Institutional ownership changes and returns around analysts’ earnings forecast release events: Evidence from Taiwan

Journal of Banking & Finance 30 (2006) 2471–2488 www.elsevier.com/locate/jbf

Institutional ownership changes and returns around analysts’ earnings forecast release events: Evidence from Taiwan An-Sing Chen *, Bi-Shia Hong National Chung Cheng University, Department of Finance, Ming Hsiung, Chia-Yi 621, Taiwan, ROC Received 6 September 2004; accepted 4 July 2005 Available online 20 March 2006

Abstract Traditional data sources do not have institutional holding data on a daily basis. Because of this, most prior empirical studies of institutional herding have focused on quarterly or annual data. The problem, however, with using quarterly or annual data on institutional holdings is that these data may not reveal institutional herding if it occurs over a shorter time interval. For this study, we make use of data from the Taiwan Stock Exchange (TSE). Unlike traditional data sources, the TSE provides daily institutional holdings information. The use of this detailed data allows us to make more interesting analysis and inferences. In this study, we examine the relationship between institutional ownership changes and returns localized around analysts’ earnings forecast release events. Analysis of institutional ownership and return data around the earnings release event allows us to investigate institutional herding and feedback behavior in a different level. Our major results are as follows: (1) there exists a relation between company specific attributes and institutional herding, (2) observed changes in institutional ownership and contemporaneous return are mainly the results of interday price impact of herding, (3) institutional investors show evidence of being informed traders in buying but not selling. Ó 2005 Published by Elsevier B.V. JEL classification: D40; G10; G19 Keywords: Institutional ownership; Analysts’ earnings forecast; Herding; Feedback trading

*

Corresponding author. Tel.: +11 886 5 242 8246; fax: +11 886 5 272 0818. E-mail address: fi[email protected] (A.-S. Chen).

0378-4266/$ - see front matter Ó 2005 Published by Elsevier B.V. doi:10.1016/j.jbankfin.2005.07.016

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1. Introduction Herding is a group of investors trading in the same direction over a period of time. Feedback trading involves correlation between herding and lag returns and is a special case of herding that results when lag returns, or variables correlated with lag returns act as the common signal. Herding and feedback trading have the potential to explain a number of financial phenomena. Yet, the bulk of prior research on institutional herding focus only on US data (Grinblatt et al., 1995; Nofsinger and Sias, 1999; Wermers, 1999; Sias et al., 2001). While these studies have frequently provided important insights on US markets and US institutional shareholders, the applicability of these findings to other markets is questionable. Furthermore, institutional and environmental differences among countries allows for analysis of different aspects of institutional herding (Choe et al., 1999; Kim and Nofsinger, 2005, for example). The stock market in Taiwan provides us with the opportunity to study institutional herding on a different level. The Taiwan Stock Exchange (TSE) provides daily institutional holdings information of institutional investors in Taiwan. This allows us to examine the relationship between institutional ownership changes and returns localized around analysts’ earnings forecast release events. By localizing our analysis around the analysts’ earnings forecast release event, we can address three important questions. (1) What is the relation between company specific attributes and institutional herding? (2) Are the observed changes in institutional ownership and contemporaneous return the results of intra-period positive feedback trading or the price impact of institutional herding? (3) Are institutional investors informed traders? The Taiwan stock market also differs from other developed stock markets in the degree of institutional equity ownership. For example, in the US, institutional investors account for more than 50% of total equity ownership (Bennett et al., 2003), while institutional ownership in Japan is about 63% (Kim and Nofsinger, 2005). Institutional investors in Taiwan, on the other hand, owned less than 30% of the outstanding shares while individual investors owned approximately 70% of the total equity ownership. Thus, analysis of this stock market allows us to improve our understanding of market environments where institutional investors, while still a big part of the market, are not the majority participants. It should also provide some insights with respect to the relationship between the level of institutional equity ownership and institutional trading behavior. Theories explaining why institutional investors might trade together range from reputation concerns (or agency problems), informed trading, informational followers, aversion to certain stocks, positive feedback trading (also known as momentum investing or trend chasing), irrational psychological factors to window-dressing strategies. Managers may disregard their private information and trade with the crowd due to the reputational risk of acting differently from other managers. For example, managers may rationally choose to focus only on information that pays off in the short term and ignore valuable information that may take a long time to be impounded into the price (Froot et al., 1992). That is, if managers are concerned about their reputations in the labor market, her/his reputation is hurt less if everyone makes the same bad decision than if only she/he makes the bad decision. Scharfstein and Stein (1990) observe that it is possible that even if the manger has information that a contrarian strategy has a higher probability of being correct, being risk averse, she/he may choose to run with the herd instead of going out on a limb and utilizing the contrarian strategy.

