Understanding seasoned equity offerings of Chinese firms

Journal of Banking & Finance 35 (2011) 1143–1157

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Understanding seasoned equity offerings of Chinese firms Hong Bo a, Zhongnan Huang a, Changyun Wang b,⇑ a b

Department of Financial & Management Studies, SOAS, University of London, London, UK China Financial Policy Research Center, Renmin University of China, Beijing, China

a r t i c l e

i n f o

Article history: Received 25 August 2009 Accepted 17 September 2010 Available online 12 October 2010 JEL classification: G30 G32 Keywords: Seasoned equity offerings Chinese listed firms Market timing

a b s t r a c t We examine the empirical relevance of standard theories explaining the motivation of Seasoned Equity Offerings (SEOs) in the Chinese context. Analyzing Chinese SEOs during 1994–2008 and controlling for other factors reflecting features of Chinese corporate finance, we find that Chinese SEOs are mostly motivated by timing the market. Financing for investment and growth receives weak empirical support. We do not obtain any consistent evidence supporting both the tradeoff and the agency theories. In addition, we find that the firm’s SEOs behavior varies between rights issues and public offerings and across different periods along with the progress of China’s market transition. Our results show that Chinese listed firms in general behave similarly as their counterparts in other countries concerning SEOs decisions in that they issue SEOs when there are opportunities to take advantage of market overvaluation. These results are consistent with the well-documented convergence trend of corporate SEOs behavior of firms around the world. In addition, our findings challenge the conventional perception on Chinese SEOs that controlling shareholders use SEOs as a means to expropriate minority shareholders. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction Recent research documents the convergence of corporate Seasoned Equity Offerings (SEOs) behavior around the world. For example, Henderson et al. (2006) examine equity financing behavior using worldwide country/region level aggregate data during 1990–2001 and report a trend of increasing international equity issuance. Moreover, these authors document that an important explanation of SEOs behavior in the literature based on mature capital markets (mainly US and UK), i.e., timing the market, is a global phenomenon. Kim and Weisbach (2008) investigate motivations for public equity offerings from 38 countries during 1990–2003. By examining the ultimate usage of capital raised, these authors provide evidence that some standard theories explaining the motivation of SEOs, such as financing for investment, timing the market, are applicable to SEOs across different countries. The evidence provided by this stream of research supports international convergence of corporate equity financing behavior. However, the mainstream research on the convergence of SEOs behavior does not include China (e.g., Henderson et al., 2006; Kim and Weisbach, 2008).1 Although SEOs share a lot of ⇑ Corresponding author. Tel.: +86 10 82509275; fax: +86 10 82509289. E-mail addresses: [email protected] (H. Bo), [email protected] (Z. Huang), [email protected] (C. Wang). 1 Henderson et al. (2006) put China into the category of ‘‘Other Asia” together with other 17 countries/regions and use aggregate data in their empirical analyses. 0378-4266/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2010.09.025

similarities across countries, little is known whether Chinese listed firms behave similarly as their counterparts elsewhere. Our paper aims to fill this gap in the literature. Chinese SEOs are interesting due to the following reasons: Firstly, Chinese financial markets are increasingly exposed to international companies. Since the opening of Chinese A-share markets to Qualified Foreign Institutional Investors (QFIIs) in 2002, the amount of approved investment increased to USD 30 billion by the end of 2008, and the number of involved international institutions was over 100. Among 1525 firms listed on the Chinese A-share markets in 2008, there were 26 firms with foreign investors as major shareholders, 11 firms with foreign investors as controlling shareholders.2 Obviously, China is playing an increasingly important role in global financial markets. Secondly, Chinese SEOs are interesting because Chinese firms are operating in a transition environment in which both capital markets and corporate governance system are still different from those of mature markets. Chinese stock markets and SEOs activities only emerged at the beginning of 1990s. Examining SEOs activities in the Chinese stock market provides us with a useful platform to check how the SEOs

2 Chinese domestic stock markets are segmented in A-share and B-share markets. A-shares are ordinary shares available exclusively to Chinese citizens and institutions before the introduction of QFIIs in 2002. B-shares are denominated in US or HK dollar and designated for overseas investors prior to opening the market to domestic investors in February 2001. The figures are computed from the CSMAR (China Stock Market & Accounting Research) database.

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behavior of Chinese listed firms has been changed over time along with the progress of China’s market transition and whether the differences in SEOs behavior over time, if any, reflect the characteristics of China’s market transition. Although the topic is interesting and important, formal explanatory researches focusing on the SEOs behavior of Chinese firms are scarce, particularly in English academic outlets. Some studies mention Chinese SEOs but with the research focus rather than SEOs. For example, Huang and Song (2006) report that more than 50% of financing of Chinese listed firms comes from external sources, and net equity financing makes up more than 50% of external financing, suggesting that Chinese firms are mainly using equity financing, in particular SEOs, as a channel of raising external capital. Zou and Xiao (2006) argue that Chinese listed firms have built-in incentives for raising equity due to tight regulations on SEOs and agency problems. Among few existing studies on Chinese SEOs, almost all investigate the reasons behind the firms’ hunger for SEOs from an agency theory perspective, explaining the SEOs behavior by the conflict of interests between controlling and minority shareholders. A major conclusion drawn from this stream of research is that SEOs are used by controlling shareholders as a means to tunnel assets from listed firms (e.g., Aharony et al., 2000; Jian and Wong, 2004; Lin et al., 2007). Therefore, the conventional perception based on very few existing studies on Chinese SEOs is that the dominant motivation for Chinese listed firms of issuing seasoned equities is to expropriate minority shareholders by controlling shareholders. Except the agency theory explanation, to our best knowledge, there are hardly any formal studies examining other possible motivations of Chinese SEOs. Given that SEOs have been the most active financing channel for Chinese listed firms, it is important to explore beyond the agency explanation why Chinese listed firms conduct SEOs. We notice that an obvious drawback of the previous studies is that the agency explanation is examined in isolation from other possible motivations of SEOs. In addition, the conclusion that controlling shareholders use SEOs as a means to expropriate minority shareholders lacks direct evidence, i.e., the previous studies fail to provide direct evidence that the financial sources used in tunneling are indeed the capital raised via SEOs. We argue that Chinese listed firms are not exclusively motivated by the expropriation of minority shareholders by controlling shareholders, other theories such as financing for investment and growth and timing the market may be also important. We aim to make a contribution to the existing literature on Chinese SEOs by broadening our understanding on the motivation of Chinese SEOs from agency theory to other possible explanations. We use Chinese SEOs during 1994–2008 to examine the empirical relevance of standard theories explaining the motivation of SEOs. After controlling for other factors that reflect features of Chinese corporate financing, we find that Chinese listed firms are mostly motivated by market timing to issue SEOs. Financing for investment and growth has received weak empirical support. However, tradeoff theory is not supported by our data. Moreover, we do not obtain any consistent evidence supporting the notion that controlling shareholders conduct SEOs to exploit minority shareholders, which has been claimed in the previous literature on Chinese SEOs. We provide evidence that market timing is the most relevant explanation of SEOs behavior in China. In addition, we also find that the behavior of Chinese SEOs varies between rights issues and public offerings and across different periods along with the progress of China’s market transition. Our results show that Chinese listed firms in general behave similarly as their counterparts in other countries concerning SEOs decisions in that they issue SEOs when there are opportunities to take advantage of market overvaluation. Our findings are in support of the welldocumented convergence trend of corporate SEOs behavior of

firms across different countries (e.g., Kim and Weisbach, 2008; Henderson et al., 2006). The remaining of the paper is organised as follows. In Section 2 we present a summary of SEOs practice in China, which contains description on the features of Chinese corporate governance. Section 3 reviews the related literature, including standard theories explaining the motivation of SEOs and the related research on China. We discuss empirical issues in Section 4. Section 5 presents empirical results. Section 6 concludes.

2. The SEOs practice in China China’s two domestic stock markets, i.e., the Shanghai and the Shenzhen stock exchanges, were established in the early 1990s. There were 1525 firms listed in the Chinese A-share markets by the end of 2008, and the total market capitalization reached 12.1 trillion RMB, the third largest stock market in the world (after US and Japan).3 Due to the transition nature of Chinese economy, Chinese stock markets function differently from those of major mature market economies in that the markets are heavily intervened by the administration. In this section, we demonstrate how China substituted administrative means for market mechanisms by focusing on the listing process and underpricing of IPOs since they obviously affect seasoned equity financing decisions of Chinese firms. The initiative of setting up domestic stock markets in China was to provide under-performed State-Owned Enterprises (SOEs) with a fresh channel of external financing, which determines that the listing process has been unavoidably political-connected. During the period of 1993–2000 the quota system was applied to the selection of firms to be listed, under which the Chinese Securities Regulatory Committee (CSRC) allocated the quota4 to ministries and local governments, who selects candidate firms to be listed. The selected firms must undergo a process of restructuring in terms of both the organizational structure and the accounting system, which is often termed as ‘‘financial packaging”. The quota system was mainly applied to former SOEs. The selected SOEs normally put their potentially profitable business units together to form a new firm to be listed and leave unprofitable units with the former SOEs. If the SOE invests more than 50% of assets in the new firm, the newly listed firm remains the status of being state-owned, otherwise it could be a new entity owned by, for example, a legal person. Obviously, this administrative listing process employed very little market principle. The first non-quota IPO appeared only in 2001 in China. This administrative intervention in the listing process in Chinese stock markets has resulted in many problems, including severe earnings management, tunneling from listed firms to parent SOEs, etc. In particular, as a consequence of this administrative listing process, the size of Chinese listed firms is in general very small in terms of market capitalization, which potentially created hunger for further seasoned equity issues. Not only the listing process but also the price of IPOs was administratively intervened in China, which is responsible for strong desire of Chinese firms for SEOs. Before 1995, the CSRC determined all IPOs prices. In most of other times before 2004 (expect for March 1999–June 2001), IPOs prices were administrated by the CSRC by setting a ceiling for the Price Earnings (PE) ratio. For example, during 1996–1999, the price was set to restrict the PE ratio in the range of 12–14. During the period of 2001–2004, the maximum PE ratio was set to be 20. Such a heavily administrative regulation on the PE ratio resulted in serious underpricing of Chinese IPOs, giving rise to strong desire for additional equity 3

The exchange rate was 6.836 RMB/USD by the end of 2008. The number of shares during 1993–1996 and the number of IPO firms during 1996–2000. 4

