Management Forecasts or Earnings Announcements - CiteSeerX

Management Forecasts or Earnings Announcements: Which Information Do Institutional Investors Trade Based on? Yan-Leung Cheung School of Business, Hong Kong Baptist University [email protected] Kun Jiang School of Business and Management, Hong Kong University of Science and Technology [email protected] Weiqiang Tan School of Business, Hong Kong Baptist University [email protected] Tusheng Xiao School of Accountancy, Shanghai University of Finance and Economics [email protected] First version: November 2010 This version: October 2011

We would like to thank In-Mu Haw, Yu Xin, Honglin Yu, Feida Zhang, and seminar participants at Sun Yat-sen University and University of International Business and Economics for their helpful comments and suggestions. Tan is grateful for the funding of this research by Hong Kong Baptist University. The authors alone are responsible for all limitations and errors that may relate to the study and the paper.

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Management Forecasts or Earnings Announcements: Which Information Do Institutional Investors Trade Based on?

Abstract This study investigates how institutional investors in China trade based on management forecasts (MEFs) and earnings announcements (EAs). MEFs are mandatory under China’s stringent regulatory framework. We find evidence that both management earnings forecasts and earnings announcements have an impact on the market. However, we find that MEFs have a bigger market impact than EAs. Based on a sample of firm-semiannual observations from 2003 to 2008, we find that change in the stock ownership of institutions is positively associated with EAs but not significantly associated with MEFs. When we further examine the relations between institutional characteristics and trading strategies, we find that growth funds exploit the arbitrage opportunity of MEFs.

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I. Introduction This study examines the trading behavior of institutional investors in China based on management earnings forecasts and earnings announcements. Using the stock holdings of institutional investors, we test whether they take advantage of and form profitable trading strategies based on management earnings forecasts and actual earnings announcements. Unlike the US market, the Chinese Stock Exchanges require listed firms to disclose management forecasts when there are substantial changes in operation sales income or earnings. This has been mandatory since 2001.1 In most jurisdictions, management earnings forecasts are voluntary. In Japan, management forecasts are effectively mandated but not strictly enforced by the regulatory framework. As a result, managers tend to provide forecasts that are upwardly biased.2 Thus, the trustfulness of voluntary management earnings forecasts is in doubt. Skinner (1994) identifies the risk of litigation as an important factor associated with a firm’s decision to voluntary discloses management earnings forecasts in the US. Many studies find that managers have incentives to bias their earnings forecasts (e.g., Nagar, Nanda, and Wysocki, 2003; Rogers and Stocken, 2005; Cheng and Lo, 2006). In China, as the announcement is mandatory and there are legal liabilities for non-compliance, there is no problem with trustfulness. The Chinese regulatory framework requires China’s listed firms to disclose management forecasts no later than 30 trading days after the end of the fiscal period when the firms expect to have a substantial deviation from the previous annual earnings. This includes a substantial gain, substantial loss, gain from an expected loss, and loss from an expected gain in annual earnings. 1

On December 20th, 2001, the Shanghai and Shenzhen Stock Exchanges promulgated a memo named “Notice of Supervising the Listed Firms to Disclose the Annual Report of 2001.” The memo specified that management should guarantee the authenticity of the management forecast. Otherwise, the Exchanges are authorized to punish the offenders. To some extent, regulation and enforcement help to ensure that information in the management forecasts and earnings announcements are consistent with each other. 2 Kato, Skinner, and Kunimura Sr. (2009) find that firms revise their forecasts downward to gain a positive earnings surprise.

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The regulations define a substantial change as an increase or decrease in earnings of more than 50 percent. Mandatory management earnings forecasts should be more credible than those disclosed on a voluntary basis. The question that we ask is how institutional investors make use of these management forecasts in China to form trading strategies; more specifically, how do they trade based on the management forecasts and earnings announcements. Finally, we attempt to address how characteristics of institutional investors affect their trading behaviors. To evaluate the information content of the management earnings forecasts (MEFs) and earnings announcements (EAs), we examine the effect of the post-earnings announcement drift (PEAD) on the stock returns. The PEAD is the tendency for a stock’s cumulative abnormal return to drift in the direction of an earnings surprise after an EA. The PEAD was first documented in the US market by Ball and Brown (1968) and has been found in other stock markets. 3 Prior studies document stock prices reacting to MEFs 4 and other studies find that MEFs could affect a subsequent PEAD.5 The extant literature documents that both MEFs and EAs are material information that could affect the stock price. This study attempts to extend the existing literature to include the information content of mandatory MEFs and examines MEFs or EAs are more informative for investors. This paper also examines how institutional investors in China trade based on MEFs and EAs and whether they form trading strategies based on these announcements. We have two primary motivations for using China’s equity market to conduct our study. 3

