Why does the Reaction to News Announcements Vary across Countries?

Transcription

1 Why does the Reaction to News Announcements Vary across Countries? John M. Griffin a, + Nicholas H. Hirschey a, and Patrick J. Kelly b a University of Texas at Austin, McCombs School of Business, Austin, TX 78712, USA b University of South Florida, Tampa, FL 33620, USA First Draft: May 22, 2008 We thank Bernie Black, Jay Hartzell, Alok Kumar, Federico Nardari, Clemens Sialm, and Sheridan Titman for helpful discussion. addresses: john.griffin@mccombs.utexas.edu, nicholas.hirschey@phd.mccombs.utexas.edu, and pkelly@coba.usf.edu.

2 Abstract We examine stock price reactions to large news events (earnings and takeover announcements) around the world. We find that the reactions to major events vary widely around the world with some markets exhibiting large reactions and other markets with little or no reaction. We investigate possible explanations including: erroneous announcement dates, the lack of meaningful accounting numbers, delayed reaction to news, poor news quality, and insider trading. Overall, we find substantial support for the cross-country differences in reactions being driven by insider trading and press freedom as a proxy for news dissemination.

3 News announcements, such as annual earnings reports, are made for most traded firms in both developed and emerging markets. Yet relatively little is known about how market participants respond to this news. Does the strength of stock price reactions to news vary widely across countries? If so, why do these reactions vary? Are they primarily driven by differences in the quality of the information released or by the timing of when the news is traded on? This paper examines the market reaction to common events around the world. We believe such a study is useful for deepening our understanding of the information environment around the world using an approach that is extremely systematic across countries. Our study reveals that stock market reactions to news do vary widely around the world and they seem to vary mainly due to the timing of when news is incorporated into prices. In an innovative paper, Bhattacharya, et al (2000) track down news announcement dates in Mexico from in a window from 1994 to Interestingly, they find that in Mexico there is no stock price reaction on the announcement day. After examining possible explanations, they conclude that this is due to rampant insider trading. In their abstract they state that their paper points toward a methodology for ranking emerging markets according to their market integrity. The biggest barrier that has stood in the way of such a systematic study is the difficulty in obtaining accurate news event dates. For example, Bhattacharya et al. (2000), use 75 events that are hand collected from Bloomberg. We also obtain event dates from Bloomberg, and we verify through hand checks of alternate news services that Bloomberg announcement dates are indeed typically accurate (roughly 75 percent of the time). We use additional procedures to increase our event date accuracy up to an estimated 90 percent accuracy. For earnings news we create two samples, one where Bloomberg events are overlapping with IBES event dates and another where they are verified by Factiva news articles. Our combined sample of earnings news event dates from January 2, 2001 to October 12, 2007 consists of 54,336 earnings announcements in developed markets (excluding the U.S.) and

4 13,884 emerging markets. Additionally, we obtain merger dates for target firms from three sources and use only the earliest date for each event from among those sources. Several papers have examined stock market reactions to earnings or news internationally. The closest paper to ours is by DeFond, Hung, and Trezevant (2007), who examine the role that investor protection, accounting standards, and insider trading laws play in explaining cross-country differences in event reactions. The first major difference between their study and ours is that they use IBES event reactions that we show (and they also acknowledge) often exhibit problems with accuracy internationally. Second, they examine only 26 primarily developed markets, whereas we are able to obtain events from many smaller markets. Third, their focus and conclusions are vastly different from ours. Bailey, Karolyi, and Salva (2006) use earnings event reactions before and after a U.S. cross-listing and find that U.S. accounting requirements lead to increases in earnings announcement reactions. In an interesting study on takeovers Bris (2005) examines general patterns of stock price run-ups prior to takeovers before and after the implementation of insider trading laws. 1 We first examine if news announcements vary across countries. We measure the average absolute return over a three-day announcement window scaled by the average non-event day volatility as our measure of normalized event volatility. We find that event dates reactions do vary substantially across countries ranging from a value of two times normal event volatility to a value of 1.03 in Mexico, which indicates that an earnings event day in Mexico is essentially the same as any other day. Nine emerging and three developed markets (Austria, Greece, and South Korea) have event day reactions below 1.2 whereas nine developed markets exhibit reactions above Perhaps due to the scarcity of takeover events, in certain countries, his paper does not contain country-by-country analysis. Ackerman, Halteren, and Maug (2008) find that insider trading laws and not actual enforcement of the laws is more important for explaining pre-announcement price run-ups for takeovers.