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Alternatively, managers may trade together simply because they receive correlated private information, perhaps from analyzing the same indicators (Froot et al., 1992; Hirshleifer et al., 1994). Recent studies reveal that measures of institutional demand are positively correlated with subsequent returns (Grinblatt and Titman, 1989, 1993). Extant studies also suggest that at least some of the correlation could be explained by institutional investors’ ability to forecast returns (Daniel et al., 1997; Wermers, 1999; Nofsinger and Sias, 1999; Chen et al., 2001). In another explanation related with information, if the information managers have is revealed sequentially, herding could also occur. Existence of informational followers has also been offered as an explanation. Mangers may infer private information from the prior trades of better-informed managers and trade in the same direction (Bikhchandani et al., 1992). Furthermore, managers may also observe the trades of others due to an information leakage by brokers or the managers themselves (particularly after that manager’s trade package is completed; Froot et al., 1992). For example, the results of Irvine et al. (2004) investigating the trading behavior of institutional investors immediately prior to the release of analysts’ initial buy and strong buy recommendations suggest that some institutional traders receive tips regarding the contents of forthcoming analysts’ reports. Similarly, Fong et al. (2004) using a database of daily trades and monthly portfolio holdings of active Australian equity managers also find evidence that brokers facilitate information transfer between managers resulting in a substantially higher level of herding. Several studies have documented substantial correlations between institutional ownership and certain share characteristics such as market capitalization, liquidity, and share price (Badrinath et al., 1996; Del Guercio, 1996; Falkenstein, 1996; Gompers and Metrik, 2001). For example, institutional investors may share an aversion to stocks with certain characteristics, such as stocks with lower liquidity or stocks that are less risky (Falkenstein, 1996). Recent empirical works present somewhat stronger evidence that institutional investors engage in some positive feedback trading strategies (Lakonishok et al., 1992; Grinblatt et al., 1995; Wermers, 1997, 1999; Nofsinger and Sias, 1999; Cai et al., 2000; Griffin et al., 2003). That is, institutional investors tend to purchase (sell) stocks that performed well (poorly) in the recent past. Institutional positive feedback trading strategies could result in herding. Lastly, institutional herding could also result from irrational psychological factors or window-dressing strategies. Several authors (Friedman, 1984; Dreman, 1979) suggest that institutional herding can result from irrational psychological factors and cause temporary price bubbles. Lakonishok et al. (1991) posit institutional herding may result from window-dressing strategies. Institutional herding may or may not destabilize stock prices. It is possible that institutional herding may be primarily responsible for large price movements of individual stocks, and, moreover it may destabilize stock prices. In many existing studies (i.e., Froot et al., 1992; Scharfstein and Stein, 1990; Banerjee, 1992; De Long et al., 1990) dealing with the herding behaviors of institutions, the herding results in an inefficient equilibrium, where the private information that the institutions may have is not fully reflected in the observable market prices. On the other hand, if institutional investors are better informed than individual investors, institutional investors will likely herd to undervalued stocks and away from overvalued stocks (Lakonishok et al., 1992). Several authors (Froot et al., 1992; Bikhchandani et al., 1992; Hirshleifer et al., 1994) also note that such herding can move prices toward, rather than away from, equilibrium values.

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2. Data and methodology For this research, we utilize daily data from Taiwan Economic Journal (TEJ) data bank rather than quarterly or annual data used by prior studies. To study institutional herding around specific informational events such as earnings forecast, we must synchronize institutional daily holdings and ownership changes. The use of this detailed data allows us to localize the analysis of institutional behavior on an information release and to make more interesting analysis and inferences on institutional herding. Using this data has several advantages over data used previous related research. First, traditional data sources do not have institutional holding data on daily basis. In most existing studies using US data, the number of shares held by institutional investors is usually gathered from sources such as CDA Spectrum (quarterly), Morningstar (monthly) or Standard and Poors’ Security Owners’ Stock Guides. For example, many of the latest studies on institutional ownership use CDA Spectrum, and it is derived from institutional investors’ 13f filings. (All institutional investors with more than $100 million in equity ownership must report their holdings to the Security and Exchange Commission (SEC) in quarterly 13f filings.) Although CDA Spectrum reports the name of the institutional holder, most recent trade, and the number of shares held by each institution for all publicly traded companies, it is available only on a quarterly basis. Data gathered in this manner though sufficient for explanatory research has limitations if results are to be used for event studies, which reflect related information rapidly. Thus, much of the interesting findings using these ‘‘dated’’ institutional ownership data cannot be used in measuring the effect of specific events over very short-term period (e.g., just several days). The limitations of quarterly and monthly holdings data set prevent us from making conclusive statements about whether institutional herding, for example, destabilizes daily stock prices. Most prior studies focus on the herding behaviors of mutual funds (Grinblatt et al., 1995; Wermers, 1997, 1999) or pension funds (Lakonishok et al., 1992). Our detailed data allow analysis of institutional investors around an information release event. Additionally, the daily data on institutional ownership changes allows us to distinguish between the hypotheses of intra-period positive feedback trading versus price impact of herding (price pressure). We examine all days between January 1, 2001 and December 31, 2003 when analysts announce the predicted earnings. These days are referred to as ‘‘event-days’’. That is, the event day (t = 0) is defined in this study as the day when analysts’ earnings forecast for a particular company is released. Our sample consists of all analysts’ earnings forecast events for firms in the Taiwan Stock Exchange (TSE). The data consist of individual daily returns, Taiwan Stock Exchange Capitalization Weighted Index (TAIEX), institutional ownership information and related financial data. The fraction of shares held by institutional investors at analysts’ earnings forecast event day (t = 0) is defined as the ratio of the number of shares held by institutions to the total number of shares outstanding. The number of firm-event observations is 3544 in 2001, 4153 in 2002 and 3644 in 2003, for a total of 11,341 firm-events. Firms are sorted into 10 portfolios based on the fraction of shares held by institutional investors at the beginning of analysts’ earnings forecast event day (t = 0). The firms in each initial institutional ownership decile are then further sorted into 10 portfolios based on the change in the fraction of shares held by institutional investors over the event period (t = 0 to 1) for a total of 100 initial institutional ownership, change in institu-