Using a sample of UK firms, Hew, Skerratt, Strong, and Walker (1996) and Liu, Strong, and Xu (2003) find that PEAD exists outside of the US as well. Booth, Kallunki, and Martikainen (1996) find that the Finland market also exhibits a PEAD pattern. Del Brio, Miguel, and Perote (2002) find PEAD in Spain as well. Griffin, Kelly, and Nardari (2007) document that the development of the PEAD pattern in emerging markets is similar that found in developed markets. 4 Prior studies document that MEFs which exceed expected earnings (good news) are associated with significant positive abnormal stock returns and no significant negative returns are observed with bad news. For example, see Waymire (1984). 5 For example, see Kato, Skinner, and Kunimura Sr. (2009); Tawatnuntachai and Yaman (2007); Tucker (2007).

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First, the mandatory MEF disclosure requirement provides us with an ideal experimental setting to examine the information content of MEFs. The information content of both announcements should be consistent because both are mandated by the regulatory framework. 6 MEFs are voluntary in the US, which may lower the litigation risk of listed firms.7 However, in China, noncompliance could result in legal consequences. Second, as the largest emerging economy in the world, China’s rapidly expanding stock market is increasingly integrated with the global economy. The combined market capitalization of the two Chinese stock exchanges (Shanghai and Shenzhen) was over RMB 12,137 billion (about USD 1,776 billion) in 2008, making the Chinese stock market the second largest stock market in Asia, after Tokyo. As China gradually opens its equity market to the international investment community, it is attracting increasing interest from the international investment community. This is evidenced by the number of investment fund houses established in the region with a special focus in China. During the past two decades, there has been rising interest in investing in Asian emerging markets, particularly China. International fund management houses have been launching various investments products that either invest solely in China or have an explicit policy of investing a fixed portion of their portfolio in China. Apart from the growth potential of China, diversifying away the risk inherent in developed markets, such as the US and Europe, is another important reason for such an investment policy. Using a sample of 64,296 institution-firm-semiannual observations from 2003 to 2008 comprised of 3,479 firm-semiannual and 1,308 mutual funds, we find evidence that both MEFs and EAs are profitable opportunities. A zero investment strategy to MEFs and EAs yields a mean cumulative abnormal return of 6.20% and 2.34% over three months, respectively. The findings 6

In the U.S., listed firms disclose MEFs voluntarily and there are not explicit litigation consequences from the SEC. Management has much more leeway to decide whether to disclose and what to disclose. 7 For example, see Skinner (1994) and Brown, Hillegeist, and Lo (2005).

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reveal that institutional investors do not form trading strategies based on MEFs. Furthermore, we examine whether the characteristics of institutional investors affect their trading strategies using hierarchical linear modeling (HLM) with firm-institution-semiannual level data. We find that growth institutional investors and open-end funds exploit the arbitrage opportunity of MEFs, whereas larger funds exploit the drift on EAs. Further robustness tests support the findings. This paper contributes to the literature in the following ways. First, there is little research on PEAD in emerging markets. We provide additional empirical evidence that PEAD exists in emerging stock markets. Second, there is no previous research on PEAD in a setting where MEFs are taken into consideration. We find that MEFs affect PEAD to some extent. This finding helps us to understand why the PEAD anomaly occurs. Third, we find that as an aggregate, institutional investors exploit PEAD but not MEFs. This finding enriches the literature on the trading strategies of institutional investors. Our findings also suggest that incorporating institution-firm-level measures provides a more powerful and direct test of the behavior of institutional investors based on the MEFs. We find that institutions bearing higher risk prefer to trade based on MEFs. These results will help us to understand how institutional investor characteristics affect trading strategies. The remainder of this paper is organized as follows. Section 2 presents the literature review. Section 3 describes the variables used in this study. Section 4 presents the model specification. Section 5 presents the sample and descriptive statistics. The results are illustrated in Section 6. Section 7 tests the robustness of our findings and the last section concludes.

