.23) (0.47) 0.009 −0.014 (0.20) (−0.28) *** 1.775 1.055*** (2.80) (2.51) −1.540*** −1.540*** (−2.89) (−3.24) 0.125 (0.04) 0.004 0.003 (0.30) (0.25) −0.003 0.167 (−0.76) (1.36) −0.923** −1.146*** (−2.13) (−2.59) −0.020 −0.038 (−0.43) (−0.63) 0.147 0.288 (0.74) (1.04) Yes Yes 17.47% 19.16% 4.60*** 2.78*** 180 180

ABHARt+3 0.640 0.523 (0.59) (0.49) −0.035 −0.032 (−0.68) (−0.62) *** 1.631 1.574*** (2.62) (2.28) −1.380** −1.377*** (−2.03) (−2.25) 2.580 (0.69) 0.017 0.019 (1.23) (1.36) −0.004 0.145 (1.01) (1.02) −0.774 −1.111*** (−1.50) (−2.14) −0.017 −0.023 (−0.36) (−0.53) 0.320 0.242 (1.08) (0.98) Yes Yes 15.41% 17.54% 2.29*** 2.48*** 180 180

Notes: *** Significant at the 1% level, ** significant at the 5% level, * significant at the 10% level, t statistics in brackets, two-tailed; Year and industry dummies are included but not reported here; All variables except size are industry-adjusted numbers. Variable definitions are listed in Table 5.


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DA has a significantly negative coefficient in all three models, meaning that higher earnings management in the pre-SEO year tend to lead to lower long-term performance of SEO firms. Such findings are not consistent with the argument that investors are compensated with higher return for investment of firms manipulating earnings to meet the profitability criteria for SEO. Rather, the results favour the argument that the market is not efficient enough in incorporating positive earnings management into stock prices, so the unexpected earnings reversal in post-offering periods leads SEO firms to have poor abnormal returns. Table 8 shows that roe is significantly positive, while DA is significantly negative within these three models. Considering that the roe could be inflate by SEO firms, so it is necessary to reconcile this competing effect between roe and DA. In Equation (1) we add the crossterm of roe*DA, the result shows in columns 3, 5, 7 of Table 8. Most of the results remain the same, the coefficient of roe*DA is negative in the first model, but positive in the second and the third model, while none of them is statistically significant among these three models, means that the extent of inflating roe by earnings management is under a certain limit, the negative of EM cannot counteract the whole positive effect of roe, so the earning threshold play a more important role in identifying good SEO firm. Table 8 also shows that BM is significantly associated with ABHAR, and Invest is negatively associated only with two-year ABHAR. Therefore, we find no evidence supporting the explanation that SEO firms with better growth opportunities tend to have higher post-offering performance.

5. Robustness results 5.1. Alternative matching method Existing studies find post-SEO abnormal returns and the magnitude of the returns to be sensitive to the benchmarks used. For example, Loughran and Ritter (1995, 2000), Jung et al. (1996) and Jegadeesh (2000) show evidence of significant long-run underperformance; Mitchell and Stafford (2000) and Mathew (2002) report insignificant abnormal returns following issues; Allen and Soucik (2008) show a positive post-offering return when matched with the market index. Barber and Lyon (1997) show that matching by market value and book-to-market ratio yields the best well-specified test statistics. As a robustness test, we also employ this matching method to see if our main results still hold. We identify a non-SEO matching firm for each SEO firm that: (1) has the close market value with the SEO firms’ in year t − 1, within the range of −95% to 105% of the SEO firm’s market value; (2) has the closest book-to-market ratio among (1); (3) has all of its accounting data available in year t − 1; (4) do not conduct an equity offering from year t − 3 to t + 2. These criteria result in matched firms for 182 observations. The pattern of findings using the matching method persists using either equal-weighted portfolio or value-weighted portfolio. Due to the limit of space, we only present the results of equal-weighted portfolios. As reported by Table 9, the ABHARs for all three periods are all insignificant. Thus, our results again suggest that Chinese SEO firms do not tend to underperform in the post-offering

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Table 9. The post-offering returns of SEO and control firms matched by market value and B/M. SEO firms Mean −0.150 −0.083 −0.039

EW BHAR250 EW BHAR500 EW BHAR750

Matching firms Med −0.147 −0.161 −0.229

Mean −0.179 −0.105 −0.093

Med −0.161 −0.186 −0.162

ABHARt

T-test

0.029 0.022 0.054

(SEO-matching firms) 0.524 0.366 0.734

Notes: *** Significant at the 1% level, ** significant at the 5% level, * significant at the 10% level, two tailed test; EW represent Equal-Weighted portfolio.