5 We next turn to understanding the causes of the cross-country event reactions. There are five main factors that may affect the reaction to a news event. First, if due to poor accounting quality the earnings news is of low or no value, then there is little reason for stock prices to react. Second, investors may not actively research stocks because the costs to collecting information are high, which is typically referred to as investor inattention. Third, due to poor information dissemination mechanisms, investors may be slow to receive the news. Fourth, investors may be able to anticipate the news prior to the event through other public, non-announcement-related sources. Fifth, the news may be largely impounded into prices through privately informed trade. We find support for the third and fifth hypothesis. We examine these hypotheses by studying differences in volatility and returns during announcement, pre-announcement, and post announcement windows. This is followed by a joint examination of these hypotheses in a cross-sectional, cross-country regression framework. To examine the first hypothesis that poor earnings quality leads to smaller announcement period returns, we examine the difference in abnormal stock returns between stocks with high and low changes in earnings over the entire fiscal year. If low event reactions are driven by poor earnings quality, then we expect a weak relation between stock prices and earnings in countries where there is little reaction to earnings announcements. In contrast, we find that the difference between the returns of firms with positive and negative changes in earnings is 21 percent per year in the quartile of countries with the lowest reactions to earnings news. This is similar to the 26 percent per year difference in the top quartile of countries with high event reactions. Next, since the strength of takeover announcements does not depend on the quality of accounting data (like earnings), we examine the relation between earnings event reactions. The cross-country correlation between earnings and takeover event reactions is a highly significant 0.57.

6 To examine the second and third hypothesis we look for evidence of drift prior to earnings announcements. In contrast to the predictions of these two hypotheses, we find substantial drift prior to earnings announcements in both high and low earnings announcement countries, though positive drift is somewhat higher in low reaction countries. To examine the fourth and fifth hypotheses dealing with news being anticipated (either through public or private channels), we examine if returns are moving in the same direction as positive and negative earnings surprises (measured relative to IBES analyst forecasts). In low reaction countries the relation between earnings surprises and prior period returns is similar to that in high reaction countries even though the stock price movement on the announcement day is small in the low reaction group. Additionally, merger announcements help us distinguish between the public and private channels, since merger announcements are unscheduled events and in most cases difficult to predict. In low reaction countries we find that 62.4 percent of the total price run-up is reflected in the pre-announcement period, whereas in high event reaction markets, only 30.9 percent of the run-up is in the pre-announcement period. Since we take the first takeover announcement date, these findings seem to support the insider trading hypothesis over public information leakage. To examine all our five main hypotheses jointly in a cross-country framework, we regress our average normalized event volatility ratio on variables reflecting aspects of accounting quality, the information environment, trading activity, insider trading law and practice, laws regarding investor protection and trade, the level of economic and market development, and trading costs. Of the thirty three variables related to these hypotheses that we examine in univariate regressions, we then examine 153 regressions of unique combinations of the 18 significant variables in bivariate regressions. From these regressions we examine the top seven in multivariate specifications (Panel C in Table V). Of these variables, two survey response measures from the World Economic Forum s Global Competitiveness Report (GCR) Executive Opinion Survey emerge as significant in nearly

7 every specification: freedom of the press (a proxy for the quality of the information environment) and prevalence of insider trading. We also estimate Panel regressions with firm-level controls and find that these two variables are highly significant, along with a GCR survey variable on the prevalence of ethical firms. Countries with a more ethical business environment, where there is less of a perception of insider trading, and with greater press freedom exhibit stronger event reactions. In sum, both our sorting results as well as cross-sectional regressions suggest that cross-country differences in the way that firms respond to news are driven by the prevalence of insider trading and strength of the financial press. Our paper relates to a large international literature that examines the effect of insider trading laws or the enforcement of these laws on issues such as the cost of capital [Bhattacharya and Daouk (2002) and Beny (2007)], volatility [Du and Wei (2004)], idiosyncratic volatility [Fernandes and Ferreira (2007)], and stock return autocorrelations patterns [Durnev and Nain (2007)]. The existence of a law or its enforcement is only a proxy for the prevalence of insider trading. Bhattacharya (2006) argues that even in a developed market such as Canada with prior enforcements that insider trading may be widespread and that larger penalties and more enforcements can improve market conditions. We add to this literature by providing a proxy to partially measure the extent to which insider trading may occur across markets. Section I describes and outlines our five main hypotheses. Section II describes our data sources and process of gathering announcements and in insuring accuracy. Section III displays our event reactions and Section IV shows various sorting tests to shed light on our hypotheses and Section V further tests them through cross-country regressions. Section VI concludes.