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tional-ownership-sorted portfolios. Firms are then re-aggregated based on their change in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, etc. This sorting procedure can create 10 portfolios with similar institutional initial ownership at the analysts’ earnings forecast day (t = 0) and large differences in the institutional ownership changes over the event period (t = 0 to 1). 3. Empirical findings 3.1. Characteristics of institutional-ownership-change portfolios Table 1 presents computed statistics for institutional ownership and change in institutional ownership for firms in each ownership change portfolio. The last second column presents an F-statistic for the null hypothesis that the characteristic does not differ across the ownership change portfolios. The last column shows the difference of the characteristic between large increase and large decrease portfolios. The results demonstrate that the portfolios exhibit similar levels of initial institutional ownership (about 10%) but vary significantly (at the 1% level) in their change in ownership – the change averages 0.77% for firms in the first portfolio (large decrease) versus 0.95% for firms in the last portfolio (large increase). In Table 1, we compute beta using the firm’s daily returns with the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) for the period from t = 200 to 20. The computed F-statistic for beta is 6.29 and is significant at the 1% level, showing beta to be different across the constructed institutional ownership decile portfolios. The betas of the extreme decile portfolios with large increase and large decrease in institutional ownership are larger than the betas of the middle decile portfolios. The next two rows in Table 1 report the size and ROE, respectively, for firms in each portfolio. Size is measured by the mean natural logarithm of the market value of equity over the event period. The variable ROE is expressed as net income divided by equity using annual data (in percent notation). Firms in the higher decile portfolios are generally larger than firms in the lower decile portfolios, but it is not monotonic. The ROEs of the decile portfolios are found to be significantly, at the 1% level, different from each other. We find that the ROEs of the extreme decile portfolios are on average larger than the ROEs of the other portfolios. The price earnings (PE) ratio is measured by the mean event period daily price divided by the prior fiscal year earnings per share (EPS). Turnover is mean daily volume expressed as a percentage of shares outstanding over the period from t = 200 to 20. StdRtn (standard deviation of returns) exhibits the mean standard deviation of daily returns over the period from t = 200 to 20. The PE ratio of the decile portfolios is not significant, however, turnover and StdRtn are found to be significantly different (at the 1% level). Firms in the extreme decile portfolios show higher turnover. Results are consistent with the premise that institutional trading helps explain why markets in general have exhibited greater liquidity in recent years (Bennett et al., 2003). We also find that the StdRtns of the extreme decile portfolios are on average larger than the StdRtns of the other portfolios, showing that institutional trading (both in buying and selling) is associated with stock volatility. Number of analysts (# of analysts) and analysts’ forecasted EPS of the decile portfolios are found to be significantly different. The number of analysts represents the mean number of analysts that make earnings forecast for a specific firm in one year. Forecasted EPS is

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Table 1 Characteristics of institutional ownership change portfolios Large decrease

Decile 2

Decile 3

Decile 4

Decile 5

Decile 6

Decile 7

Decile 8

Decile 9

Large increase

F-stat.

10.71 0.77

10.56 0.26

10.62 0.13

10.60 0.06

10.68 0.01

10.65 0.02

10.67 0.05

10.55 0.11

10.49 0.27

10.80 0.95

0.03 1101.63*

0.99 8.48 7.98% 24.92 2.25 3.65 4.74 3.33 1.23 4.35% 33.40

1.00 8.76 5.96% 27.66 1.89 3.52 4.71 2.87 1.03 4.08% 38.03

0.98 8.79 2.42% 26.15 1.69 3.42 4.66 3.10 1.00 4.18% 43.85

0.92 8.86 6.18% 16.12 1.39 3.24 4.62 2.85 0.83 4.48% 42.80

0.92 8.78 2.74% 18.74 1.29 3.16 4.41 2.49 0.74 3.61% 49.00

0.90 8.71 3.74% 22.00 1.27 3.14 4.37 2.40 0.85 4.86% 49.00

0.88 8.89 6.32% 17.53 1.29 3.20 4.43 2.99 0.84 3.63% 45.89

0.92 8.99 5.82% 24.76 1.48 3.31 4.65 3.28 0.73 2.82% 60.28

0.95 8.86 7.78% 18.51 1.71 3.45 4.62 3.26 1.06 3.47% 71.04

0.97 8.65 8.73% 18.04 2.13 3.59 4.70 3.18 0.75 2.30% 84.37

62.79

60.04

55.58

50.89

53.19

62.00

59.12

62.85

66.41

70.81

6.29* 6.34* 3.74* 0.52 35.34* 24.83* 9.41* 5.59* 2.60* 2.51* – –

Inc.  Dec. 0.09 1.72* 0.02 0.19** 0.75% 6.88 0.12 0.06 0.04 0.15 0.48* 2.05%* – –