Return 0.10 0.05 0.00 1

51

101

151

201

251

301

351

401

451

501

551

601

651

701

751 Day

-0.05 -0.10 -0.15 -0.20

SEO Firms

Matching Firms

ABHAR

Figure 3. Post-SEO stock performance for the equal-weighted portfolios of SEO and control firms matched by market value and B/M.

periods. We also plot the returns of equal-weighted portfolios using the alternative matching method in Figure 3. It is pretty clear that there is no sign of underperformance for Chinese listed firms making public offerings even under the new matching method. Table 10 presents regression results on ABHAR calculated based on control firms matched by market value and B/M. Similar to results in Table 8, we find that roe is positively associated with ABHAR in all three models and its coefficients are significant at two-year and three-year periods. The results again support the CSRC’s decisions of using roe thresholds as tools of selecting good SEO firms. DA has a negative, but insignificant coefficient in all three models, not supporting the argument that firms manipulating earnings to meet the SEO criteria are requested for higher return by investors. The coefficient of Growth is negative and significant in the model of two-year period and three-year period, Invest is negatively associated only with two-year ABHAR, and BM is positively associated only with three-year ABHAR. These results again are inconsistent with the explanation that SEO firms with better growth opportunities tend to have higher post-offering performance.

5.2. Risk comparison using Fama-French 3 factor models Although we show that firms making public offerings tend not to underperform compared with control firms matched by various firm characteristics. It is possible that offering firms


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Table 10. Regressions of ABHAR using control firms matched by market value and B/M.

Dep. Var.: Intercept Sizet−1 Roet−1 DAt−1 Aget−1 Growtht−1 Investt−1 Leveraget−1 BMt−1 Controls Adj. R2 F-statistic Obs

ABHARt+1

ABHARt+2

ABHARt+3

Coefficient

Coefficient

Coefficient

(t-stat.) 0.562 (0.77) −0.028 (−0.80) 0.205 (1.42) −0.074 (−0.50) 0.010 (0.98) −0.126 (−1.36) −0.193 (−1.43) 0.021 (0.98) 0.161 (0.75) Yes 9.11% 5.41*** 182

(t-stat.) 0.379 (0.46) −0.014 (−0.35) 0.632*** (2.56) −0.515 (−1.05) −0.004 (−0.30) −0.007* (−1.78) −1.272** (−2.01) 0.015 (0.40) 0.229 (1.18) Yes 9.78% 6.59*** 182

(t-stat.) 0.424 (0.44) −0.020 (−0.42) 0.497* (1.87) −0.153 (−0.20) −0.004 (−0.27) −0.018* (−1.92) 0.261 (0.51) 0.075* (1.76) 0.641** (2.12) Yes 6.92% 4.91*** 182


Table 11. Comparison of beta of SEO and matching firms. SEO firms

Matching firm

Mean

Mean

T-test

Obs.

0.522 0.522

−1.860* −1.830*

180 180

0.498 0.499

0.428 0.030

182 182

Matching method: Industry and size βVW 0.497 βCVW 0.496 Matching method: Market value and B/M βVW 0.497 βCVW 0.497

Notes: *** Significant at the 1% level, ** significant at the 5% level, * significant at the 10% level, two tailed test; the superscript of β identify the aggregate value weighted factors (VW) or the circulated value weighted factors (CVW).

can have higher market risks than their matching firms, so that the abnormal returns estimated above may not be unbiased. To address such concerns, we estimate the betas for both SEO firms and their matching firms using the Fama-French three-factor model8 and calculate the mean of betas for both groups. As presented by Table 11, the difference in Betas between the two groups are generally insignificant, suggesting our results are not driven by higher risks of SEO firms.