8 I. News Channels This section outlines potential causes for between-country differences in the reaction to the public release of firm-specific information. Before discussing hypotheses, a basic point is important to emphasize: Without accurate event dates, any analysis of news events is fundamentally flawed. For this reason, we use announcements that have been verified from two different databases. In addition, we confirm news announcements with independent news sources as we describe in much more detail in our data section. At its most basic level, the price reaction to a given news event is a function of the magnitude of the valuation change signaled by the news. The price reaction is further affected by 1) the quality and precision of that signal and 2) whether there is information leakage prior to the news announcement. Our five hypotheses derive from these two basic principles. A. Accounting Quality First, the strength of the earnings news reactions may vary across countries due to the importance or accuracy of financial accounting across countries, i.e. the quality and precision of the valuation signal. In the extreme, a country with meaningless accounting standards would have no reaction to earnings related news because investors assign no meaning or value to the accounting information content of the news. Ball, Kothari, and Robin (2000) find evidence that the variation in international accounting standards is sufficient to produce economically significant differences in financial statements. If firms rely more on the financial markets for funding, they may be more inclined to provide accurate and timely financial statements (see Ball, Kothari, and Robin (2000)). Similarly, Ali and Hwang (2000) show that the value of accounting information is decreasing in a country s dependence on bank finance. This paper uses measures of earnings management, the level of financial disclosure within a country, and a country s reliance on the stock market or banks for

9 external financing needs to examine whether accounting quality (signal precision) affects announcement reactions. Also, we examine news announcements from takeovers as an alternative form of news that is not typically subject to the quality of accounting information. B. Inattentive Investors and Poor Information Environment Second, reactions may be delayed due to inattentive investors. In the extreme, even in the face of significant news, stock prices might not move simply because investors are not reacting to news [Huberman and Regev (2001), Peng and Xiong (2006)]. In the U.S., inattention has been shown to lead to less reaction and more post-earnings drift on days with multiple earnings announcements [Hirshleifer, Lim, and Teoh (2007)] and on Fridays [DellaVigna and Pollet (2008)]. We examine the magnitude of inattention by looking at the strength of post-earnings announcement drift across markets and whether the magnitude of event day reactions is associated with the amount of active trading in the market. Third, a closely related hypothesis is that investors are slow to react not because they are inattentive but because information is slow to reach the investor. If investors are not aware of an event, then they cannot react to it. Consequently, one would expect stronger market reactions in countries where information is transmitted relatively quickly and effectively. There may also be a secondary effect; Grossman and Stiglitz (1980) note that when information acquisition costs are high less information will be gathered. If poor communications infrastructure represents a component of information acquisition costs, then in countries with poor communications infrastructure, not only is it harder for investors to acquire firm-specific information, but they may also be inclined to collect less of it. To analyze such effects we use controls for the quality of information dissemination within a country.

10 A final related variant of both of these hypotheses is that investors simply do not respond at all to news because the market is informationally inefficient. This could either occur due to fully inattentive investors or because news is so poorly disseminated that the marginal investor does not receive it. With the inattentive investor hypothesis or slow news dissemination, we expect to have little event reaction but substantial post-announcement drift but with a completely inefficient market there should be no movements in the direction of price announcements in either the pre-, post-, or event period. As such, a completely informationally inefficient market is similar to a market where the accounting information is invalid. In this case, the market would have neither drift in the direction of the earnings surprise nor any pre-announcement price movement in the direction of the news. C. Pre-Announcement Trading through Public or Private Information Our fourth and fifth hypotheses posit that the magnitude of news announcement reactions is reduced by information leakage prior to the event day. This could occur due to news being released through public channels outside of the official earnings announcement such as through public conference calls with analysts, our fourth hypothesis, or it could be do to investors with privileged knowledge trading on information before it is released to the public, our fifth hypothesis. Such trading will dampen market reactions to information releases, since market participant reveal information when they trade [Grossman (1981)]. It is important to note that we use the term insiders to denote trading on to information not released through public channels. It is difficult to pin down whether this trading is due to trading that is explicitly illegal in nature, though in crosscountry analysis we will investigate linkages to perception of insider trading and its relation to insider trading laws.

11 We examine preannouncement run-up to explore the possibility that information gets to the market prior to earnings events. If information is dispersed into prices through insider trading or other public or private information based trade then we should see prices moving in the direction of the earnings surprise before the reaction in countries with little event reaction. To separate the public and private channel of early information release (our fourth and fifth hypotheses), we use the prevalence of insider trading (a country level variable) to examine whether average country level reactions are lower in countries with more insider trading. Most of the evidence that we use to investigate each of these five hypotheses also has implications for the other hypotheses, which we discuss in greater detail when describing our findings. In addition, we use cross-sectional regressions and country-level variables to investigate additional aspects of what features are related to event reactions across countries. II. Data The main data in this paper consist of firm stock returns and accounting variables, countrylevel descriptive variables, and most importantly earnings and takeover announcements. A. Preliminaries: Return, Accounting, and IBES data Daily returns accounting for dividends and capital structure changes, and market capitalization series are from CRSP for the United States and from Thomson Financial s Datastream for the rest of the world. Since we wish to restrict our sample to common equity, we use stocks with CRSP share code of 10 or 11 in the U.S. For non-u.s. securities we follow the substantial screens of Griffin, Kelly, and Nardari (2008) which eliminate preferred stock, warrants, unit or investment trusts, duplicates, GDRs or cross-listings, and other non-common equity. We also use their return filters to smooth potential data errors. We use Datastream s value-weighted total market index returns. Because our reactions are relative to local indices, we leave returns in local currency. For