Firms are sorted into 10 portfolios based on the fraction of shares held by institutional investors at the beginning of analyst’s earnings forecast event day (t = 0). The firms in each initial institutional ownership decile are then further sorted into 10 portfolios based on the change in the fraction of shares held by the institutional investors over the event period (t = 0 to 1) for a total of 100 initial institutional ownership, change in institutional-ownership-sorted portfolios. Firms are then reaggregated based on their change in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, etc. Initial ownership is the fraction of shares held by institutional investors at the beginning of analyst’s earnings forecast event day (t = 0). Ownership changes are the change in the fraction of shares held by the total institutional investors over the event period (t = 0 to 1). Beta is the systematic risk, computed using firm’s daily returns with Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) for the period from t = 200 to 20. Size is the mean natural logarithm of the market value of equity over the event period. The variable ROE is expressed as net income divided by equity using annual data (in percent notation). PE ratio is measured by the mean event period daily price divided by the prior fiscal year EPS. Turnover is mean daily volume expressed as a percentage of shares outstanding over the period from t = 200 to 20. StdRtn is the mean standard deviation of daily returns over the period from t = 200 to 20. # of analysts is the mean number of analysts that make earnings forecast for a specific firm in one year. Forecasted EPS is the mean forecasted EPS for a specific firm by analysts. Earnings surprise is measured by the mean difference between the actual EPS and forecasted EPS. Relative surprise is the earnings surprise divided by stock price (in percent notation). % of positive return in each decile is computed as the percentage of the number of firm’s positive returns divided by total number of returns over the period from t = 0 to 1. % of up market is the percentage of the positive TAIEXs. The F-statistic is based on the null hypothesis that the means do not differ across the portfolios. Inc.  Dec. is the difference between the extreme large increase and large decrease portfolio. * and ** indicate statistical significance at the 1% and 5% levels, respectively.

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Initial ownership Ownership changes Beta Size ROE PE ratio Turnover StdRtn # of analysts Forecasted EPS Earnings surprise Relative surprise % of positive return % of up market

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the mean forecasted EPS for a specific firm by analysts. We also find that firms in the extreme decrease and large increase portfolios have higher number of analysts and analysts’ forecasted EPS. Given that in general the more analysts’ coverage, the less information asymmetry, these results show that institutional investors tend to trade in the firms with low degree of information asymmetry and better analysts’ prospect. We also measure earnings surprise by the mean difference between the actual EPS and forecasted EPS. Relative Surprise is the earnings surprise divided by stock price (in percent notation). We find the earnings surprises and relative surprises across all portfolios are negative. Results further show that the forecasted EPS are always larger than real EPS, suggesting that analysts are inclined to be over-optimistic. As noted by Hong et al. (2003), analysts are generally rewarded for trade generation rather than for the accuracy of recommendations. It is not surprising that analysts are always inclined to be over-optimistic. Also, O’Brien (1998) and Klein (1990) report that analysts tend to be overly optimistic in their early forecasts. The earnings surprises and relative surprises of the lower decile portfolios are on average larger than those of the higher decile portfolios. Results show that institutions tend to prefer high EPS firms and seem to have some ability to differentiate ex ante between stocks with just predicted high EPS from stocks with realized high EPS, suggesting some stock selection ability by institutional investors, and a general shifting of money out of higher (both earnings surprises and relative surprises) surprise stocks into lower surprise stocks during the event period. Gibson et al. (2004) find similar institutional investor behavior in the seasoned equity market. Their results show that seasoned equity issuers experiencing the greatest increase in institutional investment around the offer date have better performance relative to those experiencing the greatest decrease. They interpret the results as evidence that institutions are able to identify above average seasoned equity offering (SEO) firms at the time of equity issuance and increase their holdings in these potential outperformers. The last two rows in Table 1 report the percentage positive return (% positive return) and percentage up market (% up market), respectively, for firms in each portfolio. Percentage positive return in each decile is computed as the percentage of the number of firms with positive returns divided by total number of firms over the period from t = 0 to 1. Similarly, percentage up market is the percentage of sample occurring when change in TAIEX was positive. Firms in the higher decile portfolios exhibit higher percentage positive return. The higher percentage positive return, however, cannot be completely explained by the common market factor. For example, the percentage positive return for the large decrease portfolio is 33.40%, but its corresponding percentage of up market measure is a whopping 62.79%. In unreported analyses, we find the cumulative institutional ownership changes in the day 30 to 18 windows are gradually increasing. It shows that the institutional investors have begun to adjust their positions before the earnings forecast release event day and that their ownership ‘‘drift’’ in the increasing direction subsequent to these announcements. This has interesting implications in that it suggests not all trading by institutions had been done prior to the event day and there are still noticeable adjustments made by institutions subsequent to the event day. Taken as a whole, we find institutional investors generally herd to larger, lower surprise (a smaller overestimation of the EPS) stocks and away from smaller, higher surprise (a larger overestimation of the EPS) stocks around the analysts’ earnings forecast event period.