II. Literature Review Several papers investigate whether institutional investors form trading strategies based on

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PEAD. Some studies provide evidence that the presence of institutional investors reduces the magnitude of PEAD.8 Ke and Ramalingegowda (2005) find that institutional investors exploit PEAD and that their trading increases the speed with which prices impound the earnings information. Ali, Chen, Yao, and Yu (2007) present similar findings showing that actively managed US equity mutual funds use PEAD as a trading strategy. They also show that mutual funds that trade based on this strategy outperform those that do not. The existing literature finds that institutional investor characteristics affect their trading strategies. Abarbanell, Bushee, and Raedy (2003) and Bushee and Goodman (2007) examine two investment style classifications: Value and Growth. Del Guercio (1996) and Abarbanell, Bushee, and Raedy (2003) find that bank trusts and pension funds face strict prudent investment standards under the common law and the Employee Retirement Income Security Act (ERISA) and invest in a more risk-averse manner than do investment advisors. These institutions have more expertise than the average investor in valuing certain firms and different investment preferences. In contrast, Bushee (2001) finds that transient institutional investors have strong incentives to gather private information because they engage in strategies to profit from shortterm price appreciation. Ke and Petroni (2004) find that transient institutions are more likely to sell a firm before it has a break in a long sequence of earnings increases. However, Baks, Busse, and Green (2006) find that mutual funds taking “big bets” outperform more diversified funds. Prather and Middleton (2006) find that fund manager characteristics affect the performance and trading strategies of institutional investors. The characteristics of institutional investors influence their risk appetite and affect their trading strategies. After ten years of rapid development, institutional investors in China have differentiated themselves one from another. This study is the first to examine how different 8

See Bartov, Radhakrishnan, and Krinsky (2000) and Ke and Ramalingegowda (2005).

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institutional investor characteristics affect investment decisions in regards to exploiting information from MEFs and EAs.

III. Variables used in this study Because of data availability issues,9 we use semiannual data in our study. We measure the change in institutional ownership (ΔOWN) as the difference between the percent of total shares outstanding at the beginning and end of the semiannual period. The change in fund i’s ownership of firm j as a percentage of the outstanding shares at the beginning of the semiannual period t is denoted as ΔOWN ijt. We define ΔOWNjt as the change in fund holding for firm j as a percentage of the outstanding shares at the beginning of the semiannual period t. During the sample period, the market has a total of 952 voluntary management forecasts. These forecasts are excluded from our sample. We categorize the MEFs based on the announcement content for each period. We divide the MEFs into two categories: good and bad news. When a firm’s management forecast reports a 50% increase in earnings or revises the expected loss into a gain, we consider this to be good news. On the contrary, when a firm announces a 50% decrease in expected earnings and revises the expected gain into a loss, we consider this to be bad news. To investigate the effects of different disclosure timings on the trading strategies of institutional investors, we further separate MF into two variables to indicate management forecasts for different semiannual periods: MF1 measures management forecasts disclosed after the semiannual period and MF2 measures management forecasts disclosed during the 9

In China, a mutual fund only needs to disclose the top 10 stocks held according to the market value weighted in their quarterly report. This requirement leads to sample bias because of missing information about the additional stocks. To test robustness, we use the quarterly data of fund holdings to repeat our regression model and find that the findings are consistent.

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semiannual period. Both variables equal 1 if the management forecast is good news and -1 if the management forecast is bad news. We measure the EA information using standard unexpected earnings (SUE). SUE is defined as the seasonal differences in semiannual earnings, scaled by their historical standard deviation, where the standard deviation is estimated using up to a maximum of 5 reports of prior semiannual earnings. RSUE is the decile ranking of SUE (from 1 to 10) based on the distribution of SUE for each period. Figure 1 provides an example of the measurements of ΔOWN, SUE, MF1, and MF2 for a hypothetical firm whose fiscal year ends on December 31. RET0 is defined as the raw return for the calendar month before the institutional ownership semiannual measurement. RET6 is defined as the cumulative raw return for the 2 to 6 calendar months before the institutional ownership measurement. We also include the variable SIZE, defined as the natural logarithmic value of total market capitalization of the common stock at the end of the prior fiscal semiannual period. The variable BM is defined as the ratio of common book equity to total market capitalization at the end of the prior fiscal semiannual period which proxies for the risk of the stock. The variable VOL is defined as the natural logarithmic value of the trading volume, following Bartov, Radharkrishman, and Krinsky (2000) and Mendenhall (2004). We use VOL to proxy for the transaction cost. We measure liquidity using Amihud’s (2002) illiquidity measurement (ILLIQ), which is defined as the average of the daily ratio of the stock’s absolute return to its dollar trading volume over the current fiscal semiannual period. This study also examines how institutional investor characteristics affect trading strategies. Thus, we include several variables to characterize the institutional investors. First, we classify institutions into open-end funds and close-end funds. The variable FType equals one if the institution is an open-end fund and zero otherwise. An open-end fund faces redemption pressure;