12 annual earnings we use Worldscope annual earnings. Analyst forecast information internationally is obtained from IBES. Annually rebalanced size portfolios are created using U.S. market capitalization breakpoints by sorting all stocks listed on NYSE, AMEX, and NASDAQ into three market capitalization portfolios. Each non-u.s. firm s market capitalization is converted into U.S. dollars using spot exchange rates from Datastream and is sorted into one of the US-size portfolios based on its December-end market cap. B. Country-level Variables Developed or emerging classifications are based on World Bank income classifications in November Country-level variables are primarily taken from the World Bank Development Database or the World Economic Forum s 1999 Global Competitiveness Report (GCR). This source aggregates hard data and also reports the results of a survey of over 4,000 executives in 59 countries. The book reports an average response for each country and question asked. A key GCR variable of interest is a survey question that asks executives if insider trading in your country s stock markets is (1=pervasive, 7=extremely rare). We aggregate the responses in the editions from 1999, 2000, and , but the World Economic Forum seemed to have stopped asking the question in more recent surveys. We also use and extend the Bhattacharya and Daouk (2002) survey for those countries in our sample that had not yet prosecuted a case of insider trading as of their sample in December We contacted the 46 exchanges and regulators for markets covered by either Datastream or FactSet and we received definitive answers about the first enforcement of insider trading laws from 24. We received no conclusive responses from the remaining 22 countries, so in these cases we use the anti-insider trading enforcement dates found by Bhattacharya and Daouk (2002). C. Earnings Announcement Dates

13 We choose earnings announcements as our main news event because we seek to investigate a common event across countries, as well as an event with a standardized data source. Because firms in many countries only announce earnings annually, for consistency we only examine reactions to annual earnings announcement dates in all countries. C.1. Data Collection procedure For our international earnings announcement dates, we start with announcement dates from Bloomberg. We also collect earnings announcements dates from IBES. We first examine the accuracy by managing research assistants to check a sample of all events for five firms in each country in our sample. In order to examine the accuracy of these databases, we begin in Appendix Table A1, by matching and comparing all annual earnings announcement dates from IBES and Bloomberg from 1994 through 2005 where both IBES and Bloomberg have announcement dates for the same fiscal year. The count column shows the number of matched events. The next shows the number of times the event dates were identical in both datasets. IBES Earlier is the count of the number of times the IBES announcement is earlier than the Bloomberg date. More Bloomberg dates are earlier (BB Earlier) in all countries except Colombia. If dates are not falsely reported early, then Bloomberg dates appear to be much more accurate than the dates provided by IBES. In fairness to IBES, they do not claim to provide announcement dates. They instead provide the date the data were entered into their databases. In the U.S. the entry dates are the announcement dates, but this does not appear to be true around the world. To further investigate the database accuracy we sample five firms per country and examine the accuracy of each and every event announcement for each of the five companies per country by comparing these announcement dates to what we find when we search Factiva by hand. Table AII reports that of the 1346 events checked and find that Bloomberg are accurate 75 percent of the time

14 in developed markets and 3 percent of the time in emerging markets. By contrast IBES dates are accurate only 8 percent of the time in emerging markets and 23 percent of the time in developed. In unreported results we restrict the sample to only the dates where IBES and Bloomberg match within 3 days of each other. This reduces the sample to only 201 dates over all, but the accuracy jumps to 91%. 2 For this reason, we start with Bloomberg as our primary source even though we use IBES dates for cross-checking. Even though we are able to collect Bloomberg announcement dates back to 1994, we begin our sample in January 2001 since there have been significant changes over the last ten years over which the speed and manner in which information is released and reported. Our last earnings announcement date is on October 12, For U.S. firms, this sample consists of IBES dates confirmed by Compustat. To further check our international sample, we collect dates through Factiva as well. We took a sample of fifty firms in each of the developed markets and half of all emerging market firms (at least 100 per market where available) and downloaded all Factiva earnings news articles for these firms. Because of the large volume of articles we automated the date confirmation process for this sample. To begin we required articles be tagged as earnings related by Factiva. Then we checked to see if any article has a number within five percent of the actual earnings figure (as reported by Worldscope). If the publication date of such matched article from Factiva is within ±3 days of the date reported by Bloomberg, we use the earlier of these two dates as our announcement date and label the event as crossed with Factiva. We also construct a combined sample that we use for most of our analysis that is simply the combination of the Bloomberg date that were either verified by IBES or Factiva. If a Bloomberg date was verified by Factiva but we find an IBES announcement 2 These calculations are based on the assumption that the research assistant accuracy is 100 percent for the hand checked sample. The accuracy rate of the combined Bloomberg plus IBES sample could likely exceed 91 percent.