H. Bo et al. / Journal of Banking & Finance 35 (2011) 1143–1157

financing immediately after IPOs.5 Logically, it is rational for firms to raise a modest amount of funds via IPOs to reduce the loss due to IPO underpricing and wait to raise additional funds through SEOs in subsequent years when the share price is higher. Perhaps the most striking feature of Chinese listed firms was the existence of non-tradable shares before 2005, i.e., the shares held by the state and the state-related legal persons were not allowed to be traded on the secondary market. Non-tradable shares accounted for about two-thirds of total shares outstanding in a typical listed firm before 2005. This ownership split structure by design guaranteed state ownership intact after a firm went public. More importantly, for SEOs activities, the existence of non-tradable shares made the controlling position of largest shareholders unchallengeable. This is perhaps the reason why previous studies on Chinese SEOs argue that the exclusive motivation of Chinese SEOs is that controlling shareholders use SEOs as a means to expropriate outside minority shareholders. In April 2005 China officially initiated the so called ‘‘ownership split reform”. Once a firm completed the reform, its non-tradable shares are allowed to be freely traded on the secondary market. This ownership split reform may change the SEOs behavior of Chinese firms. Undoubtedly, the largest shareholders in Chinese firms are expected to restrain themselves from behaving opportunistically because they are now under closer scrutiny of the market and under pressure of being challenged by other shareholders. 3. Related literature Researches on SEOs based on firms outside China have established some standard theories explaining the motivation of a firm for issuing SEOs. These can be summarized as: financing for investment and growth; the tradeoff theory; market timing; and agency theory. In this section, we review this line of standard literature and discuss how these theories apply to China. 3.1. Financing for investment and growth A fundamental explanation of why the firm issues SEOs is to finance investment and growth. Myers (1977) claims that the firm with growth potential prefers to finance new investment by equity in order to prevent wealth transfer from shareholders to debtholders. In addition, investment under growth is of high uncertainty, suggesting high uncertainty surrounding the firm’s future cash flow, which results in uncertainty about the firm’s financial healthiness in the future. To buffer against possible financial constraints due to debt financing, the firm with greater growth potential tends to use more equity finance. Kim and Weisbach (2008) document that firms spend incremental capital mainly on R&D and capital expenditures, which suggests that firms normally use SEOs to raise additional capital in order to finance investment and growth. Walker and Yost (2008) provide evidence that no matter what the stated usage of capital raised from SEOs is, ex post, firms always use SEOs proceeds to increase capital expenditures and R&D. Harjoto and Garen (2003) find that listed firms with greater growth potential are more likely to conduct SEOs after their IPOs. Although there has been no direct evidence on financing for investment and growth through SEOs in the Chinese stock market, the theory of financing for investment and growth can be directly applied to China given that Chinese listed firms have become increasingly profit-oriented and most of them are in the growth stage. 5 Chan et al. (2004) document that the average underpricing of A-share IPOs in China over the 1993–1998 interval was 178%. Wang (2005) reports that the average initial return of Chinese IPOs over the period from 1994 to 1999 was as high as 272%.

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3.2. The tradeoff theory The tradeoff theory contends that the firm uses equity issues to adjust its capital structure in order to maintain an ‘optimal’ level of leverage that balances the benefit (tax shield) and the cost (financial distress) of debt financing (Modigliani and Miller, 1958, 1963; Myers, 1977). Although the tradeoff theory has been mainly examined in the context of the firm’s capital structure decision, some scholars document that the tradeoff theory also explains motivation of the firm’s SEOs decision. For example, both Marsh (1982) and Hovakimian et al. (2001) document that firms whose leverage ratio is higher than their optimal level are more likely to conduct SEOs to lower their leverage. The tradeoff theory is expected to apply to Chinese listed firms, at least to a certain extent. Chinese listed firms borrow mainly from banks due to the lack of a corporate debt market. Although being still dominated by state ownership, the Chinese banking sector has been undergoing major reforms, which made the Chinese banking sector similar to their counterparts in mature market economies. Banks are increasingly playing an effective monitoring role in borrowing firms, which suggests that costs of borrowing have become a priority concern for the firm. In addition, some evidence suggests that Chinese firms are concerned about tax benefits of debt financing (e.g., Huang and Song, 2006). Therefore, it is likely for Chinese firms to use SEOs as a means to adjust capital structure in order to achieve a balance between costs and benefits of debt financing.

3.3. Market timing The pecking order theory (Myers and Majluf, 1984) states that with the presence of asymmetric information between outside investors and the firm, the order of the firm’s financing choice should be firstly internal funds, risk-free debt, risky debt, and, at a last resort, equity. Because equity financing suffers the most severe information asymmetry problem between outside investors and the firm’s insiders, outside potential investors intend to under-value the firm’s equity. Hence the firm is willing to issue new equity only when it is overvalued by the stock market. Timing the market is therefore a likely explanation for the firm to use equity financing as a means to raise external capital.6 Indeed, Pagano et al. (1998) find that Italian IPOs are mainly motivated by overvaluation and timing the market. Kim and Weisbach (2008) document that the firms with high market to book ratios are more likely to keep more cash from a marginal dollar raised from SEOs than low market valuation firms, suggesting that firms with high market valuation are more likely to conduct SEOs to take advantage of their overvaluation. Henderson et al. (2006) document that market timing considerations appear to be very important in equity issuance decisions not only for domestic (US) firms but also for firms conducting cross-boarder equity financing. Graham and Harvey (2001) find that one important factor driving the firm’s SEOs decision is the pre-issue stock appreciation. In addition, evidence shows that the firm’s stock market returns will be lower after SEOs (Baker and Wurgler, 2002; Henderson et al., 2006), which indirectly supports that one possible motivation for the firm to conduct SEOs is to time the market. Chinese listed firms have been enjoying high market valuation during most of the times in the history of the Chinese stock market. For example, the sample firms used in this paper had enjoyed an

6 Autore and Kovacs (2010) document that the firms suffering with higher information asymmetry are more likely to issue equity as opposed to debt when the amount of asymmetry information is temporarily low.

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average market to book value of as high as 3.66 during 1994–2008. Some scholars claim that overvaluation may explain why Chinese listed firms prefer equity financing to other sources (e.g., Huang and Song, 2006; Zou and Xiao, 2006). 3.4. Agency theory Two types of agency problem are relevant to the firm’s SEOs decision: the agency conflict between managers and shareholders (Jensen and Meckling, 1976; Jensen, 1986), and the conflict of interests between controlling and minority shareholders (La Porta et al., 1999; Claessens et al., 2000; Berkman et al., 2009; Bennedsen and Nielsen, 2010). Agency conflict between managers and shareholders is relevant because it predicts that entrenched managers may be motivated to issue additional equity in order to have more financial resources (such as free cash flow and the like) at hands for their private benefits of control. The conflict of interests between controlling and minority shareholders is also relevant to the firm’s SEOs decision because SEOs are likely to be used by controlling shareholders as a means to expropriate minority shareholders. Researchers investigating corporate equity issue behavior based on firms in mature market economies exclusively emphasize on how agency conflict between managers and shareholders, i.e., managerial discretion, motivates the firm’s equity issue decision, while there is little evidence on the impact of the conflict of interests between majority and minority shareholders. For example, Jung et al. (1996) claim that among the three explanations of the security issue decision, i.e., the pecking-order model, the agency model (managerial discretion), and the market timing theory, the agency model (managerial discretion) explains the firm’s equity issuance behavior. Although managerial discretion has been the focus of discussion in mature markets concerning the firm’s SEOs decision, academic attention has been paid to explaining the motivation of Chinese firms’ SEOs from the perspective of conflict of interests between controlling and minority shareholders. This research stream is a natural response to the unchallengeable controlling position of the largest shareholders in Chinese listed firms due to the existence of non-tradable shares before 2005. For example, Aharony et al. (2000) report that parent SOEs often retrieve assets from listed firms to help their other non-profitable units. Chen et al. (2003) document that it is a common practice in China that parent SOEs first use related-party transactions to help the listed firm improve its financial condition for the sake of meeting the profitability requirement for issuing SEOs, and then they tunnel assets from the listed firms after SEOs. Jian and Wong (2004) find that listed firms have more frequent relatedparty transactions after rights issues. Yu et al. (2006) show that Chinese listed firms heavily engaged earnings management to meet the rights issue threshold during the 1994–2002 period. Lee and Xiao (2006) report that firms increase dividend payouts after rights issues, arguing that non-tradable shareholders use cash dividends to tunnel. 4. Data and methodology 4.1. Data Our empirical analyses cover all the listed non-financial firms that have conducted SEOs in Chinese stock markets during 1994– 2008. SEOs activities in our paper are defined as either rights issues or public offerings. In rights issues (peigu) the existing shareholders are granted with the priority to subscribe new shares, while in public offerings (zengfa) new shares are directly issued to public

investors.7 Our sample period covers almost the entire history of the Chinese stock market, which enables us to test whether the SEOs behavior of Chinese firms had been changed along with the progress of China’s market transition. Hence we are able to draw a relatively complete picture concerning SEOs behavior of Chinese listed firms. The data are taken from Sinofin database administrated by the China Centre for Economic Research (CCER) in Peking University. When preparing for the data we first collect all the SEOs cases during the sample period, we then follow these SEOs cases to identify the firms that issued these SEOs. During 1994–2008 there were in total 1081 SEOs cases and 848 firms were involved in these SEOs activities.8 We treat these 848 firms that have issued SEOs during the sample period as our sample firms whose information on SEOs activities is then matched with the information of the firms’ balance sheets and income statements during the same period. This way the firm’s SEOs activities were matched with the firm’s real and other financial activities, which provides us with a panel data set for these firms during 1994–2008. Although mainly relying on cross-section estimations based on SEOs cases in our empirical analyses, we use the information revealed by the panel data to detect some differences between SEOs firm-years and non-SEOs firm-years of our sample firms. An overall picture of SEOs activities in China during 1994–2008 is displayed in Table 1, which shows that rights issues were more active than public offerings. There were totally 1081 issues of SEOs during the sample period, of which 908 were rights issues, accounting for 84% of total SEO issues, while public offerings accounted for 16%. The total SEOs proceeds amounted to 510 billion RMB in the sample period, of which 266 billion RMB were from rights issues and 245 billion RMB were from public offerings. Table 2 presents the industry distribution of SEOs during our sample period. As we can see that more than half of SEOs were issued by manufacturing firms. Total number of SEOs conducted by manufacturing firms accounted for 54.9% of total SEOs in the sample period. There were 507 listed firms in manufacturing industry that have conducted SEOs, while the average number of listed firms in this industry in the same period was 676, hence 75% of the listed firms in the manufacturing industry have conducted SEOs during the sample period. Although the number of SEOs in wholesale and retail industry only accounted for 11.4% of the total SEOs, 94.1% of the listed firms in this industry had issued SEOs during the sample period. In sum, Table 2 shows that the majority of Chinese listed firms (77.6%) had used SEOs as an external financing channel during 1994–2008. 4.2. Variables and empirical specifications To examine empirical relevance of standard theories explaining the motivation of SEOs in the Chinese context, we need to select some proxies for the relevant theories. Following Kim and Weisbach (2008), we use annual growth rate of sales (Growth) and fixed investment (Investment) to proxy for the theory of financing for investment and growth, where Investment is measured as the changes in fixed assets scaled by total assets of the firm. We use the difference between the firm i’s leverage and the average leverage of other firms in the same industry excluding firm 7 Private placement is another way of raising capital in the Chinese stock market. Private placement (dingxiang zengfa or feigongkai faxing) refers to that shares are issued only to certain groups such as institutional investors and other enterprises. We exclude private placement from our research because private placement in China has been heavily used for ‘‘reverse acquisition”, i.e., it is often used by private firms to go ‘‘public” by means of purchasing shares issued by a listed firm via private placement. Obviously, the motivation of private placement is in most cases different from that of rights issues and public offerings. 8 We have taken some firms out of the sample due to missing observations and extreme values.