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therefore, we expect the two types of institutions to differ from each other in trading strategies. Second, we define the variable FSize as the natural logarithm of total market capitalization of the fund at the end of the prior fiscal semiannual period. We expect that the trading strategy will be different for large versus small institutional investors. Third, we identify the investment style of the institutional investor (FStyle). The investment style indicates whether the institutional investor is a value investor or a growth investor. As shown by Abarbanell, Bushee, and Raedy (2003), we expect that the investment style of the institutional investors will affect their trading strategies. We include the concentration degree (FConcen), defined as the percentage of the market value of the top 10 stocks held based on the total market value the fund is invested in the stock market. The concentration degree measures the institutional investor’s distribution of attention. Finally, the literature documents that fund manager characteristics affect the institutional investor’s performance and trading strategies (Prather and Middleton, 2006). We include the stock selection capability of the fund manager (FManager) in the model. FManager is defined as the intercept of Treynor and Mazuy’s (1966) quasi market abnormal return model. Table 1 presents the definition of all of the variables in our study.

IV. Methodologies Our analyses are divided into two parts. First, we address the issue of whether institutional investors in China trade based on MEFs, EAs, or other indicators. These indicators include momentum, size, and book to market ratio. Second, we attempt to identify how institutional characteristics affect trading strategies. We analyze the institutional investor’s trading strategies by examining the change in stockholdings using ordinary least squares regression. We aggregate all of the holdings of each

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institution into a total firm-level ownership variable. Then, we regress the change in holdings on each of the characteristics measures of the firm, respectively. We estimate the following regression model of semiannual changes of institutional ownership for firm j in period t: (1)

OWN jt   ij  1MF1 jt   2 MF 2 jt   3 SUE jt   4 RET 0 jt   5 RET 6 jt   6 Size jt   7 BM jt   8VOL jt   9 ILLIQ jt  10OWN jt 1   jt

These methodologies are commonly used in the prior research (e.g., Ke and Ramalingegowda, 2005). We also repeat our analysis using a fixed effects regression for the panel data. Furthermore, to mitigate cross-sectional correlation biases (Bernard, 1987), we also re-estimate the model using the Fama-MacBeth (1973) method. The regression model does not differentiate between institutional investors according to their characteristics. Thus, we adopt the hierarchical linear modeling (HLM) of Raudenbush and Bryk (2002) that allows us to include institutional characteristics in the analysis. For each firm, the change in holdings by individual institutions is regressed on the firm variables. The coefficients from the first stage are then regressed on the institutional characteristics measures.

The first stage

determines how the change in stockholdings is related to the trading indicators. The second stage reveals how sensitive this change is to institutional characteristics. To illustrate this methodology, let ΔOWN

ijt

represent the change in ownership by

institutional investor i in firm j at time t, S ijt represent the vector of firm information proxies, and F ijt represent the vector of institution characteristics measured at time t relative to the change in holdings. This approach can be represented by the following multilevel model: (2a)

OWN ijt   jt   jt Sijt   ijt

(2b)

 jt   1  0 Fjt

(2c)

 jt   2  0 Fjt 11

The β coefficients in Equation (2a) measure the sensitivity of changes in institutional holdings to the firm information variables. The λ coefficients in Equation (2c) measure the sensitivity of these changes to institutional characteristics. If these coefficients are significant, the results support the hypothesis that institutional investor trading is sensitive to firm information and is affected by its own characteristics. In our model, S ijt is the vector of firm information proxies, including MF1, MF2, SUE, RET0, RET6, Size, BM, VOL, ILLIQ, and OWNt1.

F ijt is the vector of the institutional characteristics measures, which include FType, FSize,

FStyle, FConcen, and FManager.

V. Data and Sample Selection

Our sample period covers the second semiannual period of 2003 through the second semiannual period 2008, a period during which the number of institutional investors in China grew rapidly. We apply the following data requirements to our sample selection. First, to avoid a contamination effect between MEFs and EAs in examining the market reaction to these announcements, we require the time difference between the two announcements to be more than 20 trading days. Second, we exclude firms in the financial industry because financial firms have a different balance sheet structure than those of other industries. Third, we also exclude firms listed in the Middle and Small Enterprises Board (MSEB), because the MSEB has special disclosure regulations. Fourth, we exclude newly listed firms that are in their first year after initial public offerings. Fifth, we exclude those firms with a market value less than RMB 1 million. Finally, we exclude firms with incomplete information for control variables. The sample selection results in 64,296 institution-firm-semiannual observations, representing 3,479 firmsemiannuals and 1,308 institutions.