15 that is earlier then we exclude the event in the combined sample. To avoid imprecise inferences, we also require that each country have at least 20 earnings news events over our combined sample to be included in any analysis. C.2. Sample Summary Statistics Panel A and B of Table 1 shows summary statistics for both developed and emerging market earnings announcements. Panel A first displays the number of firms with Datastream data and the number of these firms that match to Bloomberg and where Bloomberg has earnings dates. In developed markets, the average developed market has 1,151 common equity firms on Datastream and 854 of these firms have Bloomberg announcement dates. The average emerging market has 725 firms with 543 having Bloomberg dates. On average, firms have slightly more than six annual earnings announcement for a average of 5,245 unverified announcement dates in developed markets and 3,313 in emerging markets. We are only able to check a limited set of dates with Factiva. Because the IBES crossed with Bloomberg gathers such a large sample in most developed markets, we only check a smaller sample of fifty firms from Factiva in Developed markets and end up with data for an average of 17 firms and 42 events in each developed market. For emerging markets we sample a much larger set of firms and end up with 164 firms and 277 events on average in each market. The combined data includes the Bloomberg and IBES intersection this drastically increases the sample in developed markets. On average, there are 657 firms with data coverage in developed markets and 2,470 events. In emerging markets there are on average 260 firms and 659 events. There are four emerging markets with less than 50 combined events (Egypt, Hungry, Pakistan, and Peru). While this may seem like a small number of events, it is not when compared to the previous literature. Bhattacharya et al. (2000) s sample consisted of 19 earnings events and 75 total events in Mexico. In contrast, our sample of 118 Mexican earnings announcements has a

16 similar number of total announcements and six times the number of earnings announcements. DeFond, Hung, and Trezevant (2007) do not even have Mexico, and many other emerging markets in their sample. Panel C presents the percent of the Factiva sample that occurs each year. Most of the sample occurs after We suspect the low rate of earnings matching prior to 2004 is due to the infrequent use of the earnings tag in this part of the sample. D. Merger Announcement Dates The sample of merger announcements is comprised of data from three sources: SDC, FactSet, and Bloomberg. While there is significant overlap among sources, each source contains some events that are not present in either of the other two. The sample is restricted to initial bids; any bid for a target in any one of the databases within two years of a previous bid is thrown out. The announcement date used for this study is the first date from among the three sources. Panel D of Table 1 reports the number of merger events for which there was data in each country. The number of events per country ranges from a maximum of 807 in the U.K. to a minimum of 2 in Luxembourg. The small number of events in some countries make accurate estimation of the associated country effect difficult. So that our findings are not driven by imprecise measurement, if any country has less than ten merger announcement dates it is excluded from the sample. The sample consists of 38 countries with over ten merger dates as compared to 42 countries with over 30 earnings announcement dates. On average the 22 developed markets have 188 events per country and the 16 emerging markets have 61 events per country. The merger sample is significantly smaller than the earnings sample yet still a significant sample size of 5,114. There are only 3,943 events in this sample.

17 III. Earnings Event Reactions Across Countries We wish to examine whether significant news is disseminated on the announcement day. Since no study has comprehensively examined the stock price reaction around news announcements for a broad cross-section of emerging market countries, we do not know if the Bhattacharya et al. (2000) case of Mexico in the early and mid 1990s is the exception or the norm. Given that the amount of trading in most emerging markets has increased dramatically since the late 1990s, there are reasons to think that responses of stock price reaction to news may have changed since , even in Mexico. Here we first outline our main methodology for examining event reactions and then we turn to examining event reactions around the world. A. Preliminaries Even though positive earnings news is typically accompanied by a positive stock price reaction, our concern is whether information released is concentrated around news events, and not the direction of the news itself. Hence, we focus our analysis on announcement volatility, although in later analyses we will aloes look at signed announcement returns. Reactions are most commonly based on Dimson-beta adjusted abnormal returns (Dim. Adj.) although we check our main inferences with market-adjusted abnormal returns and find that they are similar. The market factor in both models is the value-weighted local stock market return since a local model is shown by Griffin (2002) to lead to more precise return forecasts than more complicated global or international models. Betas are calculated following Dimson (1979), using three leads and lags of the value-weighted local market return. The reaction statistics are composed of two parts: event volatility and normal volatility. Event volatility is the mean absolute abnormal return over the [-1, 2] event window relative to the earnings announcement date. Normal volatility is the mean absolute abnormal return during the [-55, -2] and [3, 55] windows. Normalized volatility