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Table 1 A summary of SEOs activities in China, 1994–2008. The sample period is from 1994 to 2008. The issue year is the year of the SEO announcement. SEOs include rights issues and public offerings. Rights issues

Public offerings No. of issues

Total SEOs

No. of issues

Capital raised (bn RMB)

Capital raised (bn RMB)

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

64 64 51 109 155 120 181 84 20 24 21 0 0 7 8

5.8 11.2 10.0 24.8 40.2 28.3 55.2 31.2 5.8 6.2 9.9 0.0 0.0 23.3 13.9

0 0 0 0 7 5 24 13 30 14 12 3 7 31 27

0 0 0 0 3.0 5.5 22.6 10.4 16.8 9.2 16.1 26.4 13.0 70.3 51.2

No. of issues 64 64 51 109 162 125 205 97 50 38 33 3 7 38 35

5.8 11.2 10.0 24.8 43.3 33.8 77.9 41.6 22.5 15.4 26.1 26.4 13.0 93.6 65.1

Capital raised (bn RMB)

Total

908

266

173

245

1081

510

Table 2 Industry distributions of SEOs, 1994–2008. The sample period is from 1994 to 2008. The industry classification is based on the CSRC industry classification code. Industry code

Industry description

No. of rights issues (1)

No. of public offerings (2)

A B C D E F G H J K L M

Agriculture Mining Manufacturing Utilities Building Transportation Information Technology Wholesale and Retail Real Estate Services Media Others

18 6 478 41 15 25 37 119 40 26 9 94

1 2 115 9 3 2 10 4 13 4 2 8

19 8 593 50 18 27 47 123 53 30 11 102

1.8 0.7 54.9 4.6 1.7 2.5 4.3 11.4 4.9 2.8 1.0 9.4

20 12 507 32 19 26 38 77 38 24 10 62

25 16 676 42 19 39 59 82 43 33 10 72

81.1 73.6 75.0 76.6 100.0 66.7 64.9 94.1 88.8 73.8 100.0 86.5

908

173

1081

100.0

865

1114

77.6

Total

i in the same year to proxy for the tradeoff theory (Tradeoff) (e.g., Hovakimian et al., 2001). Leverage is measured by the ratio of total debt to total assets of the firm. Following Kim and Weisbach (2008), we use the market to book ratio (MB) to construct a measure of overvaluation as a proxy for market timing. More specifically, overvaluation is measured by the difference between the firm i’s MB and the average MB of all other firms excluding firm i in the same industry in the same year (Overvalue). We proxy for agency conflict between managers and shareholders (managerial discretion) by extra administrative expenses (EAE). Administrative expenses are financial sources that can be easily manipulated by mangers; hence administrative expenses might be used by managers for their private benefits of control (e.g. Bai et al., 2004). More specifically, we construct extra administrative expenses (EAE) of the firm to proxy for managerial discretion, which is measured as the difference between the firm i’s administrative expenses and the average administrative expenses of all other firms in the same industry excluding firm i in the same year. These extra administrative expenses (EAE) can better reveal the degree of managerial discretion after considering for the average level of administrative expenses of other firms in the same industry. Finally, the conflict of interest between controlling and minority shareholders is proxied by the ratio of non-tradable shareholdings to total shares outstanding (Nontradable). As mentioned earlier, one distinguished feature of Chinese corporate

Total no. of SEOs (3)

As % of total SEOs (4)

No. of SEO firms (5)

Average no. of listed firms (6)

(7) = (5)/(6)  100

governance is the phenomenon of non-tradable shares held mainly by state and state-related legal agencies before 2005 (e.g., Wang, 2005). Hence, non-tradable shareholdings directly represent the interests of controlling shareholders in Chinese listed firms before 2005. Even after the ownership split reform, there are still a significant amount of non-tradable shares in the listed firms due to the lock-up period and the restrictions on the amount of non-tradable shares to be sold to the market within a specific period of time, which allows us to use non-tradable shares as a proxy for the conflict between controlling and monitory shareholders after 2005 until 2008. The logic is that if controlling shareholders use SEOs to expropriate minority shareholders, then non-tradable shareholdings should be positively associated with SEOs activities. Besides the proxies for the main standard theories that may motivate the firm’s SEOs, we include the following control variables in empirical analyses: Firm size (Size). Firm size is a relevant variable when examining the motivation of equity issue behavior (Kim and Weisbach, 2008). We use firm size as a control variable due to the following considerations: (a) to control for the effect of administrative intervention in the listing process. As explained earlier in Section 2, the quota system adopted in China restricted the size of Chinese IPOs, which may result in the desire for additional equity issues for Chinese listed firms; (b) Size also carries the effect of financial constraints faced by the firm. Larger firms in general have more sources of

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financing than smaller firms, hence the demand for external equity financing may be lower. Firm size is measured by the natural logarithm of a firm’s total assets. Profitability (Profit). We use the firm’s profitability to capture the availability of internal funds. The firm who has more internal funds may need less external financing, implying few SEOs activities according to the pecking order theory (Myers and Majluf, 1984). Profitability is measured by earnings before interests and tax scaled by total assets. Debt (Debt). Debt is another source of external financing. The availability of debt for the firm affects the firm’s SEOs decisions. More debt financing may lead to less equity financing according to the pecking order theory. However, too much debt may also require the firm to issue more equities to balance the capital structure according to the tradeoff theory (e.g., Hovakimian et al., 2001). Debt is measured by the ratio of total debt to total assets of the firm. Stock market volatility (Volatility). Because equity financing decisions are responsive to stock market volatility, we need to isolate stock market volatility from other factors affecting SEOs. For example, SEOs respond to both market volatility and market overvaluation. To examine the impact of market overvaluation, we should control for stock market volatility. We construct a measure of market volatility as the difference between the standard deviation of the firm i’s daily stock returns and the average standard deviation of daily stock returns of other firms in the same industry excluding firm i.This volatility measure captures idiosyncratic volatility of firm i’s stock returns. In addition, we use two variables to control for multiple SEOs: (a) the number of years between the last and the current SEOs (Gap) is used to control for the effect of previous SEOs; (b) a dummy variable (Next) that takes the value of one if the firm has another SEOs in 3 years subsequent to the current SEOs. Considering that SEOs decisions differ across industries, we also control for the industry effect by adding an industry dummy (Industry) in estimations. Our benchmark empirical model is then specified as follows:

SEOit ¼ b0 þ b1 Industryi;t1 þ b2 Gapi:t1 þ b3 Nextit þ b4 Sizei;t1 þ b5 Profiti;t1 þ b6 Debti;t1 þ b7 Volatilityi;t1 þ b8 TheoryProxyi;t1 þ eit

ð1Þ

We first estimate Eq. (1) using a panel data fixed effect logit model and then using a cross-section model. In the panel data logit estimation, the dependent variable SEOit is a dummy variable, taking the

value of one if the firm conducts SEOs in year t , and zero otherwise. In the cross-section estimation, the dependent variable is the amount of capital raised via SEOs scaled by total assets of the firm (SEOCapitalt). We use the lagged-one period observations of independent variables (except for Next) because the observations in year t contain the information on the consequences of SEOs in the same year. We first test the theories by using the proxy for a specific theory one by one, and then in the expanded full model we put the proxies of all relevant theories in the same estimation. Table 3 displays some characteristics between SEOs firm-years (SEOs cases hereafter) and non-SEOs firm-years (non-SEOs cases hereafter). We observe that SEOs cases are associated with smaller firm size with a t-test statistics of t = 8.51. SEOs cases also experience higher profitability than non-SEOs cases (t = 21.09). Investment ratio is also higher for SEOs cases than that for non-SEOs cases (t = 12.74). Although sales growth appears to be higher for non-SEOs cases, the difference between the two groups in sales growth is not statistically significant (t = 0.89). On average SEOs cases seem to have a lower debt to assets ratio as compared to non-SEO cases (t = 5.23), indicating that the firm may need to use SEOs as a means to raise capital when its borrowing is low. However, the average difference between the firm’s leverage and the industry average, i.e., Tradeoff , for SEOs cases is not significantly different from that of non-SEOs cases (t = 0.78), providing initial evidence that the firm’s SEOs decision may not be motivated by the tradeoff theory. It appears that the industry adjusted stock return volatility (Volatility) for SEOs cases is lower than that for non-SEOs cases (t = 7.65), which may be a consequence of timing the market by issuing SEOs because market timing may result in that the share prices of SEOs cases are closer to the industry level. We also observe from Table 3 that SEOs cases on average have a higher market to book ratio (MB) than non-SEOs cases (t = 9.26). In addition, SEOs cases also enjoy a higher overvaluation (Overvalue) as compared to non-SEOs cases. The mean of overvaluation for SEOs cases is 0.3444, while that of non-SEOs cases is 0.0845. The difference in overvaluation between SEOs and non-SEOs cases is statistically significant (t = 3.544). The observations on both the market to book ratio and overvaluation provide us with initial evidence that SEOs may be motivated by taking advantage of market overvaluation. As far as extra administrative expenses (EAE) is concerned, we see that SEOs cases have a lower extra administrative expenses (EAE) as compared to non-SEOs cases (t = 2.30). This seems to be contradictory to the agency costs explanation of SEOs, which predicts that SEOs proceeds are used by managers for their private benefits of control. However, Table 3 shows that the ratio of

Table 3 Characteristics between SEOs firm-years and non-SEOs firm-years. The sample period is from 1994 to 2008. Size is measured as the logarithmic total assets of the firm. Profit denotes profitability of the firm, which is measured by earnings before interest and tax scaled by total assets. Debt is the ratio of total debt to total assets of the firm. Volatility is measured by the difference between the standard deviation of the firm i’s daily stock returns and the average standard deviation of daily returns of other firms in the same industry excluding firm i. Growth denotes the firm’s annual growth rate of sales. Investment is measured as the change in fixed assets scaled by total assets of the firm. Tradeoff denotes the difference between the firm i’s leverage and the average leverage of other firms in the same industry excluding firm i in the same year. MB denotes the market to book ratio. Overvalue denotes overvaluation, which is measured as the difference between the firm i’s MB and the average MB of other firms in the same industry excluding firm i in the same year. EAE denotes extra administrative expenses, which is measured as the difference between the firm i’s administrative expenses and the average administrative expenses of other firms in the same industry excluding firm i in the same year. Nontradable is the ratio of non-tradable shareholdings to total shares outstanding of a firm. SEOs firm year

Size Profit Debt Volatility Growth Investment Tradeoff MB Overvalue EAE Nontradable

Non-SEOs firm year

t-Test statistics

Mean

Median

St.Dev.