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We obtain institutional investor holdings position data from the Wind database. Firm financial data and stock return data is from the China Security Market and Accounting Research database (CSMAR) and fund characteristics data is from the Wind database. Table 2 reports the descriptive statistics of the firm characteristics. The first row presents the change of ownership for the firm-institution-semiannual level. It shows that the average semiannual changes in individual institutional investor holdings tend to be small in terms of the percent of total shares outstanding (mean change is -0.01%), whereas the changes in percentiles 25 and 75 are quite large (the change in Quartile 1 is -0.39%, whereas the change in Quartile 3 is 0.35%). The results imply that institutional investors in China change their investment portfolios frequently. The second row presents the aggregate changes in ownership for institutional investors at the firm-semiannual level (the mean change is -0.20%); the changes in percentiles 25 and 75 are large (the change in Quartile 1 is -3.34% while that in Quartile 3 is 3.72%). The remainder of Table 2 provides descriptive statistics for the firm-semiannual characteristics. The mean of SUE is 0.18, whereas the mean of the raw return for the calendar month before the semiannual institutional ownership measurement (RET0) is 0.19%. The mean of SIZE is 21.33, whereas the mean of BM is 0.38. The means of VOL and ILLIQ are 22.06 and 1.81, respectively. Table 3 reports the descriptive statistics for the institutional investor characteristics. Panel A shows the descriptive statistics. We find that 80% of the institutional investors in China are open-end funds. The mean size of institutional investors is about RMB 2.4 billion (the mean of Fsize, defined as the natural logarithm of the market value, is 21.60). The range of Fstyle is from 2.10 in Quartile 1 to 2.96 in Quartile 3, suggesting that institutional investors show different preferences for value versus growth stocks in their stock portfolios. The mean of the variable Fconcen is 0.53, with a standard deviation of 0.12, implying that the institutional investors

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investing in the top 10 stocks are similar to each other. The variable FManager ranges widely from 0.04 in Quartile 1 to 0.55 in Quartile 3, with a standard deviation of 0.40, implying that the manager’s capabilities for stock selection differ from each other. 10

VI. Results

To investigate the market reaction to MEFs and EAs in China, we compute the mean cumulative abnormal return (CAR) at different time intervals. The CAR is calculated based on the method used by Foster, Olson, and Shevlin (1984) and Bernard and Thomas (1989). For each event, we calculate the firm’s size-adjusted return over different windows following the announcement. The daily size-adjusted return is calculated as the difference between the firm’s equity return and a benchmark portfolio return based on market capitalization deciles. We also test the abnormal return in long-term windows using the buy and hold abnormal return (BHAR) method. As mentioned earlier, we classify MEFs into two types: good news and bad news. We consider a firm announcing a substantial increase in earnings or gains following a previous loss to be good news. Alternatively, we consider an announcement of a substantial decrease in earnings or a loss following a previous gain to be bad news. The results are presented in Panel A of Table 4 and show MEF(bad) to have a negative market reaction for the three event windows. The negative drift lasts from the event windows of 3(-1,+1) to 21(-10,+10) days. Interestingly, the phenomenon persists for one month. The one-month BHAR is -3.35% and is statistically significant. The market reaction for MEF(good) is very positive. All event study results are

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We examine the correlation coefficients between the institutional investor characteristics to ensure there is no multicollinearity problem in the analysis. The result is not presented in the paper to save space. We find that the correlation coefficients are small between different characteristic measurements. For example, the largest coefficient is the correlation coefficient between FConcen and FSize at -0.34 .

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positive and significant. The positive drift phenomenon lasts for as long as three months. The three-month BHAR is 5.66% and is significant at the 1% level. The results demonstrate that the positive drift of MEF for positive earnings forecasts has a stronger market reaction than the negative drift generated from negative earnings forecasts. We construct an arbitrage strategy to take a long position in firms with a positive MEF and a short position in those with a negative MEF. The results shown in the last column of Panel A of Table 4 find a positive and significantly positive return for short and long terms strategies, respectively. Panel B of Table 4 presents the results of the market reaction to EAs. The results show the market reaction to firms with the most positive (RSUE=10) and negative (RSUE=1) earnings surprise. We find that EAs do have an impact on the market. For stock portfolios with the most negative earnings surprise (RSUE=1), the effect is found to be negative and significant. The negative drift is found to last for a three-day window. The effect disappears on the longer term event windows. The one-month BHAR is found to be negative and significant, but not the threemonth BHAR. However the effect is found to be stronger for firms with the most positive earnings surprise (RSUE=10). The positive effect lasts through all event windows and also for the longer-term buy and hold abnormal returns. The results for both announcements show that the immediate market reaction is stronger for good news than for bad news. In addition, the market seems to need more time to digest good news, with the positive effect lasting much longer than that for bad news. We then examine which of the two announcements is more informative. In our sample, there are firms that have both announcements and firms with only EAs. We further split our sample into two subsamples: firms with only EAs and those with both EAs and MEFs. Panel C