18 is event volatility divided by normal volatility and has intuitive appeal in that it measures absolute event returns in proportion to absolute returns outside of the earnings window. If the two periods are equivalent, the ratio will take a value of one. Differenced volatility is event volatility minus normal volatility. To assess significance, we use a non-parametric rank-deviation test for differences in abnormal absolute returns, first proposed by Corrado (1989) and implemented by Bhattacharya, el al (2000). Importantly, we wish to avoid capturing non-reactions which are due to stock price illiquidity or the fact that a stock does not trade. For this reason, in order to be included in any of our samples, we require a stock to exhibit at least 50 days of non-zero returns or volume during the period of -250 to -126 prior to the event. B. Event Reaction Results Table II first reports normalized volatility at the country-level and by size portfolio with Panel A for developed markets and Panel B for emerging for our combined sample. Size portfolios are based on prior December NYSE/AMEX/NASDAQ tercile breakpoints. First, Table II shows that country reactions vary widely across countries. Second, there are typically relatively small differences in reactions across size portfolios. Yet, in many developed markets there is a slight tendency for reactions to be larger among larger firms. For example, in small developed markets the average event reaction is 1.44 in small firms and 1.57 in large firms. In emerging markets the reactions for small firms (1.27) is slightly larger than that for large firms (1.21). Third, there is little difference in the Dimson market-model adjusted ratios as compared to those calculated using a simple return minus the market. Unless otherwise stated, we use abnormal returns calculated with the Dimson measure throughout the paper. Figure 1 ranks all of our countries from highest to lowest volatility event ratio. Developed markets are blue (light) and emerging markets are in red (dark) and significance at the five percent

19 level is indicated by stripes. First, the figure highlights how event reactions vary widely around the world. The U.K. has an event reaction of two, meaning that event volatility is double normal stock volatility, while Mexico has an insignificant event reaction of only Second, the developed markets typically have much higher event reactions. The emerging markets with high event reactions like Hong Kong and Singapore have well developed capital markets and would be thought of as advanced if not for our World Bank classifications. Third, the non-parametric t-statistics for the Figure are also reported in the last columns of Table II and show that eight markets exhibit insignificant reactions (Egypt, Philippines, Thailand, Indonesia, Taiwan, Hungry, Poland, South Korea, and Mexico). Finally, many countries with reactions that are statistically different from one have reactions that are economically close to one and much different from the reaction in most developed markets. For example, Chile, Austria, China, Malaysia, Peru, Greece, and Turkey all exhibit statistically significant reactions between 1.1 and 1.2. In contrast, U.K., Netherlands, Finland, Sweden, Switzerland, France, Belgium, and the U.S. all exhibit reactions that are at least above 1.5 times the volatility on a normal trading day. Panel B of Figure 1 shows the difference in volatility reaction from the event period to the normal period. To facilitate comparison, the ordering is kept the same as in Panel A. While the precise ordering varies somewhat across countries, the Panel shows that the overall ordering is similar with the difference in volatility measure. Markets like Hong Kong and the U.S. exhibit relatively higher measures with the difference in event volatility. The volatility ratio is relatively smaller ratios for these markets because the abnormal returns during normal or the non-earnings window is relatively more volatile. We are not able to tell whether this volatility during the normal period is due to news or private trading days. Conceptually, we prefer the volatility ratio to the volatility difference, because the ratio makes for more intuitive comparisons of abnormal volatility among countries.

20 We now turn to examining how sensitive the volatility ratio is to differences in date confirmation methodology by examining the relation between our sample of Bloomberg dates that is confirmed by IBES as compared to those verified by Factiva. Figure 2 shows a scatterplot of the average volatility ratio for each market where red (dark) circles are for emerging markets and blue (grey) boxes for developed markets. Markets with less than fifty events in the smaller of the two subsample are in boxes and circles that are solid, while countries with more observations are hollow. Panel A of Figure 2 shows that there is a linear relation between the two samples with most countries, particularly those with over fifty events mapping to each other. Overall, the cross-country correlation between the two samples is Additionally, the mean of the two samples is nearly identical with a cross-country sample average of 1.35 for those dates confirmed by IBES and 1.36 for those confirmed by Factiva. If one of the two approaches led to inaccurate dates but the other sample was accurate, then we would expect a large difference between the two subsamples. The strong relation between the two samples provides some support for the accuracy of our dates through both methods. With bad dates from both methods one would obtain no reactions in both countries. However, it would be odd that our dates would be extremely accurate using both methods in most countries but inaccurate using both methods in a few. In Panel B of Figure 2 we examine if the reaction on two earnings dates shifted dramatically through time. There is tight linear relation between the two subperiods and the cross-country correlation between the two samples is If there are shifts in news accuracy or large changes in issues related to the hypothesis we developed in Section I, then we would expect the samples to differ dramatically through time. The tight relation through time provides confidence that the event reactions are relatively accurate and stable through time within a country.