Obs.

Mean

Median

St.Dev.

Obs.

20.7958 0.0732 0.2152 0.0008 0.2943 0.0711 0.0000 4.4341 0.3444 0.0018 0.6408

20.6911 0.0652 0.2136 0.0008 0.1935 0.0469 0.0045 3.8685 0.1237 0.0059 0.6721

0.9786 0.0382 0.1292 0.0036 0.4548 0.0991 0.1276 2.4322 2.1869 0.0226 0.1364

1036 1033 1036 1036 1018 986 1036 1034 1034 1001 1036

21.0729 0.0188 0.2397 0.0001 0.3219 0.0259 0.0037 3.6749 0.0845 0.0000 0.5628

21.0093 0.0333 0.2217 0.0002 0.1215 0.0129 0.0191 2.9398 0.4179 0.0066 0.5848

1.0236 0.2015 0.2131 0.0048 2.3239 0.1360 0.2124 2.7042 2.3523 0.0304 0.1539

7743 7721 7741 7708 7125 7063 7741 7369 7368 7446 7708

8.512 21.090 5.235 7.655 0.891 12.741 0.776 9.266 3.544 2.303 17.014

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non-tradable shares to total shares outstanding is higher for SEOs cases than non-SEOs cases (t = 17.01), suggesting that SEOs activities may be motivated by expropriation of minority shareholders by controlling shareholders.

5. Empirical results 5.1. Evidence on market timing We first estimate Eq. (1) using a panel data fixed effect logit model in Stata. In this estimation we check whether the proxies for SEOs theories are able to predict the likelihood that the firm conducts SEOs. The results are reported in Table 4. Each column in Table 4 except for the last column corresponds to each theory that explains the motivation of SEOs. In column (6) of Table 4 we combine all the mentioned theories in one estimated equation due to the concern that the proxies for different theories may be correlated with each other and hence carry the effect of other theories. As we can see from Table 4, the estimated coefficient for fixed investment (Investment) has a negative sign and that for growth (Growth) is insignificant in both columns (1) and (6), suggesting that financing for investment and growth may not be a motivation for the firm to conduct SEOs. The estimated coefficient for the proxy of the tradeoff theory (Tradeoff) is insignificant in both columns (2) and (6), indicting that the firm does not seem to use SEOs to adjust its capital structure, inconsistent with the tradeoff theory. Both columns (3) and (6) show that the estimated coefficient for overvaluation (Overvalue) is highly significant with a positive sign, implying that the firm is more likely to issue SEOs

when it is overvalued by the stock market, which is consistent with the theory of market timing. In columns (4) and (6) the estimated coefficient for extra administrative expresses (EAE) is significant with a positive sign, suggesting that the need for a larger amount of extra administrative expenses may be the reason why the firm issues SEOs. Column (6) of Table 4 shows that non-tradable shareholdings (NonTradable) is significantly related to the SEOs dummy but with a negative sign. Given that non-tradable shares are mainly held by the state and the state-related agencies who are in most cases controlling shareholders in Chinese listed firm, this result does not support the expropriation explanation claimed by previous studies that SEOs are mainly used by controlling shareholders as a means to exploit outside minority shareholders in China (e.g., Aharony et al., 2000; Jian and Wong, 2004). In sum, based on the evidence reported in Table 4, we can tentatively conclude that both the market timing and the agency costs (managerial discretion) explanations seem to be important motives of Chinese SEOs. Given that firms do not conduct SEOs every year, in panel data logit estimation we can only use a dummy variable to capture whether the firm is active in issuing SEOs in a specific year. However, perhaps a more accurate measurement of SEOs activities is the amount of capital raised via SEOs. To be able to utilize this information, we reduce our sample to SEOs cases. In other words, we run the cross-section regression based on SEOs cases. We estimate Eq. (1) using Ordinary Least Squares (OLS). In this set of estimation, we use the lagged-one period observations of independent variables (except for Next) to explain how much funds raised through SEOs in the current year, where the dependent variable is the SEOs proceeds scaled by total assets of the firm (SEOcapital). The results are shown in Table 5. The estimated coefficient for

Table 4 Motivation of SEOs: panel data fixed effect logit estimation. This table presents results of panel data fixed effect logit estimation of Eq. (1). The sample period is from 1994 to 2008. The dependent variable is whether the firm conducts SEOs in year t. Gap indicates the number of years between the current SEOs and the last SEOs. Next is a dummy variable that takes the value of one if the firm issue SEOs in the next 3 years after the current SEOs. Size is measured as the logarithmic total assets of the firm. Profit denotes profitability of the firm, which is measured by earnings before interest and tax scaled by total assets. Debt is the ratio of total debt to total assets of the firm. Volatility is measured by the difference between the standard deviation of the firm i’s daily stock returns and the average standard deviation of daily returns of other firms in the same industry excluding firm i. Growth denotes the firm’s annual growth rate of sales. Investment is measured as the change in fixed assets scaled by total assets of the firm. Tradeoff denotes the difference between the firm i’s leverage and the average leverage of other firms in the same industry excluding firm i in the same year. Overvalue denotes overvaluation, which is measured as the difference between the firm i’s market to book ratio (MB) and the average MB of other firms in the same industry excluding firm i in the same year. EAE denotes extra administrative expenses, which is measured as the difference between the firm i’s administrative expenses and the average administrative expenses of other firms in the same industry excluding firm i in the same year. Nontradable is the ratio of non-tradable shareholdings to total shares outstanding of a firm. Industry effect is controlled by adding an industry dummy. The figures in parentheses are the t-statistics.

Gapt1 Nextt Sizet1 Profitt1 Debtt1 Volatilityt1 Growtht1 Investmentt1

(1)

(2)

(3)

(4)

(5)

(6)

0.5414 (7.85) 69.5926 (0.15) 0.1080 (0.64) 12.0966 (4.74) 5.6437 (5.96) 17.9799 (1.06) 0.0906 (1.37) 1.9024 (3.00)

0.7641 (11.94) 64.3820 (0.18) 0.4031 (2.60) 10.3016 (4.62) 7.0430 (2.87) 35.2867 (2.96)

0.7489 (11.71) 25.1571 (0.06) 0.3015 (1.95) 5.8949 (2.46) 4.7984 (5.81) 45.7381 (3.72)

0.7821 (11.84) 49.8920 (0.04) 0.3139 (1.97) 11.1813 (4.90) 5.6929 (6.66) 40.6452 (3.25)

0.7647 (11.87) 65.2867 (0.12) 0.4090 (2.47) 10.0706 (4.54) 5.3203 (6.46) 35.8150 (3.00)

0.3921 (0.41)

0.5315 (7.43) 24.5858 (0.03) 0.1825 (0.93) 7.7425 (2.81) 6.5868 (2.24) 33.7141 (1.88) 0.1253 (1.62) 1.6328 (2.47) 0.9180 (0.33) 0.1955 (4.10) 11.9015 (2.27) 2.1012 (1.92)

Included 2739.83 (0.000) 7653

Included 2333.50 (0.000) 5936

Tradeofft1

1.8073 (0.77)

Overvaluet1

0.1825 (4.36)

EAEt1

Industry Chi-squared (p-value) Obs.

12.2989 (2.80)

Included 2415.18 (0.000) 6300

Included 2740.26 (0.000) 7653

Included 2695.09 (0.000) 7416

Included 2627.05 (0.000) 7218

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Table 5 Motivation of SEOs: cross-section estimation. This table presents results of cross-sectional estimation of Eq. (1). The sample period is from 1994 to 2008. Gap indicates the number of years between the current SEOs and the last SEOs. Next is a dummy variable that takes the value of one if the firm issue SEOs in the next 3 years after the current SEOs. Size is measured as the logarithmic total assets of the firm. Profit denotes profitability of the firm, which is measured by earnings before interest and tax scaled by total assets. Debt is the ratio of total debt to total assets of the firm. Volatility is measured by the difference between the standard deviation of the firm i’s daily stock returns and the average standard deviation of daily returns of other firms in the same industry excluding firm i. Growth denotes the firm’s annual growth rate of sales. Investment is measured as the change in fixed assets scaled by total assets of the firm. Tradeoff denotes the difference between the firm i’s leverage and the average leverage of other firms in the same industry excluding firm i in the same year. Overvalue denotes overvaluation, which is measured as the difference between the firm i’s market to book ratio (MB) and the average MB of other firms in the same industry excluding firm i in the same year. EAE denotes extra administrative expenses, which is measured as the difference between the firm i’s administrative expenses and the average administrative expenses of other firms in the same industry excluding firm i in the same year. Nontradable is the ratio of non-tradable shareholdings to total shares outstanding of a firm. Industry effect is controlled by adding an industry dummy. The figures in parentheses are the t-statistics, computed using the white (1980) heteroskedasticity consistent standard error.

Constant Gapt1 Nextt Sizet1 Profitt1 Debtt1 Volatilityt1 Growtht1 Investmentt1

(1)

(2)

(3)

(4)

(5)

(6)

0.9809 (7.57) 0.0108 (4.22) 0.0322 (2.99) 0.0392 (8.21) 0.6707 (5.28) 0.0330 (0.90) 4.7875 (3.96) 0.0073 (0.74) 0.0392 (0.89)

1.1278 (7.29) 0.0069 (1.78) 0.0390 (2.58) 0.0466 (6.42) 0.7867 (4.21) 0.0259 (0.10) 7.6244 (4.31)

1.0489 (6.67) 0.0043 (1.11) 0.0399 (2.67) 0.0403 (5.45) 0.5322 (2.54) 0.1097 (1.96) 6.8323 (3.85)

0.9519 (8.93) 0.0102 (4.05) 0.0398 (3.84) 0.0385 (7.86) 0.7191 (5.59) 0.0324 (0.86) 4.4885 (3.75)

1.2075 (7.57) 0.0057 (1.49) 0.0386 (2.58) 0.0466 (6.57) 0.8047 (4.31) 0.0733 (1.35) 7.5197 (4.28)

0.0839 (1.75)

0.9491 (9.07) 0.0060 (2.24) 0.0350 (3.40) 0.0350 (7.22) 0.4541 (3.34) 0.0998 (0.63) 4.0467 (3.46) 0.0009 (0.09) 0.0455 (1.09) 0.1633 (1.05) 0.0095 (4.35) 0.1522 (0.85) 0.0828 (2.70)

Included 0.1022 8.43 (0.00) 981

Included 0.2103 11.98 (0.00) 826

Tradeofft1

0.1000 (0.42)

Overvaluet1

0.0088 (2.58)

EAEt1

Industry Ad.R-squared F-statistic (p-value) Obs.

