Financial Reporting Quality and Noise Trading

Transcription

1 Financial Reporting Quality and Noise Trading: Evidence from Chinese A-B Twin Shares Tao Ma * Current Version: September 2015 Abstract We examine whether earnings quality can reduce noise trading by examining price movements of A-B twin shares traded in the Chinese stock markets. In the absence of noise trading, A- and B- share prices should move in synchronicity in response to innovations in fundamentals. However, the existence of noise trading will cause A-B share prices to move in opposite directions, resulting in A-B share non-synchronicity. We find that firms with high earnings quality are associated with low price non-synchronicity and this negative association is significantly weakened after an exogenous decrease in earnings quality arising from China s adoption of new accounting standards in We also find that price non-synchronicity is significantly lowered on earnings announcement dates and our results cannot be explained by non-synchronous trading of A-B shares. By employing the unique twin-share setting, this paper circumvents some empirical design weakness in prior studies and provides unambiguous evidence that earnings quality can reduce noise trading and improve share price informativeness. JEL Classification: M41; G14; G15 Key Words: Earnings Quality, Noise Trading, Information Efficiency, Chinese Stock Market * The author thanks Charles Boster, Bob Lipe, D. H. Zhang, and Guochang Zhang for very helpful comments. All remaining errors are my own. Contact: Moore School of Business, University of South Carolina, 1014 Greene Street Columbia, SC tao.ma@moore.sc.edu; Tel:

2 1. Introduction Stock prices play an important role in disseminating information to capital markets. 1 Speculative trading by traders with information about firm fundamentals moves stock prices and helps impound fundamental information into stock prices (see e.g., Grossman and Stigliz 1980; Glosten and Milgrom 1985; Kyle 1985). However, the existence of noise traders, i.e., investors who do not have access to inside information and act on noise (see e.g., Kyle 1985; DeLong et al. 1990; Mendel and Shleifer 2012), can cause stock prices to deviate from firm fundamental values, reducing price informativeness (Keynes 1936; DeLong et al. 1990; Stein 1996; Barberis et al. 1998; Kogan et al. 2006; Mendel and Shleifer 2012). Alternatively, the existence of noise trading causes less informative stock price system, which obscures the efficient transmission of fundamental information and increases likelihood of deviations of stock prices from firm fundamentals. Although theories predict that improved financial disclosures can reduce noise trading (e.g., Diamond and Verrecchia 1991; Easley and O Hara 2004; Lambert et al. 2012), empirical evidence supporting this argument is non-existent due to the difficulty of observing noise trading empirically. Public disclosures can make more investors to become informed (Diamond 1985; Diamond and Verrecchia 1991), help generate new information (Kim and Verrecchia 1994, 1997), and increase information precision or reduce information uncertainty (Easley and O Hara 2004; Lambert et al. 2012), all of which can lead to reduced information friction associated with noise traders. According to Grossman and Stigliz (1980), as more investors become informed and trade on fundamental values, their demands and therefore stock prices will vary closely with firm fundamental values and less with noise. 2 Existing accounting literature provides some empirical 1 For example, changes in stock prices convey meaningful signals to capital markets, help guide managers to make efficient capital investments, and allow for timely intervention in cases of poor management (Gilchrist et al. 2005; Durnev et al. 2004; Chen et al. 2007). 2 As a stock price becomes more informative, it can further reduce the likelihood of noise trading. 1

3 insights on the association between earnings quality and various measures of price informativeness (e.g., Kim and Shi 2012; Callen et al. 2013); however, none of the studies directly test whether earnings quality can reduce noise trading. Importantly, as discussed later, the inability to isolate price movements driven by noise traders from those driven by fundamental values makes such empirical associations difficult to interpret. In this study, we explore the price movements of A-B twin shares traded in the Chinese stock markets and shed light on whether accounting quality can reduce noise trading. Although a majority of Chinese firms issue only A-shares targeted at domestic investors, a subset of firms are also allowed to issue B-shares that are traded alongside A-shares on the same stock exchange. Before 2001, B-shares were traded only by foreign investors, but the restriction has been lifted since then, and domestic investors with foreign currency savings accounts are allowed to hold and trade B-shares. The two classes of shares (A-B twin shares) have exactly the same voting rights and cash flow rights. One interesting feature about the twin shares is that A-B share prices do not always move in the same direction. Figure 1 graphs the fractions of trading days that A- and B-shares have opposite signs in returns (unsynchronized returns) in a given year. Both market-adjusted and unadjusted A-B returns exhibit a high frequency of unsynchronized movements, although the non-synchronicity has been decreasing as the Chinese economy has developed. For example, for unadjusted A-B returns, the fraction of unsynchronized returns decreases from 37% in 1999 to around 20% in In contrast, untabulated results show A- and B-shares move together with their own market returns on more than 70% of trading days. As a matter of fact, prior to 2003, both shares comove more with their own market than with their own twin shares. [Insert Figure 1 about here] 2