21 IV. Hypothesis Testing We investigate our five hypotheses using several tests and methodologies. A. Earnings and Returns Event reactions may be small in many countries because accounting earnings are of poor or no quality. To investigate this possibility we examine if stock returns are moving in the same direction as earnings information in the course of a year prior to just after the announcement (-230, - 2). Firms are placed into bins based on the ordering of high to low abnormal event reactions based on the ordering of the normalized volatility ratio in Figure 1. We examine the abnormal stock returns over the year for both firms with positive changes in earnings and those with negative changes in earnings. The analysis is similar in spirit to the sorting approach used in the international study of the relation between earnings and returns performed by Alford, et al. (1993). Figure 3 shows that for the combined sample, the relation between news and abnormal returns is strong in all four periods, though stronger in the high reaction countries. Nevertheless, even in the lowest reaction set of countries there stocks with positive news over the year earn five percent abnormal returns over the year, whereas countries with negative news announcements earn below 16 percent a more than 21 percent difference in returns. By comparison the difference in returns between positive and negative news firms is over 26 percent in event reaction markets and over 22 percent in the next to highest event reaction market. At first glance one might say that stocks in low reaction countries exhibit stronger reactions to negative earnings news than to positive news this inference is incorrect. Since abnormal returns are calculated based on the returns in the market over the year, the benchmark itself in may contain many positive earnings firms. Over our 2001 to 2007 period the emerging markets in the low reaction countries experienced much stronger growth in earnings and returns than those in developed markets. This would be reflected in positive market returns in each market. Panel B shows return differences between those with positive and

22 negative earnings changes over the year for firms in the sample verified by Factiva ( ). In sum, both for the combined and Factiva-only sample earnings are tightly linked with stock returns in both high and low reaction markets providing evidence against the hypothesis that low reactions are due to low earnings quality. We also turn to examining an event that does not rely on accounting information takeovers. Table III displays diagnostics of target firm abnormal event reactions on the announcement day. These results are displayed by size portfolio, as well as with both market and Dimson (1979) adjusted returns. One country that stands out is Norway where the average abnormal volatility on takeover announcement days is 14 times that of a normal day. The average developed market has an event reaction of 2.19 as compared to an event reaction of 3.59 for emerging markets. Figure 4 plots the relation between our combined earnings sample to the reaction of takeovers. As should be expected, takeover reactions are much larger than earnings reactions. For the most markets there is a clear relation between the abnormal takeover reaction and the abnormal earnings reaction. Across countries there is a correlation of 0.57 between event reactions and earnings reactions. This again provides evidence that cross-country patterns in earnings event reactions are not primarily driven by differences in accounting conventions across markets. Nevertheless, we will also further examine the relation between accounting variables and crosscountry reactions later in our cross-sectional regression analysis. B. Inattentive Investors and the Information Environment We next examine whether investor inattention or a low quality information environment is partially responsible for slow or no reactions in low reaction countries. If earnings are meaningful, but not incorporated upon the announcement, then one should see post-earnings announcement

23 drift [Ball and Brown (1968)]. Griffin, Kelly, and Nardari (2008) find substantial drift in both developed and emerging markets but do not look at the relations between event reactions and drift. We calculate earnings surprises as the difference between the actual reported earnings per share and the mean analyst earnings per share forecast. We include only the last forecast for each analyst at least 14 calendar days and not more than 182 calendar days before the reporting date. This difference between actual and expected earnings is then scaled by price six calendar days prior to the reporting date in order to normalize across different firms. Within a country, we sort events into the positive (negative) portfolio if SUE is in the top (bottom) 60 percent of positive (negative) SUEs. We first wish to see if returns over the three-day event window are in the same direction as the earnings surprise. Panel A of Figure 5 shows that for firms with positive earnings surprises the relation monotonically declines across portfolios from 1.9% in the high reaction country to 0.3% in the low reaction country. For negative earnings surprises there is not a monotonic relation due to only a small negative surprise in the next to highest reaction group of countries. Nevertheless, there are large differences across groups, with firms in high reaction countries exhibiting negative returns of 1.6% in high reaction countries and only 0.6% in low reaction countries. Panel B also shows similar patterns for the part of the sample that is verified with Factiva though the differences are not significant in the high reaction countries, likely due to the scarcity of observations here since many more firms were sampled in emerging markets. The main point from these figures is that inferences using earnings surprises yield a consistent picture as those from using volatility ratios. Having established this fact, we turn to measuring post-event price movements. Panel B shows that there is post-earnings announcement drift in the period from 3 to 55 trading days following the earnings announcement. The difference between the average returns to positive earnings announcements minus the return from negative earnings announcements is 0.8% percent in high reaction markets and 2.0% in low reaction markets. For the Factiva sample there is