0.1864 (0.99)

Included 0.1691 11.67 (0.00) 840

Included 0.0995 8.21 (0.00) 981

financing for investment (Investment) becomes positive but insignificant in columns (1) and (6). The estimated coefficient for the tradeoff theory (Tradeoff) remains insignificant. The positive association between SEOs and managerial discretion (EAE) disappears in columns (4) and (6) in Table 5. The estimated coefficient for non-tradable shareholdings (NonTradable) remains significant with the same sign (negative) as in Table 4. More importantly for the purpose of the paper, in Table 5 we again obtain evidence that the estimated coefficient for overvaluation (Overvalue) is significant with positive sign in both columns (3) and (6). Since the result concerning non-tradable shareholdings (NonTradable) in both Tables 4 and 5 does not support the expropriation explanation claimed by previous studies on Chinese SEOs, we argue that Chinese SEOs can be explained by other theories rather than the conflict between controlling and minority shareholders. On the ground that the market timing theory is consistently confirmed in both Tables 4 and 5, in the following empirical analyses we concentrate on the market timing explanation. Apart from the results concerning the proxies for the theories explaining SEOs motivation, some control variables turn out to be significant. Firstly, from Tables 4 and 5 we observe that the estimated coefficient for firm size (Size) is highly significant with a negative sign in almost all the estimations, suggesting that smaller firms are more likely to conduct SEOs (Table 4) and raise more funds through SEOs (Table 5). This result confirms our conjecture that the administrative intervention in the listing process created hunger for SEOs after the firm’s IPO (see Section 2). However, the

Included 0.1055 8.70 (0.00) 981

Included 0.1480 11.85 (0.00) 938

result concerning firm size does not confirm the financial constraints explanation, which predicts that smaller firms should face more difficulties in obtaining external financing and hence few SEOs activities. Secondly, all the estimated equations in both Tables 4 and 5 show that the estimated coefficient for profitability (Profit) is highly significant with a positive sign, which is not in line with the prediction of the pecking order financing theory. The positive relation between profitability and SEOs suggests that the firms may need SEOs for other reasons rather than financing for investment and growth. Thirdly, the estimated coefficient for debt (Debt) is positive and significant in panel data estimations (Table 4). This result is not consistent with the pecking order theory either. However, the estimated coefficient for debt is in most cases not significant in cross-section estimations (Table 5). Finally, the relation between SEOs and stock market volatility (Volatility) is not clear though. In Table 4, the estimated coefficient for volatility (Volatility) is mostly negatively significant, while it is highly positively significant in Table 5. Assuming that the results base on SEOs cases (Table 5) are more informative than that base on the SEOs dummy variable (Table 4),9 then according to Table 5 the positive relation between the firm’s stock returns volatility and SEOs proceeds is consistent with our main result on timing the market. In 9 In cross-sectional (SEOs cases) estimations, we have the direct observation on the quantitative measure of SEOs activities, i.e., SEOs proceeds, which allows us to directly link how much capital raised via SEOs with the firm’s real and other financial activities.

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general, higher stock returns volatility implies higher average stock returns,10 suggesting possible upward market valuation of the firm, which provides the firm with stronger incentives to raise more capital via SEOs. Now that the market timing theory has been clearly confirmed by evidence in both Tables 4 and 5, we conduct a few additional tests to provide robustness evidence. To save space we focus on cross-section estimation based on SEOs cases. To proceed, we first check the ex post impact of SEOs on the firm’s end-of-year abnormal stock returns by estimating the following equation:

Returnat ¼ b0 þ b1 Industryt þ b2 Gapt þ b3 Sizet þ b4 Profit t þ b5 Debtt þ b6 Volatilityt þ b7 Growtht þ b8 Inv estment t þ b9 SEOcapitalt þ et

ð2Þ

In the above equation the key explanatory variable is the amount of capital raised through SEOs scaled by total assets of the firm, i.e., SEOcapital. All other variables involved are defined as the same as in Section 4.2. In this test, we use all the explanatory variables in year t to check the impact of them on the firm’s end-of-year abnormal stock returns. We are particularly interested in the estimated coefficient b9 , which captures the ex post effect of SEOs on the firm’s end-of-year abnormal stock returns (Returna). The firm’s abnormal stock return is defined as the difference between the average annualized daily stock returns of firm i and the average annualized daily stock returns of other firms in the same industry excluding firm i in the same year. We do so to exclude the common movement of the firm’s stock returns. In the standard literature, it is quite common to check whether SEOs activities negatively affect the firm’s stock returns, if so, it indirectly confirms that the firm is actually taking advantage of market overvaluation by issuing more seasoned equities (e.g., Henderson et al., 2006). The result of estimating Eq. (2) is reported in column (1) of Table 6, which shows that the estimated coefficient for SEOCapital is negatively significant in explaining the firm’s end-of-year abnormal stock returns. We further test the market timing theory by following Kim and Weisbach (2008). These authors point out that the firm issuing SEOs when its market value is relatively high may not be necessarily driven by timing the market. Instead it is likely that the firm with high market value has more investment opportunities and greater growth potential, hence the firm needs more external capital. If this is the case, even if the firm issues SEOs when its market value is high, the firm may not be motivated by taking advantage of market overvaluation. Therefore it is necessary to distinguish between market timing overvaluation SEOs and non-market timing overvaluation SEOs. According to Kim and Weisbach (2008) if a firm with high market value is motivated by timing the market, then it should stockpile cash raised via SEOs, while a firm with high market value who issues SEOs but is not motivated by taking advantage of market overvaluation should not stockpile capital raised via SEOs, instead the firm should invest more SEOs proceeds in fixed investment. Following Kim and Weisbach (2008), we estimate the following equation to further test the market timing theory,

Inv estment t ¼ b0 þ b1 Industryt þ b2 Gapt þ b3 Sizet þ b4 Profit t þ b5 Debtt þ b6 Volatilityt þ b7 Growtht þ b8 SEOCapitalt þ b9 SEOCapitalt  Ov erv aluet þ b10 Ov erv aluet þ et

ð3Þ

10 For the positive relation between the level and its variability of a stochastic variable, see Bo and Sterken (2002) and the cited literature there.

Table 6 Further evidence on market timing. This table presents further evidence on market timing. The sample period is from 1994 to 2008. The dependent variable varies across estimations. Returnat is the firm’s abnormal stock return defined as the difference between the annual average daily stock returns of firm i and the annual average daily returns of other firms in the same industry excluding firm i in the same year. Investment is measured as the change in fixed assets scaled by total assets of the firm. Returnt+3 is the firm’s average daily stock reruns over 3 years after the current SEOs. Returnatþ3 is the firm’s average abnormal stock returns over 3 years after the current SEOs, where the firm’s abnormal stock returns is adjusted by the stock market index. Gap indicates the number of years between the current SEOs and the last SEOs. Next is a dummy variable that takes the value of one if the firm issue SEOs in the next 3 years after the current SEOs. Size is measured as the logarithmic total assets of the firm. Profit denotes profitability of the firm, which is measured by earnings before interest and tax scaled by total assets. Debt is the ratio of total debt to total assets of the firm. Volatility is measured by the difference between the standard deviation of the firm i’s daily stock returns and the average standard deviation of daily returns of other firms in the same industry excluding firm i. Growth denotes the firm’s annual growth rate of sales. Overvalue denotes overvaluation, which is measured as the difference between the firm i’s market to book ratio (MB) and the average MB of other firms in the same industry excluding firm i in the same year. SEOCapital denotes the proceeds raised through SEOs scaled by total assets of the firm. The figures in parentheses are the tstatistics, computed using the white (1980) heteroskedasticity consistent standard error.

Constant Gapt

(1) Returnat

(2) Investmentt

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

0.0022 (2.29) 0.00002 (1.11)

0.0822 (0.95) 0.0070 (3.57)

0.0002 (0.25) 0.00003 (1.55)

0.0001 (3.33) 0.0118 (9.44) 0.0018 (5.31) 0.0628 (5.69) 0.0002 (5.24) 0.0009 (2.45) 0.0004 (2.33)

0.0067 (1.66) 0.3593 (3.24) 0.0918 (2.97) 0.2324 (0.25) 0.0187 (4.06)

0.0212 (1.25)

0.000002 (0.05) 0.0078 (6.25) 0.0013 (3.81) 0.0583 (5.56) 0.0002 (4.98) 0.0009 (2.52) 0.0004 (2.41)

0.0041 (4.25) 0.00004 (1.72) 0.0005 (6.02) 0.0002 (4.47) 0.0011 (1.01) 0.0008 (2.63) 0.0501 (4.97) 0.00006 (1.39) 0.0004 (1.27) 0.0003 (1.78)

0.0027 (4.23) 0.00003 (2.10) 0.0002 (4.43) 0.0001 (4.44) 0.0009 (1.21) 0.0003 (1.58) 0.0319 (4.80) 0.00005 (1.84) 0.00006 (0.27) 0.0002 (1.73)

0.0188 (2.83) 0.0029 (1.07)

0.0003 (4.24) 0.0003 (9.92)

Included 0.1686 13.38

Included 0.0953 7.05

Included 0.2537 19.43

Included 0.1063 7.35

Included 0.0739 5.26

(0.00) 978

(0.00) 977

(0.00) 977

(0.00) 909

(0.00) 909

Nextt Sizet Profitt Debtt Volatilityt Growtht Investmentt SEOCapitalt SEOCapitalt  Overvalue

Overvaluet Industry Ad.R-squared F-statistic (pvalue) Obs.