4 This high frequency of unsynchronized movement is surprising given that both shares are traded based on the same underlying assets, which suggests that A- and B-share prices should move in lockstep in response to innovations in firm fundamentals. Morck et al. (2000) and Albagli et al. (2013) also document similar price movements in the Chinese stock markets. Both papers argue that lack of firm-specific information (high quality financial reporting) causes prices of firms listed on Chinese stock exchanges to be vulnerable to large systematic price swings arising from noise trading. As A- and B-share noise traders may hold different beliefs, A- and B-share prices will likely move in different directions, resulting in unsynchronized movements in their respective share prices. Further, A- and B-share noise trading obscures efficient transmission of information across A- and B-share markets, increasing the likelihood of two share prices to diverge. We use A-B share return non-synchronicity to calibrate the extent of price movements driven by noise traders. We argue that disclosures of public information, which increase firmspecific information, will reduce noise traders. We measure A- and B-share return nonsynchronicity as the percentage of trading days that A- and B-share daily market-adjusted returns have opposite signs. We adjust A- and B-share daily returns by their equally weighted market returns in order to gauge divergences about firm-specific information rather than on market-wide information. Alternatively, we also use the simple Pearson s correlations of A- and B-share market-adjusted returns to measure non-synchronicity. Lastly, building on Morck et al (2000), we rely on the comovement between A- and B-share unadjusted returns with market returns to infer noise trading with high comovement with market returns indicating high likelihood of noise trading. Following previous studies examining the informational role of financial reporting in the international stock markets (Bhattacharya et al. 2003; Leuz et al. 2003; Leuz et al. 2009), we use 3

5 four measures of earnings quality: earnings aggressiveness, avoiding earnings losses, and two different types of earnings smoothness. Detailed definitions of the four individual earnings quality measures are discussed in Section 4.1. Consistent with the literature, our empirical analyses rely on a composite measure of earnings quality based on the four individual earnings quality metrics with higher values indicating high earnings quality. Using a sample of 1,157 firm-year observations during the 15-year period from 1999 to 2013, we find a significantly negative relationship between our composite measure of earnings quality and A- and B-share non-synchronicity, which is consistent with the hypothesis that financial reporting can reduce noise trading and increase A- and B-share price informativeness. We next use the adoption of the IFRS-based new accounting standards in 2007 as an exogenous change in accounting quality. Consistent with previous studies findings that firms experienced declines in accounting quality after the adoption of the new standards (He et al. 2012; DeFond et al. 2014; Hou et al. 2015), we find that after 2007 there is an increase in the measure of A-B share non-synchronicity, and the negative effect of earnings quality on non-synchronicity is significantly reduced. Further, using earnings announcements as an alternative proxy for public information disclosures, we find a significant decrease in A-B share non-synchronicity on announcement dates, suggesting that the measure of non-synchronicity is unlikely to be driven by some unobservable firm characteristics that we fail to control. Our empirical results remain robust when we use Pearson s correlations of A- and B-share market-adjusted returns and comovement with market returns to infer noise trading. One valid concern is that the measure of A-B share non-synchronicity may be driven by unsynchronized trading between A- and B-shares. 3 In particular, because B-shares are less liquid 3 To address the issue of low trading frequency, we delete trading days with zero returns and firm-years with fewer than 20 trading days for both A- and B-shares. 4

6 than A-shares (e.g., less frequently traded), information will likely be incorporated into A-share prices in a more timely fashion than B-shares even if the same information is observed by both markets at the same time. More importantly, if earnings quality can increase A- and B-share liquidity and therefore reduce A-B share non-synchronous trading, any negative association between earnings quality and return non-synchronicity will be capturing the liquidity effect of earnings quality rather than reducing noise trading. To rule out this potential confounding effect, as discussed later, we take several cautious steps in constructing our sample as well as model specifications. 4 In addition, we also use five-day and ten-day cumulative returns as alternative measures of non-synchronicity. As the measurement window increases, we expect the effect of unsynchronized trading to become less problematic as it is unlikely that B-share prices need more than five to ten days to incorporate staled information that is timely impounded in A-share prices. Expanding the measurement window to five and ten days does not change our empirical findings. Furthermore, we also examine changes of A- and B-share liquidity around earnings announcement dates and fail to find evidence supporting this alternative explanation. Our paper is related to Callen et al. (2013) and Kim and Shi (2012). Both studies rely on the statistical association between market returns and individual stock returns to infer price informativeness. Callen et al. (2013) examine the relationship between earnings quality and the timeliness of stock prices in incorporating lagged market information. The authors find that as earnings quality increases, the power of lagged market returns in explaining current period stock returns decreases, suggesting that lagged market information is impounded into stock prices in a timely manner. Kim and Shi (2012) instead focus on the relationship between individual stock returns and concurrent market returns. They find that market returns have weak explanatory power 4 Detailed discussions on the various steps we take are provided in Section