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presents the results of the market reaction to EAs for firms with only EAs. The results are similar to those presented in Panel B. Panel D presents the market reaction to EAs of a subsample of firms with both MEFs and EAs. The results show that the market still reacts immediately to bad news over a three-day window. The market also reacts immediately to good news during the 3day window. A positive reaction is found in the one-month buy and hold period. In summary, the market reaction to MEFs is stronger than that to EAs. Our findings reveal that the market reaction to EAs is dominated by firms without MEFs. However, when firms post an MEF, the market does not have much of a reaction to their EA. This implies that investors react more to MEFs than to EAs, if there is an MEF. Our findings show that investors consider MEFs to be more informative than EAs. One possible explanation is that MEFs are mandatory, which could minimize the creditability issue of voluntary management forecasts. Next, we examine whether institutional investors in China form trading strategies based on the two announcements. Table 5 reports the regression results on the change in ownership of an institutional investor in firm j at semiannual t. We apply three regression models for the test. Column (1) reports the OLS regression results, whereas Columns (2) and (3) report the results using a fixed effects regression and the Fama-MacBeth cross-sectional regression, respectively. The predicted signs on the explanatory variables are listed in the second column. The coefficient on RSUE is positive and significant in the three models. The results suggest that institutional investors as an aggregate exploit the arbitrage opportunity of EAs. However, the coefficients of MF1 and MF2 are not significant in the three models, suggesting that overall, institutional investors do not exploit the arbitrage opportunity of MEFs. The coefficients of SIZE and BM are significantly positive and negative, respectively, implying that institutional investors show systematic preference for large stocks and growth

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stocks in their trading decisions. The transaction cost proxy variable VOL is negatively associated with ΔOWNjt, suggesting that institutional investors prefer stocks with low transaction costs. As expected, the coefficient of OWNt-1 is significant and negative. We further examine how institutional characteristics affect trading strategies. Table 6 presents the results of the HLM. We only report the coefficients for the interactions between the firm variables and the institutional investor characteristics. The characteristic FStyle is significantly and positively associated with MF2 in the HLM model, suggesting that institutional investors exploit the arbitrage opportunity of MEFs of current earnings performance. This result also shows that the interaction coefficient of FType and MF2 is negative and significant, implying that open-end funds do not exploit the drift of MEFs. The results imply that institutional investors who are more aggressive on performance will exploit MEFs. The results show that SUE is exploited by larger institutions. We next investigate whether evidence of institutional investor trading is affected by the information environment. Management forecasts provided by larger firms are often more reliable than those of smaller firms. Transaction costs are larger in smaller firms, which could reduce the potential trading profits. To test for any differences based on firm size, we estimate the HLM model with the sample partitioned into small and large firms based on the median market value of equity. Table 7 presents the results for the small and large firm subsamples. The results show that growth funds, open-end funds, and concentrated-investing funds exploit the arbitrage opportunity of management forecasts in smaller firms. This result is consistent with greater opportunities for informed trading among small firms due to their poor information environment, which is consistent with the findings of Bushee and Goodman (2007). The positive relationship between

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FSize and SUE observed in Table 7 is driven by large firms. This result implies that large firms and larger institutional investors have systematic preferences to exploit PEAD but not MEFs.