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The evidence shows that institutional investors herd, both in buying and selling, to higher systematic risk, higher ROE, more liquid and more volatile firms. In addition, results suggest that institutional investors seem to exhibit greater ability in stock selection than analysts. 3.2. Institutional ownership changes and returns Table 2 presents the earnings forecast release event period returns, pre- and post-event returns, respectively. We focus on the buying and selling the same stock over the event period by institutional investors and the relationship between the change in ownership and returns. Results indicate that analysts’ earnings forecast release events affect institutional ownership and that institutional investors react to analysts’ earnings forecast release events. We find a pronounced positive relationship between event period return and changes in institutional ownership, also a positive relationship between pre-event period return and ownership changes. On the other hand, we find no monotonic relation between ownership changes and post-event return. The positive relation between ownership changes and lag return is consistent with the premise that institutional investors are positive feedback traders (Lakonishok et al., 1992; Grinblatt et al., 1995; Wermers, 1997, 1999; Nofsinger and Sias, 1999; Cai et al., 2000; Griffin et al., 2003). It is also similar with the findings documented by Irvine et al. (2004) showing that institutional buying prior to analysts’ recommendation earn positive abnormal trading profits. The positive relation between ownership changes and contemporaneous return is consistent with the evidence suggested by Griffin et al. (2003) who find that there is a strong contemporaneous relation between changes in institutional ownership and stock returns at the daily level and is consistent with two hypotheses: (1) intra-event period positive feedback hypotheses, (2) price impact of herding (price pressure). To further compare the relative importance of the relationships between changes in institutional ownership and returns over the pre-event, event, and post-event interval, we estimate the following regressions in Panel A of Table 3: Changei;event ¼ a þ b1 Changei;pre þ b2 ARi;pre þ b3 ARi;event þ b4 ARi;post þ ei;event .

ð1Þ

Eq. (1) is a regression of institutional ownership changes over the event period (t = 0 to 1) on pre-event ownership changes, pre-event, contemporaneous and post-event abnormal returns. Changei,event represents the changes in institutional ownership over the event period (t = 0 to 1) for stock i. Changei,pre is the changes in institutional ownership over the pre-event period (t = 2 to 1) for stock i. ARi,pre, ARi,event and ARi,post is the market model adjusted pre-event, event, and post-event abnormal returns, respectively. In Panel A of Table 3, we find b3 to be highly significant, and it has, on average, more influence on the dependent variable than b2 and b4. In other words, the relationship between institutional ownership changes and event period abnormal return is stronger than both its pre- and post-event relationships. Although institutional investors may observe the preevent returns and take some positive feedback trading strategy around the event period, we find the changes in institutional ownership are mainly related with the contemporaneous returns. There are two possible explanations for the positive relation between ownership changes and contemporaneous return. Specifically, it may result from intra-event period positive feedback trading and/or price impact of institutional herding (price pressure). We will distinguish these explanations in next section.

Table 2 Institutional ownership change portfolios and return Large decrease

Decile 2

Decile 4

Decile 5

Decile 6

Decile 7

Decile 8

Decile 9

Large increase

0.54* 0.63*

0.44* 0.41*

0.21 0.07

0.06 0.24***

0.05 0.35*

1.05* 0.55*

1.93* 1.33*

4.67* 3.97*

Panel B: Pre-event Abnormal return t = 2 to 1 t = 4 to 3 t = 6 to 5 t = 9 to 7 t = 19 to 10 t = 30 to 20 t = 10 to 1 t = 20 to 1 t = 30 to 1

0.59* 0.12 0.28 0.44** 0.55 0.48 1.22* 0.76 0.29

0.28*** 0.19 0.64* 0.62* 0.84* 0.41 1.52* 0.74 0.26

0.21 0.40** 0.45** 0.56* 0.11 0.26 1.63* 1.40* 1.00***

0.00 0.27 0.15 0.01 0.77** 0.49 0.32 0.49 0.83

0.26 0.15 0.06 0.23 0.71** 0.04 0.33 0.91*** 0.87

0.20 0.21 0.10 0.02 1.09* 0.21 0.75** 1.36* 1.68*

0.55* 0.24 0.18 0.08 1.84* 1.53* 1.17* 2.54* 3.97*

1.94* 0.75* 0.44*** 1.00* 3.26* 0.95** 4.44* 6.55* 7.43*

0.15 0.34** 0.17 0.07 0.18 0.50 1.16* 1.32* 1.79*

0.39* 0.03 0.03 0.01 0.14 1.04* 0.51*** 0.65*** 1.63*

0.23 0.36** 0.16 0.05 0.33 1.17* 0.85* 1.16* 2.25*

0.02 0.29*** 0.18 0.02 0.05 0.78* 0.48*** 0.51 1.22*

F-stat.

Inc.  Dec.

104.10* 113.64*

6.41* 6.21*

32.21* 6.33* 3.31* 5.71* 6.49* 1.34 30.16* 24.00* 18.97*

3.97* 1.77* 0.42 1.26* 1.93* 0.24 7.22* 8.36* 8.44*

3.79* 0.91 0.82 0.59 0.73 1.00 0.84 0.75 0.57

0.83* 0.42*** 0.05 0.02 0.00 0.61 0.37 0.37 0.17

period return 2.03* 1.02* 0.02 0.26 1.33* 0.71*** 2.78* 1.81* 1.01

Panel C: Post-event period return Abnormal return t = 2 to 3 0.25 t = 4 to 5 0.50* t = 6 to 7 0.28** t = 8 to 10 0.18 t = 11 to 20 0.09 t = 21 to 30 1.47* t = 2 to 10 1.10* t = 2 to 20 0.99** t = 2 to 30 2.36*