7 for individual stock returns for firms voluntarily adopting IFRS prior to The authors argue that voluntary adoption of IFRS facilitates the incorporation of firm-specific information into stock prices and increases stock price informativeness. Although stock price movements can convey firm fundamentals, they can also be driven by noise trading, which will signal deviations of stock prices from fundamental values (see e.g., Keynes 1936; Stein 1996). As acknowledged by Roll (1988) and argued by many others (e.g., West 1988; Hou et al. 2013), the inability to control for noise traders makes it problematic to infer price informativeness based on the association between market returns and individual firm returns. For example, if investor sentiments drive individual firm stock return variance, a low association with market returns may capture stock price inefficiency rather than efficiency. Likewise, a high association in the presence of a market-wide price frenzy will be an indication of low price inefficiency. In this study, the A-B twin share structure allows us to almost perfectly isolate price movements driven by noise traders from those driven by fundamentals and to examine whether noise trading decreases as earnings quality increases. Further, we also exploit an exogenous change in accounting quality generated by the adoption of new accounting standards, which allows us to better infer the causal relationship between earnings quality and price informativeness. In addition, our paper also extends the existing studies by documenting that earnings announcements decrease noise trading and improve price information efficiency. The rest of the paper is organized as follows. Section 2 provides background information on China s stock markets and mandatory financial reporting; Section 3 lays out the development of the hypothesis and Section 4 describes the empirical model setup. Section 5 presents the main empirical results followed by robustness tests in Section 6; Section 7 concludes the paper. 6

8 2. Structure of Chinese Stock Markets and Financial Reporting China s two stock exchanges in Shanghai and Shenzhen were established in the early 1990s in an effort to reform state-owned enterprises and attract foreign investment. The two stock markets have grown rapidly since their inception and by the end of 2013, the number of firms listed on the two exchanges had reached 2,489, with a total market capitalization over 3.8 trillion U.S. dollars. While the majority of firms issue only A-shares that are purchased by individual Chinese investors, a unique feature of the Chinese market is that some companies are also allowed to issue B-shares with identical voting and cash flow rights as A-shares. Class A-shares are denominated in Chinese Renminbi (RMB) and class B-shares are denominated in either U.S. dollars (for B-shares traded on the Shanghai Stock Exchange) or Hong Kong dollars (for B-shares traded on the Shenzhen Stock Exchange). The issuance of B-shares was meant to attract foreign capital without losing government control over the foreign currencies exchange and minimize the exposure of Chinese financial institutions to foreign exchange risk. While A-shares are restricted to domestic investors during this paper s sample period, B-shares could be traded only by foreign investors before On February 28, 2001, the Chinese Securities and Exchange Commission (CSRC) lifted the restriction and allowed domestic individual investors in China to invest in the B-shares market, but not vice versa. Another feature of the A-B twin shares is that before the B-share reform in 2001, B-shares were priced well below A-shares. The B-share discounts could not be arbitraged away because investor short-selling was prohibited by Chinese law. Further, government rules also constrained firms from undertaking equity issuances or share buy-backs, mechanisms firms can use to correct share mispricing (Chan et al. 2008; Tang 2011; Ma and Zhang 2014). However, lifting the restrictions on domestic investors holdings of B-shares significantly decreased although did not 7

9 completely eliminate the B-share discount and, in some cases, B-shares are even traded at a premium to A-shares. B-shares are all public shares, while A-shares have three different categories: public shares, state shares, and legal shares. Both public A- and B-shares are freely tradable on the exchanges and are open to individual investors. State shares are held by the government and legal shares are held by state-owned enterprises or other economic entities. State and legal A-shares are illiquid and are traded off the exchanges. Although state and legal shares are not freely tradable, the owners nevertheless enjoy the same voting rights and same claims to dividends. Starting in April 2005, the CSRC carried out ownership restructure reform and required gradual conversion of some nontradable state or legal shares to freely tradable public shares. By the end of 2007, almost all publicly listed firms had successfully completed their ownership structures. Before 2007, firms issuing only A-shares needed to prepare their financial reports following Chinese generally accepted accounting standards (CGAAP). In addition to preparing CGAAP-based financial reports, firms issuing both A- and B-shares are also required to issue financial reports following International Accounting Standards (IAS) or International Financial Reporting Standards (IFRS). Public firm disclosure rules prepared by the CSRC explicitly stated that when the disclosure requirements for any specific events were different between Chinese GAAP and IAS, firms should follow the stricter disclosure requirements for both financial reports. Hence, except for the language used and certain different accounting item numbers, the contents and formats of the two sets of financial reports were identical. 5 In February 2006, the Ministry of Finance issued a new set of Accounting Standards for Business Enterprises (ASBEs). The new standards substantially converge with IFRS and became mandated for all public firms on January 5 We compared several firms financial reports prepared under the Chinese GAAP and IAS and found that the information content disclosed in the two sets of financial reports was identical. 8