8

We use weekly returns to estimate the Fama-French three-factor model. The estimate window is between +1 and +245 trading weeks.

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Table 12. Regressions of ABHAR with the benchmark of market index.

Dep. Var.: Intercept Sizet−1 Roet−1 DAt−1 Aget−1 Growtht−1 Investt−1 Leveraget−1 BMt−1 Controls Adj. R2 F-statistic Obs

ABHARt+1

ABHARt+2

ABHARt+3

Coefficient

Coefficient

Coefficient

(t-stat.) −0.049 (−0.07) −0.002 (−0.06) 0.453** (2.01) −1.026*** (−2.63) 0.013 (1.29) 0.173* (1.90) −0.254 (−0.78) −0.009 (−0.31) 0.185 (1.12) Yes 13.07% 3.22*** 180

(t-stat.) 0.202 (0.20) −0.012 (−0.24) 1.037*** (2.54) −1.593*** (−3.11) 0.008 (0.55) 0.161 (1.23) −1.241*** (−2.74) −0.038 (−0.65) 0.240 (1.11) Yes 16.78% 3.02*** 180

(t-stat.) 0.418 (0.37) −0.026 (−0.50) 1.168*** (2.84) −1.381** (−2.07) 0.024 (1.56) 0.173 (1.23) −0.805 (−1.52) −0.042 (−0.82) 0.305 (1.24) Yes 14.21% 2.01*** 180


5.3. Alternative benchmark In Tables 8 and 10, we measure the long-term abnormal return by using the firms’ characteristic matching method. ABHAR is computed by subtracting the buy-and-hold returns of a matched non-issuing firm from that of an issuing firm. Since the abnormal return maybe sensitive to the benchmark employed, in order to control for the cyclical volatility on Chinese stock market, in this section we use the market index as the benchmark, the ABHARs are computed by subtracting the return on the value-weighted market index from the buy-and-hold return of SEO firms. Table 12 shows the regression results of the model (1). Table 12 shows the regression results on the market-adjusted return, we find that most of the results remain the same. Similar to Tables 8 and 10, roe has a significant positive coefficient in all three models and its coefficient increase as the period extends. As a consequence, the earning thresholds is an effective way for CSRC to identify good SEO firms. The coefficient of DA is significantly negative among all three models, means that the earnings management before SEO has significantly negative effect on the post-SEO performance. The result of the regression remains the same with the alternative market benchmark, the findings of this research can be robust.

6. Conclusion In this paper, we examine the long-run performance of SEO firms in the Chinese context, as China’s unique institutional features may affect the mechanisms of SEOs. In particular, to


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protect minority investors and prevent listed firms from abusing SEOs, the CSRC has historically imposed strict accounting thresholds to applicants. Given that the stringent accounting thresholds represented the Chinese government’s gradual reactions to the problems arising in periods of loose regulation, such as low quality of SEO firms and high offering prices, it is reasonable to assume that these thresholds for profitability can generate long-term implications to firms’ post-offering performance. Using a sample of public offerings from 1998 to 2010, we observe quite different patterns of post-SEO stock performance from those in mature markets. We do not find any material evidence of underperformance in the period of up to three years after the SEO. Our regression analysis shows that post-offering stock performance has a strong positive association with the prior roe of SEO firms, implying that roe thresholds can be sound tools ex-ante to identify good firms. Moreover, our results do not provide support for the argument that firms manipulating earnings to meet the SEO criteria face demands from investors for higher return, and do not provide support for the argument that firms with greater growth opportunities can take advantage of SEO better and have higher post-offering performance. Our findings are robust to the alternative method of using control firms matched by market value and book to market ratio, and the result remains after controlling the market effect. We show that our results are clearly not driven by higher market risk borne by SEO firms. Our study attempts to shed light on the discussion about the costs and benefits of government intervention in SEO market. Moreover, the new evidence provided in this paper contributes to our understanding the mechanisms of SEOs under strong government intervention in transitional economies.

Disclosure statement No potential conflict of interest was reported by the authors.

Funding This work was supported by National Natural Science Foundation of China (NSFC) [71372104, 71502102].

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