24 actually a smaller difference between positive and negative post-earnings drift in the low reaction market than in many of the other groups. Panel C examines drift in the window of 56 to 125 trading days following the announcement. Here there is little drift in high reaction countries but substantial negative drift (over three percent) in low reaction countries. Overall, the results from Panels C and D show some more drift in low reaction countries, although the differences are not large. To sum up we find only weak support for the inattentive investors or poor news environment hypotheses. In a later section we use crosssectional regression analysis to disentangle these competing hypotheses. C. Pre-Announcement Trading through Public or Private Information We next examine whether there is pre-announcement information leakage in the same direction as the earnings surprise. Panel D of Figure 5 shows that in the combined sample there is more trading in the direction of positive earnings surprise events in the prior period [-55, -2] in all four reaction portfolios, though the pre-announcement returns are largest in the low reaction sample (5.1 percent return). Interestingly, however, firms with negative earnings surprises also have positive returns in the low reaction sample (3.1 percent). The result is that the pre-announcement drift is similar in high and low reaction countries. The sample that is verified with Factiva also yields similar inferences. In Panel E we also examine post-announcement trading in the period from [-125, -56]. Here we find that in low reaction countries high earnings stocks outperform low earnings stocks by over seven percent whereas in high reaction markets the difference is only over three percent. This is consistent with information leakage occurring earlier in low reaction countries. However, the results are not as strong with the smaller Factiva verified sample. For low reaction firms, since earnings typically move in the same direction as the analyst forecast and in similar or greater magnitude to that in high reaction markets in both the pre- and post-announcement period, both the pre- and post-announcement results also provide strong

25 evidence against the first hypothesis that earnings are not moving in the announcement window in low reaction countries due to poor earnings quality. Our results seem to point to significant trading in the same direction as the news announcement which is consistent with some form of information leakage. In Table IV we also exhibit information leakage for takeovers. Pre-announcement leakage is lower in low reaction market. However, the total premium appears to be much lower as well. Takeover targets in low earnings reaction countries exhibit a much lower event reaction (3.5 percent) than those in high reaction markets (17.4 percent) but when averaged across the two prior subperiods ([-126, -56] and [-55, -2]) still exhibit pre-announcement leakage (5.8 percent) which is almost as great as that in the high reaction markets (7.8 percent). The end result is that in low reaction markets 62.4 percent of the total price run-up from [-126, +2] is reflected in the preannouncement period whereas in high earnings reaction markets only 30.9 percent of the total runup is reflected in the pre-announcement period. Unlike with earnings events, with mergers, we see no drift for low reaction markets. In contrast to earnings announcement, merger announcements are unplanned and unscheduled. Hence, the relatively larger stock price increase in the preannouncement period for low reaction countries is suggestive of informed (or insider) trading. In the next section we explore how average event reactions with a country are associated with country-level measures of insider trading to further shed light on whether the lower event reactions associated with information leakage is the result of public or private sources of information. V. Cross-Country Analysis In this section we bring additional evidence to bear on the five hypothesized factors influencing the magnitude of announcement reaction: accounting quality, investor attention and information environment, public pre-announcement information release, and insider trade. We first

26 describe our cross-sectional data, then precede to univariate and multivariate cross-country regressions followed by checking the robustness of our inferences to firm-level controls with panel regressions. A. Cross-Country Data Event reaction characteristics can and do vary across companies, but they have a strong country-level component as well. For example, while companies within a market can have differing levels of accounting quality, base levels of accounting quality are something that is often mandated by rule or law for all firms listing in a market. As such, we may find differences in event reactions across markets as a function of differences in country-wide characteristics. In the following subsections we examine whether a broad set of variables that relate to these characteristics is associated with the size of the event reaction. The variables are taken from a variety of sources and are chosen because they reflect aspects of accounting quality, the information environment, trading activity, insider trading law and practice, laws regarding investor protection and trade, and the level of economic and market development that relate to our hypotheses. Where data availability permits we take the average value of the variable over our 2001 to 2007 sample. 3 We conduct our analysis of the cross-country evidence for our five hypotheses by first examining univariate regressions with event reaction as the dependent variable and proxies for accounting quality, insider trading law and practice, etc. as the independent variables. It is important to note that our regressions are not meant to imply causality but are merely capturing associations between variables. We then progress to bivariate regressions and trivariate regressions in order to explore which of these country-level characteristics is most important in the cross section. 3 We have also collected variables from other papers or from data sources that only cover part of the 2001 to 2007 period, in these cases we use the average value of the data available.