10 1, Under the new accounting rules, firms issuing A- and B-shares only need to prepare one set financial reports following the ASBEs. 3. Hypothesis Development Following prior studies (e.g., Kyle 1985; Black 1986; DeLong et al. 1990; Mendel and Shleifer 2012), noise traders are investors who have no access to inside information and trade on noise. Theoretical studies have shown that noise traders can affect stock prices and cause those prices to diverge significantly from fundamental values (e.g., DeLong et al. 1990; Barberis et al. 1998; Kogan et al. 2006; Mendel and Shleifer 2012). For example, DeLong et al. (1990) argue that due to the difficulty of predicting noise traders beliefs, rational arbitrageurs face significant risk betting against noise traders. Mendell and Shleifer (2012) show that noise traders can have a disproportionally large impact on market prices because other investors may chase noise as if it were information. Theoretical studies all argue that public disclosures of information can reduce noise traders. For example, one line of theory argues that public disclosures can make private information publicly available to investors, through which more investors will become informed (Diamond 1985; Diamond and Verrecchia 1991). Kim and Verrecchia (1997) show that public disclosures of information can stimulate investors with heterogeneous beliefs to generate more new information. In addition, disclosures of financial information can also increase information precision and reduce information uncertainty about a firm s future cash flows (Verrecchia 1980; Easley and O Hara 2004; Lambert et al. 2012). Hence, public disclosures provide important firm-specific information to capital markets and reduce noise traders as more investors likely become informed with more precise information and lower information uncertainty. As such, investors are likely to trade on fundamentals and their demands for shares and subsequently share price movements will vary 9

11 more with fundamentals (Grossman and Stiglitz 1980). In contrast, low quality financial reporting is associated with less firm-specific information, i.e., less-informed investors, and low information precision, which increases the likelihood of noise traders and deviations of stock prices from fundamentals. For the setting of the A-B twin share structure, A- and B-shares are two market segments with different compositions of investors. Firms with low earnings quality are more likely to experience supply shocks originated by noise traders from either A- or B-shares. If noise traders from A- and B-shares hold different beliefs about firm values, their different beliefs will increase the likelihood of A-B share price divergence. While high-quality financial reporting can reduce the likelihood of noise traders and subsequently shocks from noise traders for A- and B-shares, low-quality financial reporting can induce divergent beliefs arising from noise traders. In addition, increased noise trading due to low earnings quality is associated with an opaque price system and prohibits timely transmission of information across A- and B-share markets, further exacerbating the divergence in A- and B-share price movements. This discussion leads to our main research hypothesis: Hypothesis: Earnings quality is negatively correlated with the divergence of A- and B- share price movements. 4. Empirical Method 4.1. Measures of Earnings Quality Following the literature that investigates accounting quality s role in international capital markets, we use earnings aggressiveness, loss avoidance, and two measures of earnings smoothness to gauge earnings quality (e.g., Bhattacharya et al. 2003; Leuz et al. 2003; Leuz et al. 2009). The first earnings quality is the signed magnitude of accruals to measure the extent of 10

12 discretions or aggressiveness exercised by managers in financial reporting. Because managers have stronger incentives to overstate than to understate reported earnings, excessive discretion toward aggressively overstated earnings increases noise to reported earnings and decreases earnings quality. Watts and Zimmerman (1986), among others, argue that managers in the United States can exercise discretion in accruals to increase informativeness of financial reporting; however, as stated in Leuz et al. (2003) and Bhattacharya et al. (2003), this does not apply to countries with weak investor protection such as China. In particular, unique Chinese regulations exacerbate the incentive to overstate earnings. For instance, firms must maintain returns on equity of at least 10% in the previous three years in order to be qualified to issue additional shares to existing shareholders (Chen and Yuang 2004). Likewise, firms will lose their public listing status if they incur negative net profits in three consecutive years (Liu and Lu 2007). Therefore, Chinese firms have strong incentives to exercise discretion in accruals to overstate their earnings. Following previous studies, we measure accrual discretion as the ratio of total accruals over lagged total assets with higher values indicating more aggressive accounting reporting. Our second measure of earnings quality is the likelihood of avoiding small negative earnings. Following Bhattacharya et al. (2003) and Leuz et al. (2003), firms are identified as having small positive earnings if the ratio of net income over lagged total assets is between zero and The underlying assumption is that firms manipulate earnings to avoid small losses and report small positive earnings instead. Our third and fourth measures of earnings quality are of earnings smoothness. Firms with strong incentives to report positive earnings will be averse to any swings in earnings, and therefore are more likely to smooth out earnings. For example, firms may purposefully report lower earnings in good performance years and use the reserve in future years when earnings performances are 11

13 poor. Alternatively, they can also recognize past losses in years when they have a great performance. Hence, artificially smoothed earnings obscure the true performance and decrease the quality of reported earnings (Bhattacharya et al. 2003; Leuz et al. 2003). Following prior studies, our first measure of earnings smoothness is the ratio of the standard deviation of earnings from operations over the standard deviation of cash from operations, and our second measure of smoothness is the correlation between the change in total accruals and the change in cash from operations. All of the variables are scaled by total assets. Cash flow from operations is calculated as the difference between net income and total accruals. 6 To calculate the standard deviation and correlation, we require firms to have at least three years but no more than five years of valid observations. Lower standard deviation ratios will be consistent with the idea that firms use accruals to smooth out volatility in cash flows, resulting in low earnings quality. The correlation between changes in cash from operations and changes in total accruals should be negative by the nature of accrual accounting. However, as pointed out by Leuz et al. (2003), it becomes more likely that firms are smoothing earnings to hide the true operating performance as the magnitude of the negative correlation increases. In this case, a higher correlation value (the magnitude of negative correlation is small) represents higher earnings quality. Consistent with previous studies, we rely on a composite measure of earnings quality based on these four individual earnings quality metrics in our empirical analyses. In constructing the composite measure, we multiply the aggressiveness measure by minus one so that a higher value represents higher earnings quality. Then, in each year, we sort the earnings quality components except for loss avoidance into decile rankings ranging from value one (lowest quality) to ten 6 This is consistent with Bhattacharya et al. (2003), Leuz et al. (2003), and Leuz et al. (2009). Total accruals is calculated as (Δ current assets Δ cash) (Δ total current liabilities Δ short-term debt) depreciation expense, where Δ is the change over the last fiscal year. 12