0.95* 0.26 0.52* 0.91* 0.56*** 0.32 2.36* 2.02* 1.51**

0.21 0.44* 0.10 0.12 0.26 0.44 1.14* 1.38* 1.78*

0.05 0.39** 0.09 0.07 0.28 0.50*** 0.71** 0.97** 1.44*

0.45* 0.44* 0.06 0.19 0.44 0.88* 0.88* 1.28* 2.12*

0.61* 0.34*** 0.00 0.39*** 0.91** 0.69** 0.95* 1.84* 2.50*

1.08* 0.08 0.23 0.16 0.09 0.86** 1.46* 1.36** 2.19*

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Firms are sorted into 10 portfolios based on the fraction of shares held by institutional investors at the beginning of analyst’s earnings forecast event day (t = 0). The firms in each initial institutional ownership decile are then further sorted into 10 portfolios based on the change in the fraction of shares held by institutional investors over the event period (t = 0 to 1) for a total of 100 initial institutional ownership, change in institutional-ownership-sorted portfolios. Firms are then re-aggregated based on their change in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, etc. Raw return is the continuously compounded return of a stock, defined as the natural logarithm of one plus gross return. Abnormal return is the risk adjusted return using the market model (Ri,t = ai + biRm,t + ei,t where Ri,t is individual return for stock i on day t, Rm,t is concurrent return for TAIEX on day t, and bi is the systematic risk for stock i, computed using firm’s daily returns with TAIEX over the period from t = 200 to 20). The F-statistic is based on the null hypothesis that the means do not differ across the portfolios. Inc.  Dec. is the difference between the extreme large increase and large decrease portfolio. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

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Decile 3

Panel A: Event period return (t = 0 to 1) Raw return 1.74* 1.20* Abnormal return 2.24* 1.52*

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Table 3 Abnormal returns and ownership changes Panel A Changei;event ¼ 0:01 þ 0:26Changei;pre þ 0:01ARi;pre þ 0:06ARi;event  0:00ARi;post þei;event , Adj. R2 = 0.28 ð1:64Þ

ð18:03 Þ

ð6:06 Þ

ð31:06 Þ

ð0:20Þ

Panel B Changei;1 ¼  0:00 þ 0:28Changei;0 þ 0:01ARi;0 þ 0:03ARi;1 þei;1 , Adj. R2 = 0.20 ð0:77Þ

ð25:27 Þ

ð11:49 Þ

ð23:44 Þ

In Panel A, we estimate a regression of institutional ownership changes over the event period (t = 0 to 1) on preevent ownership changes, pre-event, contemporaneous and post-event abnormal returns. In Panel B, we estimate a regression of institutional inter-day ownership changes during t = 1 (the day after the forecast release event day) on previous day’s ownership changes, the same day’s abnormal returns and previous day’s abnormal returns. Fstatistic is based on the null hypothesis that the coefficients do not differ in the regression model of each portfolio. * indicates statistical significance at the 1% level.

3.3. Positive feedback trading and/or price impact of herding? A positive relation between changes in institutional ownership and returns measured over the same period arises if: (1) institutional investors engage in positive feedback trading and/or (2) institutional investors’ herding impacts prices. If positive feedback trading is responsible for the positive relation between changes and contemporaneous returns, the ownership changes should occur after the stock price has changed. That is, daily changes in institutional ownership should be positively correlated with lag returns. Also, if institutional herding could drive returns, the daily changes in institutional ownership should be positively correlated with contemporaneous returns. Recall that Panel A of Table 2 shows a positive relation between ownership changes and event period returns. Firms in the decile experiencing the largest decrease in institutional ownership show statistically significant average abnormal returns of 2.24%. On the other side, firms in the decile experiencing the largest increase in institutional ownership show statistically significant average abnormal returns of 3.97%. Panel A of Table 3 also shows that the contemporaneous relationship between ownership changes and abnormal return is stronger than the prior event relationship. Given that positive feedback trading involves correlation between herding and previous day’s return, but the price impact of herding involves contemporaneous effect, we can define the relative degree of price impact of herding by the relation between changes in institutional ownership and returns over the analyzed event interval. The following regression, therefore, allows us to distinguish between the hypotheses: Changei;1 ¼ a þ b1 Changei;0 þ b2 ARi;0 þ b3 ARi;1 þ ei;1 .

ð2Þ

Eq. (2) is a regression of institutional inter-day ownership changes during t = 1 (the day after the forecast release event day) on previous day’s ownership changes, the same day’s abnormal returns and previous day’s abnormal returns. Changei,1 is the changes in institutional ownership after the event day (t = 1) for stock i. Changei,0 is the total changes in institutional ownership at the event day (t = 0) for stock i. ARi,0 and ARi,1 is the market model risk-adjusted abnormal return at and after the event day, respectively. The results shown on Panel B of Table 3 suggest that the apparent inter-day positive relationship between the change in institutional ownership and returns is mainly contemporaneous with changes in ownership occurring on the same day as price change. When we conduct a regression of institutional ownership changes during t = 0 (the analyst’s earn-

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ings forecast release event day) on previous day’s ownership changes, previous day’s abnormal returns and the same day’s returns (not reported) the results obtained were similar. Taken as a whole, the evidence shows that both herding and feedback effects are observed around analysts’ earnings forecast release events, but the contemporaneous relation between changes in institutional ownership and stock return is stronger than the positive feedback trading effect. Our evidence is consistent with Nofsinger and Sias (1999), Wermers (1999) and Griffin et al. (2003) findings. Results thus far show that the relationship between changes in institutional ownership and returns is on average contemporaneous. To further examine whether positive feedback trading or price pressure is the cause of this relationship, we utilize the intra-day trading relationships between volume and returns and estimate the following regressions: Volumei;j ¼ a þ b1 Volumei;j1 þ b2 Ri;j þ b3 Ri;j1 þ b4 Ri;j2 þ b5 Ri;j3 þ b6 Ri;j4 þ b7 Ri;j5 þ ei;j .