We are interested in

@Inv estment t ¼ b8 þ b9 Ov er v aluet : @SEOCapitalt If the estimated coefficients for both b8 and b9 are positive in (3), then the sensitivity of investment to the capital raised via SEOs will be larger for the firm with higher market overvaluation, which implies that, although the firm issues SEOs when market overvaluation is high the firm is mainly motivated to raise extra capital to finance investment. Therefore, market timing does not explain the motivation of the firm’s SEOs. However, if the estimated coefficient b9 is negative and significant in (3), then we can conclude that the firm is mainly motivated by timing the market. The result of estimating model (3) is reported in column (2) of Table 6. We can see that the estimated coefficient for b8 is positive but insignificant,

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i.e., investment undertaken by our sample firms does not really depend on the capital raised via SEOs. More importantly, the estimated coefficient for b9 is negative and significant. Putting together, the results on both b8 and b9 show that SEOs of our sample firms do not seem to be explained by financing for investment and growth theory, indirectly lending support to the market timing explanation. Next we apply the logic of estimating Eq. (3) to check how the market overvaluation affects the sensitivity of the firm’s endof-year abnormal stock reruns to SEOs. We estimate the following model,

Returnat ¼ b0 þ b1 Industryt þ b2 Gapt þ b3 Sizet þ b4 Profitt þ b5 Debtt þ b6 Volatilityt þ b7 Growtht þ b8 Inv estment t þ b9 SEOCapitalt þ b10 SEOCapitalt  Ov erv aluet þ b11 Ov erv aluet þ et

ð4Þ

Here we are interested in

@Returnat ¼ b9 þ b10 Ov erv aluet : @SEOCapitalt If the estimated coefficients for both b9 and b10 are negative and significant in Eq. (4), then the end-of-year abnormal stock returns will be lower for firms with higher market overvaluation. If the firm with high market overvaluation faces more severe decline in post SEOs stock reruns as compared to low market overvaluation firms, then it suggests that market timing explains why the firm issues SEOs. The result of estimating Eq. (4) is reported in column (3) of Table 6. We observe that the estimated coefficient for b9 is significant with a negative sign, consistent with the result we obtained in column (1) of Table 6. More importantly, the estimated coefficient for b10 is negative and highly significant. The results on both

b9 and b10 suggest that the firm that is relatively overvalued will suffer more severely in terms of it’s the end-of-year post SEOs abnormal stock returns as compared to the firms that are less overvalued. This result lends further support to the market timing explanation. In columns (4) and (5) of Table 6 we conduct further estimations to check the robustness of the results. In the literature, a common approach to testing market timing of equity issuance is to use long term stock returns, normally 3 years (e.g., Baker et al., 2003). Following this literature we use two longer term stock performance measures: (a) the firm’s average daily stock reruns over 3 years after the current SEO (Returnt+3) ; (b) the firm’s average abnormal stock returns over 3 years after the current
SEO Returnatþ3 , where the firm’s long term abnormal return is computed by using the stock market index to adjust the firm’s long term stock performance (e.g., DeAngelo et al., 2010). In this set of estimation we control for an additional variable, i.e., the dummy variable (Next) indicating whether the firm has other SEOs in the next 3 years after the current SEOs since this dummy variable is particularly relevant for testing the firm’s 3 year long term stock performance. Again we see that the market timing theory is supported by evidence reported in columns (4) and (5) of Table 6. This result suggests that more funds raised by SEOs will lead to larger decline in both the firm’s long term stock returns and the firm’s long term market adjusted abnormal stock returns, further confirming that market timing is an important motivation for our sample firms to conduct SEOs. 5.2. Does SEOs behavior differ between types of SEOs? Different types of SEOs may contain different information for both the market and shareholders. In particular in China, the two

Table 7 Motivations of SEOs between rights issues and public offerings. The sample period is from 1994 to 2008. The dependent variable is SEO proceeds/total assets in year t. The independent variables are the same as those in Table 5. The figures in parentheses are the t-statistics, computed using the white (1980) heteroskedasticity consistent standard error. Rights issues

Constant Gapt1 Nextt Sizet1 Profitt1 Debtt1 Volatilityt1 Growtht1 Investmentt1

Public offerings

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

0.8951 (8.95) 0.0052 (1.92) 0.0134 (1.47) 0.0354 (7.50) 0.6094 (5.21) 0.0759 (2.22) 3.1275 (2.87) 0.0147 (1.60) 0.0388 (1.04)

1.1847 (6.25) 0.0011 (0.23) 0.0275 (1.74) 0.0453 (5.22) 0.7222 (3.44) 0.3602 (1.13) 6.0500 (3.09)

0.9139 (6.94) 0.0033 (0.92) 0.0209 (1.83) 0.0353 (5.50) 0.5109 (3.01) 0.0865 (1.89) 2.4165 (1.70)

0.8905 (5.05) 0.0040 (1.42) 0.0251 (2.70) 0.0346 (6.75) 0.6896 (5.53) 0.0689 (1.89) 2.4656 (2.16)

1.2263 (6.44) 0.0022 (0.45) 0.0253 (1.62) 0.0464 (5.39) 0.7668 (3.67) 0.1152 (1.88) 6.2040 (3.19)

2.0164 (6.88) 0.0018 (0.30) 0.1310 (2.39) 0.0853 (6.65) 1.1905 (3.30) 0.0036 (0.03) 13.1609 (3.51) 0.0174 (0.64) 0.2839 (1.52)

2.0285 (6.90) 0.0015 (0.26) 0.1434 (2.62) 0.0858 (6.72) 1.1865 (3.33) 0.1083 (0.25) 13.2634 (3.57)

1.9694 (7.04) 0.0059 (1.05) 0.1199 (2.25) 0.0798 (6.49) 0.6915 (1.85) 0.0127 (0.13) 11.3564 (3.16)

2.0688 (7.02) 0.0008 (0.14) 0.1438 (2.63) 0.0867 (6.75) 1.1375 (3.09) 0.0236 (0.23) 13.1485 (3.57)

2.0919 (6.95) 0.0018 (0.31) 0.1371 (2.47) 0.0863 (6.81) 1.1897 (3.34) 0.0338 (0.34) 13.0117 (3.53)

0.0906 (1.65)

0.9156 (9.12) 0.0027 (0.97) 0.0220 (2.64) 0.0307 (6.46) 0.4493 (3.67) 0.2968 (1.79) 2.0621 (2.04) 0.0114 (1.35) 0.0398 (1.15) 0.2055 (1.26) 0.0066 (3.35) 0.0527 (0.33) 0.0730 (2.64)

0.0539 (0.63)

2.0304 (6.66) 0.0021 (0.35) 0.0886 (1.61) 0.0784 (6.05) 0.6267 (1.61) 0.2122 (0.50) 10.1097 (2.71) 0.0582 (2.02) 0.2668 (1.46) 0.1567 (0.38) 0.0246 (3.78) 0.1012 (0.19) 0.1001 (1.19)

Included 0.0975 6.89 (0.00) 819

Included 0.2501 12.11 (0.00) 667

Included 0.3748 6.92 (0.00) 159

Included 0.3754 7.45 (0.00) 162

Included 0.4201 6.72 (0.00) 159

Tradeofft1

0.2452 (0.78)

Overvaluet1

0.0055 (2.01)

EAEt1

Industry Ad.R-squared F-statistic (p-value) Obs.

0.0740 (0.40)

Included 0.1987 13.04 (0.00) 681

Included 0.0951 6.73 (0.00) 819

Included 0.1429 8.87 (0.00) 819

Included 0.1584 10.72 (0.00) 776

0.0762 (0.18) 0.0019 (2.36) 0.2772 (0.52)

Included 0.3738 7.40 (0.00) 162

Included 0.4187 8.73 (0.00) 162

Included 0.3748 7.43 (0.00) 162

1153

H. Bo et al. / Journal of Banking & Finance 35 (2011) 1143–1157

main types of SEOs, i.e., rights issues and public offerings, are regulated by the CSRC differently. For example, the number of new shares issued in right issues was restricted to 30% of total current shares, while in public offerings it was required that the new funds raised cannot exceed the firm’s total equity in the previous year. Controlling shareholders must make a promise to subscribe a certain amount of shares prior to the shareholders’ general meeting in rights issues, while they could give up any subscriptions in public offerings. Moreover, if the controlling shareholders fail to fulfill the promise in rights issues, the issuers must return all the funds to subscribed shareholders together with the costs of funds. Consequently, the issue prices of the two types of SEOs turn out to be different. The issue prices for rights issues are much lower than the market price, while the issue prices for public offerings are closer to the market price. Logically, firms are expected to be more prudent in rights issues than in public offerings. These may induce different SEOs behavior. In this subsection, we check if SEOs motivation differs between rights issues and public offerings. More specifically, we repeat the estimations shown in Tables 5 and 6 for rights issues and public offerings, respectively. The corresponding results are reported in Tables 7 and 8. Table 7 shows that the estimated coefficient for overvaluation (Overvalue) is positively significant in both columns (3) and (6) for both rights issues and public offerings, suggesting that among other possible theories, timing the market is important in explaining both rights issues and public offerings. However, in Table 8 rights issues receives stronger evidence (columns (2), (3) and (5)) that is consistent with the market timing explanation than public offerings does (columns (1) and (5)), which indicates that although both types of SEOs are mainly motivated by timing the market, the firm uses more rights issues to take advantage of market overvaluation than public offerings. This result is in line with

the SEOs practice in China. Since controlling shareholders must subscribe in rights issues and the firm must bear the risk of issue failure in rights issues, the firm has stronger incentive to time the market in rights issues than in public offerings. 5.3. SEOs behavior over time The empirical results we have obtained so far suggest that the Chinese SEOs are in general dominated by timing the market (Tables 4–6 ) and the market timing result is slightly different between rights issues and public offerings in terms of the extent to which market timing dominates (Tables 7 and 8). Another question arises concerning whether Chinese listed firms experience different SEOs behavior over time along with the progress of China’s market transition. The progress of market transition in China changes the condition of the capital market on the one hand and influences the development of corporate governance system of Chinese firms on the other. Therefore it is relevant to examine further whether the SEOs behavior of Chinese firms differs across different periods and whether these differences, if any, reflect the characteristics of China’s market transition at different stages. For this purpose we divide our sample period into three subsample periods: (a) 1994–1997; (b) 1998–2005; and (c) 2006–2008. The first subsample period (1994–1997) is the set-up stage of Chinese stock markets, during which the listing process was tightly controlled by the government and the listed firms were mainly related to the former state-owned enterprises. In this period the listed firms were just starting to perform as modern corporations. In the second subsample period (1998–2005), firms have gradually established a modern corporate governance system. In 1998 Chinese listed firms started formally and regularly to report corporate governance information. Additionally, the institution of Board of

Table 8 Further evidence on market timing between rights issues and public offerings. The sample period is from 1994 to 2008. The dependent variable varies across estimations. The independent variables are the same as those in Table 6. The figures in parentheses are the t-statistics, computed using the white (1980) heteroskedasticity consistent standard error. Rights Issues