14 (highest quality). For the measure of loss avoidance, we assign a ranking of one if firms are identified as having small positive earnings and ten otherwise. We obtain the total of the four different rankings and then scale this sum so that it ranges from zero to one. Finally, the composite measure of earnings quality is the natural logarithm of one plus the scaled sum. Because the sample firms prepared financial reports in accordance with both CGAAP and IAS or IFRS before 2007, for the period prior to 2007, each firm has two measures of composite earnings quality corresponding to the two standards. Starting from 2007, each firm then only has one measure of composite earnings quality following the new accounting standards (ASBEs). As explained in a later section, we use A-share composite earnings quality as our main measure of earnings quality, 7 which follows the standard of CGAAP prior to 2007 and ASBEs after As a robustness check, we also use B-share composite earnings quality, which is only available prior to 2007, to ensure that our results are not sensitive to the use of different measures of earnings quality Empirical Model We analyze whether financial reporting can affect A- and B-share non-synchronicity, our proxy for noise trading. We control for factors both at the share and firm level that may affect the measure of non-synchronicity. We estimate the following linear regression model: Logit(NSynchro it ) = β 0 + β 1 LEQ it 1 + β 2 LASupplyt it + β 3 LBSupply it + β 4 LATO it + β 5 LBTO ii + β 6 LAAmihud it + β 7 LBAmihud it + β 8 LANoChg it + β 9 LBNoChg it + β 10 LAMktCap it + β 11 LBMktCap it + β 12 StateOwn it + β 13 SHExchg i + β 14 Protect i + γ j + τ t + ε it (1) where NSynchro is the A- and B-share non-synchronicity measured as the annual percentage of trading days that A- and B-shares have opposite signs in daily market-adjusted returns. We adjust 7 As shown in Section 5.2, the effect of B-share earnings quality on A-B share non-comovement is absorbed by A- share earnings quality. 13

15 the A- and B-share daily returns value by its own respective equally weighted market returns in order to capture divergent beliefs of A-B share investors on individual firm values. We perform a logistic transformation ln[(nsynchro)/(1-nsynchro)] to ensure that the dependent variable is not bounded within [0,1] in values. This logistic transformation preserves the monotonicity and at the same time provides better econometric properties. Nevertheless, our results are not sensitive to this transformation. We take several cautious steps to control for the effect of trading frequency/liquidity on the measure of non-synchronicity, which can potentially confound the association between earnings quality and non-synchronicity. This is particularly important because B-shares are less liquid than A-shares. To illustrate the issue, because B-shares are less liquid (e.g., less-frequently traded) than A-shares, information will be incorporated into A-share prices in a more timely fashion than B-shares even if the information is observed by both markets at the same time. More importantly, if high earnings quality is associated with high stock liquidity, which leads to low non-synchronous trading of A-B shares, then a negative association between earnings quality and return non-synchronicity may be capturing the effect of earnings quality in reducing share liquidity rather than reducing noise trading. To address this concern, we first delete firm-years with low trading frequency. Specifically, we delete any trading days with zero returns for both A- or B-shares; we also delete firm-years with fewer than 20 valid trading days. Second, as discussed below, we include several proxies that are commonly used to measure stock liquidity or trading frequency. Further, in the robustness test section, we conduct additional tests to show our results are robust to tests with alternative empirical specifications. Lastly, we also restrict our sample to the period in some model specifications. As noted above, prior to 2001, B-shares were restricted to foreign investors only; 14

16 as a result, B-shares were less liquid relative to A-shares. However, after 2001, domestic investors are allowed to hold B-shares and B-share liquidity increases significantly relative to A-shares. Differences in trading liquidity between A- and B-shares are significantly reduced. LEQ is the A-share composite measure of earnings quality as discussed in the previous section. Alternatively, we also use LBEQ, B-share composite earnings quality, as a robustness check. We use earnings quality measured at the beginning of the year to ensure that financial reporting is already observed by stock market participants. In addition, using lagged earnings quality also alleviates concerns that earnings quality and A-B share non-synchronicity are simultaneously determined by unobservable factors. LASupply and LBSupply are the natural logarithms of A- and B-share supply, respectively, where share supply is the daily average number of tradable shares outstanding in a year divided by one thousand. LATO and LBTO are the natural logarithms of one plus A- and B-share turnover, where share turnover is the annual average of the daily ratio of trading volume to the total tradable shares outstanding; LAAmihud and LBAmihud are the natural logarithms of A- and B-share Amihud illiquidity with Amihud illiquidity calculated as the annual average of the daily ratio of the absolute stock return to dollar volume then multiplied by one hundred thousand. LANoChg and LBNoChg are the natural logarithms of one plus the percentage of no-price-change trading days in a year for A- and B-shares as in Mei et al. (2005). 8 We use share supply, share turnover, Amihud illiquidity, and no-price-change to capture A- and B-share trading frequency or liquidity, all of which can affect the non-comovement of A- and B-share returns. Amihud illiquidity and noprice-change have been shown to be effective measures of market liquidity for both the U.S. and emerging markets by many studies (see e.g., Lesmond et al. 1999; Amihud 2002; Bekaert et al. 8 Although we delete trading days on which unadjusted returns are zero, as a control variable we include the number of zero-return days. 15