ð3Þ

Eq. (3) is a regression of intra-day trading volume in trade j for stock i at the analysts’ earnings forecasted day (t = 0) on previous trade’s volume, the same trade’s returns and previous trade’s returns. Volumei,j is the trading volume in trade j for stock i at the earnings forecasted day (t = 0). Volumei,j1 is the previous trade’s volume. Ri,j and Ri,jk (k = 1, . . . , 5) is, respectively, the continuously compounded return (or log return) in trade j and trade j  k for stock i. Because the scale of dependent variables is very different from that of independent variables, we first standardize both the independent and dependent variables, such that all variables have the same mean (zero) and standard deviation (one). Since the standardized regression coefficients are scale-free, we can directly compare coefficients across different portfolios. Table 4 shows that the relationship between institutional trading volume and previous trade’s returns is generally stronger than the relationship between volume and current trade’s returns. The coefficients in buying induced (higher decile) portfolios are larger than in selling induced (lower decile) portfolios. It is of interest to note that stocks that experience large increases in institutional ownership exhibit intra-day positive feedback trading, but the same is not apparent for stocks that experience large decreases in institutional ownership. That is, institutional investors tend to buy when the previous trade’s return was positive. To summarize, we can make a conjecture that the positive relationship between ownership changes and event period returns mainly results from inter-day price impact of herding rather than the inter-day positive feedback trading, though there are some evidence of intra-day positive feedback trading in buy induced herding portfolios. For the most part, our findings are consistent with daily pattern found by Griffin et al. (2003) who use daily and intradaily analyses to examine the competing explanations for the contemporaneous relation in Nasdaq 100 stocks. 3.4. Momentum effects Recall Table 2, the abnormal returns show a positive correlation between ownership changes and pre-event returns, suggesting some evidence of positive feedback trading, particularly in the extreme portfolios. Recent empirical works suggest that institutional investors tend to purchase (sell) stocks that performed well (poorly) in the recent past

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Table 4 Intra-day returns and trading volume

b1 b2 b3 b4 b5 b6 b7 F-stat. Adj. R

2

Large Decile decrease 2

Decile 3

Decile 4

Decile 5

Decile 6

Decile 7

Decile 8

Decile 9

Large increase

1.01* 0.01 0.02 0.01 0.02 0.02 0.00

1.00* 0.01 0.05 0.07 0.06 0.03 0.00

0.99* 0.10*** 0.19* 0.21* 0.20* 0.17* 0.12**

0.97* 0.32* 0.37* 0.38* 0.31* 0.32* 0.17*

0.97* 0.16** 0.20** 0.05 0.04 0.06 0.04

0.99* 0.19* 0.19* 0.16** 0.15** 0.11*** 0.05

0.99* 0.33* 0.34* 0.33* 0.32* 0.26* 0.14*

1.00* 0.26* 0.33* 0.33* 0.33* 0.28* 0.19*

1.00* 0.27* 0.32* 0.29* 0.26* 0.20* 0.15*

1.00* 0.04 0.13* 0.14* 0.10** 0.06 0.04

261,532* 153,687* 174,787* 69,353* 41,986* 46,622*

73,980* 104,859* 139,183* 235,401*

0.98

0.94

0.97

0.97

0.94

0.91

0.92

0.96

0.97

0.98

Firms are sorted into 10 portfolios based on the fraction of shares held by institutional investors at the beginning of analyst’s earnings forecast event day (t = 0). The firms in each initial institutional ownership decile are then further sorted into 10 portfolios based on the change in the fraction of shares held by institutional investors over the event period (t = 0 to 1) for a total of 100 initial institutional ownership, change in institutional-ownership-sorted portfolios. Firms are then re-aggregated based on their change in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, etc. For the regressions, we first standardize both the independent and dependent variables, such that all variables have the same mean (zero) and standard deviation (one). Then we estimate, controlling for the previous trade’s volume, the following: Volumei,j = a + b1Volumei,j1 + b2Ri,j + b3Ri,j1 + b4Ri,j2 + b5Ri,j3 + b6Ri,j4 + b7Ri,j5 + ei,j, Volumei,j is the trading volume in trade j for stock i at the earnings forecasted day (t = 0). Volumei,j1 is the previous trade’s volume. Ri,j and Ri,jk (k = 1, . . . , 5) are, respectively, the continuously compounded return (or log return) in trade j and trade j  k for stock i. The F-statistic is based on the null hypothesis that the coefficients do not differ in the regression model of each portfolio. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(e.g., Grinblatt et al., 1995; Wermers, 1999; Nofsinger and Sias, 1999; Cai et al., 2000). To further evaluate the momentum effects, we sort firms into 10 deciles based on their raw returns over the pre-event period (t = 2 to 1). For each decile, we also compute the event period abnormal returns using the market model risk-adjusted procedure. Table 5 reports the results. The first two rows of the table reveal the familiar return momentum pattern. The lower panel shows that, on average, past winners experience increase in institutional ownership and past losers experience decrease in institutional ownership. To further evaluate the relation between momentum and changes in institutional ownership, we next take a two-pass sorting procedure. Stocks are first sorted into 10 momentum portfolios based on their raw return over the pre-event period (t = 2 to 1). We then independently sort the stocks into 10 portfolios based on their change in institutional ownership over the event period (t = 0 to 1). Table 6 reports the event period abnormal returns for the 100 portfolios thus constructed. After controlling for momentum portfolios, results show that the type of institutional herding (buying versus selling) causes different impacts on event period abnormal returns from winner to loser deciles. When the change in institutional ownership is controlled for, only the sell induced herding portfolios show significant difference between past winners and losers. On the other hand, the buy induced herding portfolios do not show significant differences. Overall, the results reported in Table 6 show a general relation between event period abnormal returns and changes in institutional ownership. Regardless of stratification into winner and loser portfolios,