Constant Gapt

Public Offerings

(1) Returnat

(2) Investmentt

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

(1) Returnat

(2) Investmentt

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

0.0013 (1.12) 0.00003 (1.32)

0.0422 (0.39) 0.0070 (2.75)

0.0025 (2.24) 0.00003 (1.25)

0.3810 (1.69) 0.0074 (2.08)

0.0077 (2.53) 0.00001 (0.31)

0.0010 (0.19) 0.3196 (2.66) 0.0966 (2.81) 0.4046 (0.39) 0.0265 (4.28)

0.0003 (2.43) 0.0157 (3.37) 0.0004 (0.51) 0.0619 (1.99) 0.00002 (2.97) 0.0018 (1.59) 0.0024 (3.65)

0.0196 (2.02) 0.7143 (2.09) 0.0380 (0.49) 1.9088 (0.82) 0.0074 (1.01)

0.0197 (1.07)

0.0001 (1.96) 0.0071 (5.46) 0.0013 (3.56) 0.0607 (5.46) 0.0002 (3.19) 0.0008 (2.21) 0.0002 (1.28)

0.0032 (4.78) 0.00005 (0.28) 0.0002 (3.87) 0.0005 (4.83) 0.0006 (0.75) 0.0004 (1.67) 0.0103 (2.53) 0.00002 (0.54) 0.0001 (0.37) 0.0002 (1.69)

0.0065 (2.18) 0.00004 (0.89)

0.0000 (0.06) 0.0001 (2.17) 0.0118 (8.82) 0.0022 (5.80) 0.0002 (4.11) 0.0007 (1.77) 0.00009 (0.45)

0.0065 (6.15) 0.00003 (0.10) 0.0004 (5.51) 0.0003 (6.29) 0.0012 (1.10) 0.0011 (3.49) 0.0559 (5.38) 0.00002 (0.48) 0.0003 (1.05) 0.0002 (1.28)

0.0039 (0.06)

0.0003 (2.68) 0.0145 (3.13) 0.0002 (0.27) 0.0621 (2.00) 0.0003 (3.24) 0.0015 (1.41) 0.0027 (3.50)

0.0064 (1.50) 0.0001 (1.97) 0.0011 (1.92) 0.0002 (1.50) 0.0007 (0.12) 0.0016 (1.29) 0.0024 (0.06) 0.00001 (0.12) 0.00007 (0.52) 0.0009 (1.14)

0.0030 (1.22) 0.00005 (1.69) 0.0008 (3.38) 0.0001 (1.11) 0.0015 (0.56) 0.0004 (0.59) 0.0296 (1.30) 0.0001 (1.46) 0.0020 (1.78) 0.0006 (1.76)

0.0347 (3.04) 0.0047 (1.29)

0.0004 (3.27) 0.0003 (8.77)

0.0085 (0.76) 0.0045 (0.71)

0.00003 (1.24) 0.0001 (2.01)

Included 0.0938 5.98 (0.00) 819

Included 0.2743 18.18 (0.00) 819

Included 0.1170 2.22 (0.00) 158

Included 0.1920 3.04 (0.00) 158

Included 0.1087 1.76 (0.04) 107

Included 0.0980 1.82 (0.04) 107

Nextt Sizet Profitt Debtt Volatilityt Growtht Investmentt SEOCapitalt SEOCapitalt  Overvalue Overvaluet Industry Ad.R-squared F-statistic (p-value) Obs.

Included 0.1709 10.91 (0.00) 819

Included 0.1368 8.47 (0.00) 802

Included 0.0619 4.78 (0.00) 802

Included 0.1662 2.96 (0.00) 159

1154 Table 9 Motivation of SEOs over time. The dependent variable is SEO proceeds/total assets in year t. The independent variables are the same as those in Table 5. The figures in parentheses are the t-statistics, computed using the white (1980) heteroskedasticity consistent standard error. 1994–1997

Constant

Nextt

Sizet1 Profitt1 Debtt1 Volatilityt1 Growtht1 Investmentt1

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

0.6787 (2.23) 0.0107 (0.81) 0.0215 (0.89) 0.0257 (1.73) 0.6825 (1.70) 0.0046 (0.03) 0.4617 (0.15) 0.0010 (0.03) 0.3620 (2.57)

1.7570 (2.35) 0.0012 (0.04) 0.0462 (0.95) 0.0790 (2.32) 1.4298 (1.79) 0.2449 (0.15) 8.3345 (1.40)

1.6716 (2.34) 0.0024 (0.09) 0.0462 (0.96) 0.0462 (2.16) 1.3048 (1.55) 0.1167 (0.46) 8.1590 (1.37)

0.6424 (2.21) 0.0086 (0.86) 0.0182 (0.98) 0.0244 (1.71) 0.7469 (2.17) 0.0362 (0.35) 1.8389 (0.79)

1.7143 (2.43) 0.0002 (0.01) 0.0447 (0.93) 0.0800 (2.34) 1.4337 (1.80) 0.0996 (0.40) 8.4305 (1.41)

1.0376 (8.42) 0.0094 (3.04) 0.0238 (1.96) 0.0412 (7.33) 0.6807 (5.02) 0.0235 (0.59) 4.3769 (3.05) 0.0075 (0.67) 0.0070 (0.14)

1.0439 (7.60) 0.0060 (1.69) 0.0329 (2.48) 0.0404 (6.19) 0.6865 (4.64) 0.0806 (0.33) 5.2880 (3.28)

0.8756 (6.17) 0.0030 (0.89) 0.0331 (2.56) 0.0307 (4.58) 0.3306 (1.93) 0.0931 (2.00) 4.4450 (2.79)

1.0238 (7.79) 0.0064 (2.00) 0.0401 (3.20) 0.0413 (6.86) 0.7219 (5.09) 0.0175 (0.41) 5.2346 (3.45)

1.0844 (7.75) 0.0049 (1.46) 0.0307 (2.34) 0.0408 (6.51) 0.7099 (4.80) 0.0261 (0.59) 5.3948 (3.39)

1.1617 (1.74) 0.0024 (0.31) 0.1607 (1.50) 0.0727 (4.64) 0.8765 (1.19) 1.9145 (1.29) 6.5257 (1.38)

1.9034 (5.20) 0.0036 (0.47) 0.1604 (1.48) 0.0753 (4.81) 0.4577 (0.60) 0.0487 (0.33) 5.1994 (1.08)

1.9425 (5.33) 0.0017 (0.24) 0.1863 (1.71) 0.0774 (4.97) 0.6602 (0.92) 0.0794 (0.54) 5.5724 (1.22)

1.9042 (5.20) 0.0016 (0.22) 0.1782 (1.62) 0.0776 (4.90) 0.5689 (0.78) 0.0461 (0.31) 5.2693 (1.10)

0.0632 (1.44)

0.9130 (7.55) 0.0069 (2.21) 0.0307 (2.68) 0.0319 (5.38) 0.4225 (2.89) 0.0958 (0.46) 3.4694 (2.58) 0.0002 (0.02) 0.0164 (0.37) 0.0276 (0.13) 0.0106 (4.44) 0.1628 (0.86) 0.0827 (2.26)

1.9560 (5.26) 0.0046 (0.61) 0.1104 (1.01) 0.0793 (4.94) 0.1500 (0.20) 0.1130 (0.75) 6.3189 (1.33) 0.0070 (0.22) 0.3281 (1.71)

0.0464 (0.26)

0.3494 (0.95) 0.0138 (0.94) 0.0214 (0.82) 0.0180 (1.07) 0.4159 (0.88) 0.6877 (1.06) 0.3652 (0.11) 0.0009 (0.02) 0.3630 (2.45) 0.7203 (1.13) 0.0041 (0.36) 0.3665 (0.65) 0.0604 (0.70)

0.0753 (0.61)

1.5405 (2.01) 0.0069 (0.82) 0.1414 (1.19) 0.0776 (4.53) 0.3850 (0.42) 1.0935 (0.60) 6.4159 (1.26) 0.0067 (0.17) 0.2538 (1.14) 1.1378 (0.65) 0.0059 (0.63) 0.8187 (0.87) 0.0071 (0.05)

Tradeofft1

0.1369 (0.09)

Overvaluet1

0.0085 (0.46)

EAEt1

Industry Ad.R-squared F-statistic (p-value) Obs.

2006–2008

0.3535 (0.89)

0.0534 (0.22) 0.0113 (4.01) 0.1813 (0.82)

1.8979 (1.34) 0.0053 (0.68) 0.8948 (1.06)

Included 0.0888 1.86

Included 0.0534 1.82

Included 0.0540 1.83

Included 0.0931 2.24

Included 0.0537 1.82

Included 0.0762 1.65

Included 0.1806 10.12

Included 0.1375 8.49

Included 0.1572 9.76

Included 0.1540 9.45

Included 0.1402 8.65

Included 0.2306 10.78

Included 0.3727 3.69

Included 0.3787 3.80

Included 0.3636 3.62

Included 0.3713 3.71

Included 0.3625 3.61

Included 0.3428 2.77

(0.04) 108

(0.03) 205

(0.03) 205

(0.00) 171

(0.03) 205

(0.08) 103

(0.00) 663

(0.00) 706

(0.00) 706

(0.00) 697

(0.00) 706

(0.00) 654

(0.00) 69

(0.00) 70

(0.00) 70

(0.00) 70

(0.00) 70

(0.00) 69

H. Bo et al. / Journal of Banking & Finance 35 (2011) 1143–1157

Gapt1

1998–2005

Table 10 Further evidence on market timing over time The dependent variable varies across estimations. The independent variables are the same as those in Table 6. The figures in parentheses are the t-statistics, computed using the white (1980) heteroskedasticity consistent standard error. 1994–1997

Constant

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

(1) Returnat

(2) Investmentt

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

(1) Returnat

(2) Investmentt

(3) Returnat

(4) Returnt+3

(5) Returnatþ3

0.0065 (2.61) 0.0006 (0.78)

0.1639 (0.94) 0.0076 (1.30)

0.0070 (3.09) 0.00005 (0.75)

0.0099 (7.84) 0.00001 (0.25) 0.0002 (2.41) 0.0004 (6.83) 0.0027 (2.24) 0.0009 (2.09) 0.0050 (0.43) 0.00002 (0.05) 0.0007 (1.37) 0.00001 (0.09)

0.0080 (6.28) 0.00003 (0.76) 0.0002 (2.32) 0.0004 (6.04) 0.0027 (2.25) 0.0008 (1.94) 0.0122 (1.03) 0.00006 (0.11) 0.0007 (1.27) 0.00003 (0.22)