17 2007). The differential trading frequency and liquidity between A- and B-shares will cause return disparity. Hence, we expect that by controlling for A-share (B-share) liquidity or frequency, as B- share (A-share) liquidity or frequency increases, A-B non-synchronicity will decrease. We also include A- and B-share natural logarithms of annual average of daily market capitalization, LAMktCap and LBMktCap, to capture the information environment of A- and B- shares. Controlling for A-share (B-share) market capitalization, B-share (A-share) capitalization should be negatively associated with non-comovement. In addition, we also include state ownership (StateOwn), calculated as the number of shares owned by the government divided by the total number of shares outstanding, and a dummy variable SHExchg, which is set to one if the firm is listed on the Shanghai Stock Exchange and zero if listed on the Shenzhen Stock Exchange. Previous studies find that firms with high government ownership suffer from high information asymmetry (Jiang et al. 2010), which will be positively associated with the measure of nonsynchronicity. In addition, compared to firms listed on the Shenzhen Stock Exchange, firms listed on the Shanghai Stock Exchange are mostly state-owned enterprises (SOEs) which thus suffer from high information asymmetry due to weak incentives to provide transparent information to individual shareholders (Liu and Lu 2007). Hence, we expect a positive correlation between SHExchg and logit(nsynchro). As discussed in Section 2, A- and B-shares are priced differently, with A-shares traded at a premium relative to B-shares. In Equation (1), we implicitly control for the difference in A- and B-share stock prices by including both A- and B-shares share supplies and market capitalization on the right-hand side of the equation. To further rule out the possibility that price disparity may drive the non-synchronicity, we also include A-share prices, LAPric, and B-share prices, LBPric, 16

18 in some model specifications. Both variables are measured as the natural logarithm of one plus the annual average of A- or B-share daily prices. 9 We also control for property rights protection (Protect) obtained from Mako and Xu (2006). The property rights protection measure is based on the outcomes from the World Bank survey of 120 Chinese cities conducted in We match the respective survey city with each of our sample firms headquarters cities; the city-level property rights protection index is time invariant and is scaled to a range from zero to one with one indicating the highest protection rights. Morck et al. (2000) and Albagli et al. (2013) document that strong property protection rights can encourage private information acquisition and make firm prices more informative, resulting in a negative association between Protect and logit(nsynchro). Lastly, we include industry and year fixed effects. Industry is based on FTA1 classification obtained from Datastream. As shown in Figure 1, the measure NSynchro exhibits an overall decreasing trend during the sample period. Including year dummies effectively controls for this time trend. In Equation (1), we use logarithmic transformation for both the dependent variable and independent variables that measure A- and B-share characteristics. By doing so, we are able to control for the differences in A- and B-share characteristics and their effects on return nonsynchronicity. 11 We estimate this model using ordinary least square estimate (OLS) and robust standard errors are clustered at the firm level. 9 Share price differences reflect differences in A- and B-share investors required discount rates on firms future cash flows. These differential discount rates will result in differences in the magnitude of stock returns but not the direction of stock returns for A- and B-shares if investors of A- and B-shares respond rationally to the same information innovations. 10 The survey asks respondents the likelihood that their companies contractual and property rights (including enforcement) are protected. Detailed discussion on the survey and the measure of protection is provided in Mako and Xu (2006). 11 For example, simultaneously including both log values of A- and B-share supply is similar to controlling for the log of the ratio of A-share supply over B-share supply, which captures the difference in A- and B-share supply. 17

19 5. Empirical Results 5.1. Sample Construction and Descriptive Statistics Our sample comprises 84 unique companies that have both A- and B-shares listed on the Shanghai or Shenzhen Stock Exchanges, covering the period from 1995 to Observations for our empirical analyses start from 1999 because the calculation of earnings quality requires five years of time-series data. We obtain accounting and market data from the China Stock Market & Accounting Research (CSMAR) database. IAS/IFRS-based accounting data prior to 2007 are obtained from the Taiwan Economic Journal (TEJ). The final sample consists of 1,157 firm-year observations. All continuous variables are winsorized at the 1 st and 99 th percentiles. Table 1 reports the distributions of the main variables. In Panel A, we list the composite measure of earnings quality and the four individual earnings quality metrics for A-shares only. The means for the three earnings quality measures, earnings aggressiveness (Aggress) and earnings smoothness measures (Smooth_Ratio and Smooth_Corr), are , and , respectively, all of which are in line with Bhattacharya et al. (2003) and Leuz et al. (2003). On average, firms report small positive earnings (SmallPos) 16% of time; in sharp contrast, they report small losses (SmallNeg) less than 2% of time, indicating strong incentives to increase earnings upward to avoid a small loss. In Panel B, we provide descriptive statistics of A- and B-share characteristics. Although A- and B-shares are traded for the same underlying assets, they move in opposite directions on more than 30% of trading days (NSynchro). A- and B-shares differ in trading frequency (turnover) and liquidity (Amihud measure of liquidity and no-price-change) with A-shares being greater than B-shares for all measures. Both share supplies and return volatility for A- and B-shares are similar to each other, but A-shares are traded at a higher price than B-shares. In Panel C, we present the 18