VII. Robustness Test

Due to the data availability, we use semiannual data in the analysis. To test whether our findings are sensitive to the data frequency, we also use quarterly data to repeat the regression models. We repeat our analysis by assuming that the institutional ownership of firm j equals 0 if the ownership data for firm j is not in the top 10 stock list in the 1st and 3rd quarter reports and compare the percentage of institutional ownership between the two quarters to get ΔOWNjt and ΔOWNijt. The findings show that the significances of the variable coefficients are smaller than those reported in Tables 6 and 7. But the conclusion is consistent with the previous analysis. This implies that our inferences are not sensitive to the data frequency. In our study, due to the different timings of the forecasts, we separate the MEFs into two variables: MF1 and MF2. We also repeat the regressions using a single variable MF. We find that the results are consistent with those in Table 6, but that the coefficient of the interaction variable MF*FType is significant at the 10% level only. The other findings are consistent with those in Table 6. In our study, we only include observations that have data regarding MEFs, EAs, and the holding positions of institutional investors. This could induce a sample selection bias problem due to the holding preferences of institutional investors. Therefore, we further test whether the sample selection problem affects our findings. First, we get the full population of 11,459 observations in the sample period, including observations with and without management forecasts. We rank SUE in the full population (from 1 to 10), and then we compare the

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distribution of the sub-groups with institution holdings and without institution holdings. We find that the distribution of the two sub-groups is not significantly different. Second, the correlation coefficient of MF1 and SUE is 0.339, whereas that of MF2 and SUE is 0.195, implying that the two announcements are related but not highly correlated with each other. Third, we further repeat the regression analysis using the full sample. At this stage, we set the holding position of institutional investors equal to 0 if the holding position data are missing. We find that the significances of the coefficients are smaller than we report in Tables 5 through 7, but the main findings are consistent. All of these tests imply that our findings are not sensitive to our sample selection process.

VIII. Conclusions

The first part of this paper examines the information content of MEFs and EAs in China. Unlike most developed markets, MEFs are mandatory under China’s stringent regulatory framework. Thus, MEFs in China should be more credible that those of jurisdictions with voluntary disclosure. We find that both MEFs and EAs have an impact on the market. Our findings show that there is evidence of post announcement drift (PEAD) for both MEFs and EAs in China. However, the market does not react to a firm’s EA when the firm has an MEF. This implies that MEFs are more informative than EAs. This may be the reason that MEFs are mandatory in China; the market believes that MEFs are more credible. The second part of this paper examines whether institutional investors in China form trading strategies based on these two announcements. Based on a sample of 64,296 institutionfirm-semiannual observations from 2003 to 2008, we find that changes in institutions’ stock ownership are positively associated with contemporaneous earnings surprises but insignificantly

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associated with MEFs. This implies that institutional investors in China form trading strategies based on EAs. When we examine the relations between institutional characteristics and trading strategies, our findings reveal that growth funds exploit the arbitrage opportunity of MEFs.

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Analysis Methods.” Second edition, Sage Publications, Thousand Oaks, California (2002). Rogers, J. L., and P. C. Stocken. “Credibility of Management Forecasts.” The Accounting Review, 80 (2005), 1233-1260 Skinner, D. “Why Firms Voluntarily Disclose Bad News.” Journal of Accounting Research, 32 (1994), 38-60. Tucker, J. “Is Openness Penalized? Stock Returns Around Earnings Warnings.” The Accounting Review, 82 (2007), 1055-1087. Tawatnuntachai, O., and D. Yaman. “Do Investors Overreact to Earnings Warnings?” Reviews of Financial Economics, 16 (2007), 177-201. Treynor, J. L., and K. K. Mazuy. “Can Mutual Funds Outguess the Market?” Harvard Business Review, 44 (1966), 131-136. Waymire, G. “Additional Evidence on the Information Content of Management Earnings Forecasts.” Journal of Accounting Research, 22 (1984), 703-718.

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ΔOWNt

12/31/2005 Management Forecasts (MF1t) Announcement date for 2nd interim of 2005

6/30/2006 Earnings surprise (SUEt) Management Forecasts Announcement date for (MF2t) Announcement date 2nd interim of 2005 for 1st interim of 2006

Figure 1 Timeline for measurement of variables for a hypothetical firm. SUE, ΔOWN, MF are defined in Table 1.