0.0028 (2.37) 0.00006 (2.46)

0.1576 (1.26) 0.0054 (2.10)

0.0015 (1.24) 0.00001 (0.58)

0.00001 (0.01) 0.0001 (4.15) 0.0005 (4.95) 0.00002 (0.32) 0.0023 (1.63) 0.0003 (1.00) 0.0556 (4.81) 0.00009 (1.69) 0.0006 (1.64) 0.0009 (3.24)

0.0006 (0.89) 0.00005 (3.00) 0.0003 (3.92) 0.00004 (1.10) 0.0002 (0.18) 0.00009 (0.38) 0.0350 (4.55) 0.00009 (2.41) 0.00004 (0.16) 0.0005 (2.73)

0.0020 (0.35) 0.00007 (0.87)

0.9835 (3.35) 0.0089 (1.94)

0.0068 (0.49) 0.0001 (0.75)

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

na

Nextt Sizet Profitt Debtt Volatilityt Growtht Investmentt SEOCapitalt

0.0002 (2.09) 0.0160 (6.98) 0.0017 (2.10) 0.0450 (1.97) 0.0001 (1.30) 0.0014 (1.37) 0.0003 (1.33)

0.0009 (0.05) 0.0232

0.0003 (2.95) 0.0089 (3.77) 0.0011 (1.43) 0.0431 (2.08) 0.00004 (0.44) 0.0014 (1.55) 0.0001 (0.49) 0.0005

(1.46) 0.0043 (0.64)

(2.62) 0.0005 (5.75)

Included 0.2551 5.65

Included 0.0944 2.32

Included 0.3840 8.48

Included 0.2841 6.06

Included 0.2470 5.18

(0.00) 205

(0.00) 205

(0.00) 205

(0.00) 205

(0.00) 205

SEOCapitalt  Overvalue Overvaluet Industry Ad.R-squared F-statistic (p-value) Obs.

2006–2008

(2) Investmentt

0.0046 (0.54) 0.0244 (0.13) 0.0101 (0.17) 0.7710 (0.48) 0.0342 (4.63)

0.0643 (1.97) 0.0267

0.00006 (1.08) 0.0051 (3.51) 0.0006 (1.78) 0.0697 (6.03) 0.0002 (5.11) 0.0013 (3.59) 0.0011 (3.51) 0.0002

(3.23) 0.0037 (1.10)

(2.79) 0.0003 (8.72)

Included 0.1972 11.82

Included 0.0995 5.58

Included 0.2918 17.11

Included 0.1098 6.10

Included 0.0816 4.67

(0.00) 706

(0.00) 705

(0.00) 705

(0.00) 706

(0.00) 704

0.0002 (3.22) 0.0094 (6.43) 0.0015 (4.05) 0.0744 (6.06) 0.0003 (5.18) 0.0012 (3.31) 0.0011 (3.78)

0.0092 (1.58) 0.5868 (3.95) 0.1566 (3.96) 0.8871 (0.74) 0.0133 (2.30)

0.0996 (1.17) 0.0042

0.0002 (0.32) 0.0336 (1.43) 0.0004 (0.07) 0.1849 (1.20) 0.0034 (3.01) 0.0062 (1.04) 0.0082 (2.52) 0.0012

(0.14) 0.0024 (0.26)

(1.67) 0.0001 (0.41)

na

na

Included 0.1008 1.74

Included 0.1523 2.18

Included 0.2430 2.41

na na na

na na na

(0.09) 67

(0.03) 67

(0.01) 67

na

na

0.0001 (0.55) 0.0126 (1.43) 0.0010 (0.58) 0.0093 (0.14) 0.0010 (1.54) 0.0024 (1.06) 0.0027 (1.80)

0.0462 (3.71) 0.4134 (0.84) 0.0372 (0.36) 2.8816 (0.88) 0.0114 (0.31)

H. Bo et al. / Journal of Banking & Finance 35 (2011) 1143–1157

Gapt

1998–2005

(1) Returnat

1155

1156

H. Bo et al. / Journal of Banking & Finance 35 (2011) 1143–1157

Directors was introduced around 2001. Hence it can be seen that the listed firms at this stage had formal corporate governance system in place. We only had a few SEOs cases in 2005 when the ownership split reform was initiated in April 2005. Since these SEOs cases in 2005 took place before April 2005, we include 2005 into the second subsample period. Finally, the third subsample period (2006–2008) may reflect some effects of the ownership split reform. Non-tradable shares in theory were merged with tradable shares in this period, but in practice there are still a significant amount of non-tradable shares in Chinese listed firms during this period. In sum, given these characteristics regarding China’s market transition, the listed firms should respond to internal and external environment differently regarding their SEOs decisions. Again we repeat the estimations in Tables 5 and 6 for the three subsample periods, respectively. The results are displayed in Tables 9 and 10.11 It is interesting to observe from Tables 9 and 10 that there are indeed differences in SEOs behavior across different subsample periods. In the first subsample period (1994–1997), the estimated coefficient for financing for investment (Investment) is positive and significant in both columns (1) and (6) of Table 9. In the meantime, the estimated equation shown in column (3) in Table 10 for the period of 1994–1997 provides a piece of evidence confirming the market timing theory. Putting together, the results in Tables 9 and 10 suggest that the sample firms during 1994–1997 were possibly motivated by both the market timing and financing for investment when they issue SEOs, although it seems that financing for investment dominates. As we have already mentioned that at the beginning of establishing the Chinese stock market, the government had hold a tight control of the listing process. Consequently, the firms selected to be listed were of good quality in general and had great growth potential. This may explain the result that in addition to the market timing explanation, SEOs during this period were also motivated by financing for investment and growth. This result is also consistent with the Chinese practice in that Chinese stock markets were initially designed and used mainly for providing the loss-making state-owned enterprises with a fresh channel to raise external capital. In contrast, the results in both Tables 9 and 10 strongly support that market timing dominates other theories for SEOs activities during 1998–2005. The estimated coefficient for overvaluation (Overvalue) is highly positively significant in both columns (3) and (6) in Table 9 for the period of 1998–2005. In addition, all the estimated equations in Table 10 for this period confirm the market timing theory (see columns (1)–(5) for the period of 1998–2005). This result is also consistent with the practice of Chinese stock markets. As we mentioned earlier, in the subsample period 1998–2005, the number of listed firm had grown very fast and this was the period of the majority of SEOs activities took place. The firms quickly learned from the market practice, timing the market became a dominating motivation for SEOs in this period. Finally, in the subsample period after the ownership split reform (2006–2008), the result is a mixture of both financing for investment and market timing theories. Column (1) in Table 9 for the period of 2006–2008 shows that financing for investment is important for SEOs in this period, while columns (1) and (3) of Table 10 for the period of 2006–2008 provide some evidence supporting the market timing theory in this period. Two reasons may help explain the SEOs behavior in the post-2005 period in China: firstly, the ownership split reform has brought about improved corporate governance, hence financing for investment became 11 Because our sample period ends in 2008, there are no observations for 3 year stock returns after the current SEOs. Hence it is impossible to obtain results for long term stock returns estimations in columns (4) and (5) in Table 10 for the period of 2006–2008.

important in addition to timing the market; and secondly, public offerings dominated in this period (see Table 1), and thus, firms have less incentives to time the market (see Section 5.2). 6. Conclusions Our understanding on the seasoned equity offerings (SEOs) behavior is mainly based on firms in mature market economies, little is known about Chinese firms in this respect. In this paper we use Chinese SEOs during 1994–2008 to examine the empirical relevance of standard theories explaining the motivation of SEOs. After controlling for other factors that reflect features of Chinese corporate financing, we find that Chinese listed firms are mostly motivated by market timing to issue SEOs. Financing for investment and growth has received weak empirical support. However, the tradeoff theory is not supported by our data. Moreover, we do not obtain any evidence supporting the notion that controlling shareholders conduct SEOs to exploit minority shareholders, which has been claimed in the previous literature on Chinese SEOs. We provide evidence that timing the market is the most relevant explanation of SEOs behavior in China. In addition, we also find that Chinese firms’ SEOs behavior varies between rights issues and public offerings and across different periods along with the progress of China’s market transition. In sum, our result shows that Chinese listed firms in general behave similarly as their counterparts in other countries concerning SEOs decisions in that they issue SEOs when there are opportunities to take advantages of market overvaluation. This result is consistent with the welldocumented convergence trend of corporate SEOs behavior of firms across different countries (e.g., Kim and Weisbach, 2008; Henderson et al., 2006). In addition, our result challenges the conventional perception on Chinese SEOs that Chinese listed firms are mainly motivated by the expropriation of minority shareholders by controlling shareholders when they issue SEOs. Acknowledgements The authors thank Tao Li for providing us with part of the data used in the paper, and Valen Sun for research assistance. Changyun Wang gratefully acknowledges the financial support from the National Science Fund for Distinguished Young Scholars (No.: 70725003). We thank anonymous referees for helpful comments and suggestions on an earlier version of the paper. References Aharony, J., Lee, C.J., Wong, T.J., 2000. Financial packaging of IPO firms in China. Journal of Accounting Research 38, 103–126. Autore, D.M., Kovacs, T., 2010. Equity issues and temporal variation in information asymmetry. Journal of Banking and Finance 34, 12–23. Bai, C., Liu, Q., Lu, J., Song, F.M., Zhang, J., 2004. Corporate governance and market valuation in China. Journal of Comparative Economics 32, 599–616. Baker, M., Wurgler, J., 2002. Market timing and capital structure. Journal of Finance 57, 1–32. Baker, M., Stein, J.C., Wurgler, J., 2003. When does the market matter? Stock prices and the investment of equity-dependent firms. Quarterly Journal of Economics 118, 969–1006. Bennedsen, M., Nielsen, K.M., 2010. Incentive and entrenchment effects in European ownership. Journal of Banking and Finance 34, 2212–2229. Berkman, H., Cole, R.A., Fu, L.J., 2009. Expropriation through loan guarantees to related parties: Evidence from China. Journal of Banking and Finance 33, 141– 156. Bo, H., Sterken, E., 2002. Volatility of the interest rate, debt and firm investment: Dutch evidence. Journal of Corporate Finance 8 (2), 179–193. Chan, K., Wang, J., Wei, K.C., 2004. Underpricing and long-term performance of IPOs in China. Journal of Corporate Finance 10, 409–430. Chen, X., Lee, C., Li, J., 2003. Chinese Tango: Government Assisted Earnings Management. Working Paper, Tulane University, A.B. Freeman School of Business. Claessens, S., Djankov, S., Lang, L.H.P., 2000. The separation of ownership and control in East Asian corporations. Journal of Financial Economics 58 (1–2), 81– 112.

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