20 distributions of firm characteristics. On average, the government owns 24% of firms outstanding shares, and half of the firms are listed on the Shanghai Stock Exchange. The average property rights protection index, which ranges between zero and one, is [Insert Table 1 about here] Table 2 presents the correlation matrix between A-B share non-synchronicity and various measures of earnings quality. As shown in Table 2, the variable logit(nsynchro) is negatively and significantly correlated with the composite measure of earnings quality LEQ and the correlation coefficient is Likewise, the correlations with the four individual earnings quality metrics are also in the predicted directions and are statistically significant. Specifically, A-B share nonsynchronicity decreases when firms reported earnings are less smoothed (higher values of Smooth_Ratio and Smooth_Corr represent low earnings smoothness and high earnings quality), when firms reported earnings are less aggressive, and when firms do not report small positive earnings. Hence, univariate correlations provide the first set of results that are consistent with our conjecture. [Insert Table 2 about here] 5.2. Multivariate Analyses Table 3 presents the test results from estimating Equation (1). We first estimate Equation (1) without controlling for share and firm characteristics, and results are reported in column (1). Consistent with our expectations, the coefficient on LEQ is negative and statistically significant at the 1% level (coefficient = and t-value = -3.66) as reported in column (1). After adding share- and firm-level characteristics, the coefficient on LEQ decreases in magnitude to as shown in column (2), but remains statistically significant at the 1% level with a t-value of

21 In column (3), we restrict the sample period to to ensure that our results are not driven by observations in the pre-reform period when B-shares were traded less frequently and at a discount compared to A-shares. Results in column (3) show that the coefficient on LEQ remains negative and statistically significant. In column (4), we add A- and B-share prices to control for the potential confounding effect due to the disparity in A- and B-share prices. Adding A- and B- share prices does not change the qualitative inference. In columns (5)-(8), we report results using the B-share composite earnings quality (LBEQ), which is only available prior to The sample is reduced to 535 firm years. Without controlling for firm and share characteristics, the coefficient on B-share composite earnings quality, as shown in column (5), is and is statistically significant at the 1% level. This negative coefficient is robust to the control for firm- and share-level characteristics as reported in column (6). In column (7), we include both A-share and B-share composite earnings quality. Interestingly, after controlling for A-share earnings quality, the coefficient on B-share earnings quality becomes zero but the coefficient on A-share earnings quality remains negative and statistically significant, suggesting that A-share earnings quality completely absorbs the effect of B-share earnings quality on A-B non-synchronicity. This finding is consistent with Eccher and Healy (2000) that A-share earnings have a higher association with annual stock returns than B-share earnings. Hence, in subsequent tests, we focus on A-share earnings quality only. Lastly, column (8) shows that adding A- and B-share stock prices does not change the qualitative inferences on the coefficient estimates on A-share and B-share earnings quality. [Insert Table 3 about here] For the control variables, we find that an increase in B-share supplies but not A-share supplies can lead to a significant reduction in A-B non-synchronicity. Both A- and B-share 20

22 turnovers are negatively associated with non-synchronicity. While the A-share Amihud measure of illiquidity is not significantly associated with return non-synchronicity, the B-share Amidhud illiquidity measure is positively associated with non-synchronicity. Likewise, the percentage of no-price-change trading days is positively associated with A-B non-synchronicity, and this association is significant for both A and B shares. Hence, empirical results are largely consistent with the notion that as shares become less liquid and less frequently traded, A-B share nonsynchronicity increases. Interestingly, we find that while A-share capitalization has a mixed and mostly nonsignificant association with A-B non-synchronicity, B-share capitalization is positively and significantly correlated with non-synchronicity. This positive association can be explained by the fact that in China large firms tend to be owned by the state or local government, which is associated with weak corporate governance, characterized as self-dealings and weak incentives to be transparent to outside investors (Liu and Lu 2007; Jiang et al. 2010). Hence, large firms tend to be more informationally opaque, resulting in lower return synchronicity. Consistent with this argument, we also find a positive although not significant coefficient on the variable of state ownership. The coefficient on state ownership is not significant due to its high correlation with firm capitalization. Firms listed on the Shanghai Stock Exchange experience higher unsynchronized returns relative to firms listed on the Shenzhen Stock Exchange. Again, this is consistent with the argument that firms listed on the Shanghai Exchange are more likely to be large state-owned companies whose information environments tend to be opaque The Effect of New Accounting Standards Adopted in 2007 As noted earlier, in 2006, the Chinese Ministry of Finance issued a new set of accounting standards, Accounting Standards for Business Enterprises (ASBEs). The new standards 21