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TABLE 1 Variable Definition Variable Definition Firm Characteristics OWN ijt The fund i’s ownership for firm j as a percentage of the outstanding shares at the beginning of the semiannual calendar t. ΔOWN ijt The change of fund i’s ownership for firm j between the percent of outstanding shares at the beginning and end of the semiannual calendar t. ΔOWN jt The change of aggregate institution’s ownership for firm j between the percent of outstanding shares at the beginning and end of the semiannual calendar t. MF1 Management forecasts disclosed after the end date of the semiannual period but before the disclosure date of the earnings announcement. MF1 equals -1 if bad news in management forecasts; MF1 equals 1 if good news in management forecasts; 0 otherwise. MF2 Management forecasts disclosed during the semiannual period. MF2 equals -1 if bad news in management forecasts; MF2 equals 1 if good news in management forecasts; 0 otherwise. SUE Standard unexpected earnings. SUE is defined as seasonal differences in semiannual earnings, scaled by their historical standard deviation, where the standard deviation is estimated using up to a maximum of 5 reports of prior semiannual earnings. RSUE Rank of SUE. RSUE is the decile ranking of SUE (from 1 to 10) based on the distribution of SUE for each period. RET0 The raw return for the calendar month before the institutional ownership measurement semiannual. RET6 The cumulative raw return for the 2 to 6 calendar months before the institutional ownership measurement semiannual. SIZE The natural logarithmic value of total market capitalization of the common stock at the end of the prior fiscal semiannual period. BM Book to market ratio is defined as the ratio of common book equity to total market capitalization at the end of the prior fiscal semiannual period. VOL The natural logarithmic value of the trading volume following Bartov, Radharkrishman, and Krinsky (2000) and Mendenhall (2004). We use VOL to proxy transaction cost. ILLIQ Amihud’s (2002) illiquidity measurement. ILLIQ is defined as the average over the current fiscal semiannual period of the daily ratio of the stock’s absolute return to its dollar trading volume. Institutional Investor Characteristics FType Fund type is equal to 1 if the institution is an open-end fund, otherwise equal to 0 if the institution is a close-end fund. FSize The natural logarithm of total market capitalization of the fund at the end of the prior fiscal semiannual period. FStyle Investment style of the fund, calculated by the sum of (average P/E ratio of stocks held by fund/ average market P/E ratio) + (average P/B ratio of stocks held by fund/ average market P/B ratio). FConcen Stock investment concentration. The percentage of the market value of the top 10 stocks held based on the total market value the fund invests in the stock market. FManager The stock selection capability measure of the fund manager, defined as the intercept of Treynor and Mazuy’s (1966) quasi market abnormal return model.

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TABLE 2 Descriptive Statistics of Firm Characteristics This table reports descriptive statistics for the change in institutional ownership and firm characteristics. The sample includes the observations which have the data of management forecasts, earnings announcement, and the holding positions of institutional investors from 2003 to 2008. The statistics for all variables (except ΔOWN ijt) are based on 3,479 firm-semiannuals. Definitions of variables are in Table 1. Std. Variable Obs Mean Q1 Median Q3 Dev 64,296 -0.0116 1.1674 -0.3924 0.0000 0.3532 Change in ownership by an individual institution (ΔOWN ijt, %) 3,479 -0.1970 7.9582 -3.3359 0.3423 3.7184 Change in aggregate ownership by all institution (ΔOWN jt, %) SUE 3,479 0.1771 0.6099 -0.2457 0.1325 0.5952 MF1

3,479

0.0822

0.3718

0.0000

0.0000

0.0000

MF2

3,479

0.1028

0.3876

0.0000

0.0000

0.0000

RET0 (%)

3,479

0.1865

0.5671

-0.1647

0.0764

0.4192

RET6 (%)

3,479

0.2035

0.5646

-0.1400

0.0538

0.3442

SIZE

3,479

21.328

1.0106

20.560

21.217

21.954

BM

3,479

0.3830

0.2328

0.1984

0.3390

0.5208

3,479

22.064

1.3021

21.029

22.053

23.042

3,479

1.8071

2.0371

0.4319

0.9937

2.3665

VOL -9

ILLIQ (×10 )

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TABLE 3 Descriptive Statistics of Institutional Investor Characteristics This table reports descriptive statistics of institutional investor characteristics. The sample includes the observations which have the data of management forecasts, earnings announcement, and the holding positions of institutional investors from 2003 to 2008. The statistics for all variables are based on 1,308 institution-semiannuals. Panel A reports descriptive statistics. Panel B reports the Pearson (top) and Spearman (bottom) correlation coefficients. P-value are presented in parentheses below correlation coefficients. *, ** and *** denotes two-tailed statistical significance at 10%, 5% and 1%, respectively. Definitions of variables are in Table 1. Panel A: Descriptive Statistics Variable FType FSize FStyle FConcen FManager

Mean 0.7974 21.602 2.5596 0.5301 0.3157

Std. Dev 0.4021 1.2468 0.6302 0.1199 0.4018

Q1 1.0000 20.881 2.1019 0.4453 0.0400

Median 1.0000 21.706 2.4779 0.5187 0.2800

Q3 1.0000 22.520 2.9563 0.5994 0.5500

FType

FSize

FStyle

FConcen

FManager

Panel B: Correlation Matrix Variable FType

1.0000

FSize

-0.0486* (0.0791) 0.2066*** (