23 substantially converge with IFRS and became mandated to all public firms effective on January 1, The implementation of the new accounting standards represents the most significant change in Chinese accounting history; however, previous studies document that the implementation of the ASBEs reduces rather than increases financial reporting quality. For instance, He et al. (2012) argue Chinese institutional infrastructure is incompatible with IFRS. In particular, the lack of relevant information to facilitate fair value pricing provides great flexibility to managers after China switched to fair value accounting under IFRS. Hence, the switch to new standards exacerbates managers incentives to manipulate earnings. Consistent with the arguments, He et al. (2012) document that the adoption of the ASBEs exacerbates earnings management in cases of trading securities sales and debt restructuring. Hou et al. (2015) find that, after the adoption of the ASBEs, Chinese firms experience a significant decrease in various measures of accrual-based earnings quality and firms suffer from a decline in investment efficiency. DeFond et al. (2014) show that foreign institutional investors decreased their investment in China due to the weak institutional infrastructure that impairs the reporting quality of the ASBEs. In addition to A-B share firms financial reporting quality experiencing a significant decrease after 2007, deteriorating accounting quality of A-share-only firms can also exert a negative externality on A-B firms information transparency. Previous studies have documented the existence of information externality in various settings (Schipper 1990; Gleason et al. 2008; Francis and Michas 2013). For example, Gleason et al. (2008) document that accounting misstatements discovered at one firm cause price declines of non-restating firms in the same industry as investors rationally reassess the credibility of financial statements issued by nonrestating firms. The information externality can be explained by the clustering of accounting choices for firms in the same industry (Schipper 1990) and by the sharing of the same auditors 22

24 (Francis and Michas 2013). In this section, we take advantage of this exogenous decrease in the financial reporting environment and examine whether A- and B-share return non-synchronicity increase in response. The key to the identification of this test is to control for other events that concurrently occurred with the implementation of the new accounting standards. One such significant event is the U.S. financial crisis, which originated from the U.S. capital market in 2007 and ended in Because the Chinese banking system was still under tight capital control and was relatively isolated from international financial markets around 2007, China s exposure to the financial crisis was limited to the real sector of the economy arising from the decrease in demands for Chinese products (Li et al. 2012). Nevertheless, to control for the potential ripple effects of the U.S. financial crisis, in some of the empirical analyses we exclude the financial crisis period from our sample and examine whether our results remain unchanged. Another concurrent noticeable event is the ownership structure reform started in 2005, under which part of the illiquid A-shares owned by the government and legal persons became gradually tradable after The gradual increase in the supply of freely tradable A-shares in the market may affect the measure of non-synchronicity. As discussed in the previous section, in Equation (1) we control for the measures of freely tradable A- and B-shares and related trading frequency and liquidity, which should mitigate this potential concern. To confirm that the overall financial reporting environment is indeed worsened after 2007, we investigate the effect of the new accounting standards on the four measures of earnings quality metrics for all A-share-only firms. Specifically, we examine the changes of the four earnings quality measures from the period to Again, we exclude the period in order to control for the potential confounding effects of the U.S. financial crisis. Results 23

25 are tabulated in Table 4 and Figure 2, where we graphically present the moving trends of the four measures of earnings quality from 2004 to Except for the measure of small positive earnings, which is not directly related to the change in accounting standards, the other three measures all indicate significant deteriorations in earnings quality after For example, the mean of earnings aggressiveness increases by after the adoption of the ASBEs and the increase is statistically significant at the 1% level. Likewise, Panel A of Figure 2 also shows the average signed accruals experience a sudden jump in the year 2007 when the ASBEs were adopted. More importantly, the upward trend (decreasing earnings quality) continues in the period post-financial crises. As a matter of fact, the measure of aggressiveness even becomes positive after In addition, firms reported earnings also become more smoothed after 2010 and the two measures of earnings smoothness based on the ratio and correlation increase by and 0.022, respectively. Both increases are statistically significant at 10% or lower. In contrast, the percentage of small positive earnings experiences a slight decrease after 2009, suggesting that managers increased financial reporting flexibility reduces rather than increases the likelihood of small positive earnings. Hence, both Table 4 and Figure 2 provide unambiguous evidence that the Chinese financial reporting environment deteriorates after the adoption of the ASBEs. Unreported results show that A-B share firms also experience a similar decreasing pattern in their A-share earnings quality metrics. [Insert Figure 2 and Table 4 about here] After documenting a decrease in earnings quality for all publicly listed A-share-only firms, we next switch our focus to the moving trend of A-B share non-synchronicity surrounding the adoption of the ASBEs. As discussed in Section 1, Figure 1 shows the year-by-year cross-sectional average of A-B share market-adjusted return non-synchronicity for the sample period. As shown in Figure 1, A-B shares have experienced a gradual decrease in share non-synchronicity starting 24