Pre-event raw return (t = 2 to 1) Event period abnormal return (t = 0 to 1) Ownership changes (t = 0 to 1)

Losers

Decile 2

Decile 3

Decile 4

Decile 5

Decile 6

Decile 7

Decile 8

Decile 9

Winners

F-stat.

Win.  Los.

10.26*

5.89*

3.73*

2.27*

1.23*

0.27*

0.76*

2.10*

4.20*

8.30*

11072.80*

18.56*

1.16*

0.34**

0.20

0.03

0.21

0.12

0.52*

0.49*

0.25

0.71*

8.91*

1.87*

0.19*

0.06*

0.05**

0.03

0.01

0.03

0.04

0.05**

0.12*

0.26*

25.62*

0.45*

Firms are sorted into 10 deciles based on their raw return over the pre-event period (t = 2 to 1). Abnormal returns are market model risk-adjusted returns over the event period (t = 0 to 1). For each decile, we compute changes in institutional ownership for institutions as a whole and for the three categories, QFIIs, Dealers, and Funds. Changes in institutional ownership are in the fraction of shares held by institutional investors over the event period (t = 0 to 1). The F-statistic is based on the null hypothesis that the means do not differ across the portfolios. Win.  Los. means the difference between the past winners and losers. * and ** indicate statistical significance at the 1% and 5% levels, respectively.

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Table 5 Short-term momentum portfolios and institutional event period ownership changes

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2484

Losers Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Winners

Large decrease

Decile 2

Decile 3

Decile 4

Decile 5

Decile 6

Decile 7

Decile 8

Decile 9

Large increase

F-stat.

Inc.  Dec.

4.14* 2.75* 1.65* 2.12** 2.21* 1.29 2.30** 1.85*** 2.81* 0.39

3.00* 1.71* 1.87* 1.99* 1.45* 1.71* 0.07 0.89 2.33* 0.24

0.71 0.46 1.30* 0.66 0.96** 0.00 0.54 0.42 1.11** 2.12**

0.30 0.62 0.47 0.21 0.39 0.05 0.16 0.49 0.87*** 1.42***

0.23 0.83*** 0.12 0.45 0.57 0.20 0.68*** 0.01 0.01 1.89**

0.61 0.08 0.36 0.10 0.90** 0.29 0.34 0.75*** 0.04 1.76**

0.01 1.23*** 0.27 0.25 0.63*** 0.93** 0.02 0.14 0.48 0.11

0.27 1.31* 0.88** 0.67 0.58 0.88*** 1.05* 0.47 0.39 0.39

0.78 0.83 1.19** 1.82* 1.42** 0.91** 1.63* 2.48* 1.07* 1.14**

3.72* 2.80* 2.70* 3.97* 4.67* 4.02* 3.85* 2.67* 4.93* 4.19*

11.49* 7.56* 7.03* 10.63* 14.44* 10.19* 9.37* 7.35* 20.69* 12.49*

7.86* 5.55* 4.35* 6.09* 6.89* 5.31* 6.15* 4.52* 7.75* 4.58*

2.54*

2.02**

F-stat.

2.70*

2.54*

1.70***

1.02

Win.  Los.

3.75*

2.76*

1.41***

1.12

1.66**

2.37*

1.42

1.42

0.93

1.55

–

–

0.12

0.66

0.36

0.47

–

–

Firms are sorted into 10 portfolios based on the fraction of shares held by institutional investors at the beginning of analyst’s earnings forecast event day (t = 0). The firms in each initial institutional ownership decile are then further sorted into 10 portfolios based on the change in the fraction of shares held by institutional investors over the event period (t = 0 to 1) for a total of 100 initial institutional ownership, change in institutional-ownership-sorted portfolios. Firms are then re-aggregated based on their change in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, etc. The F-statistic is based on the null hypothesis that the means do not differ across the portfolios. Inc.  Dec. is the difference between the extreme large increase and large decrease portfolios. Win.  Los. Means the difference between the past winners and past losers. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

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Table 6 Event period abnormal returns by momentum portfolio

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returns for large increase portfolios are positive while returns for large decrease portfolios are negative. These results are in line with existence of informed trading by institutions beyond simple momentum trading. 3.5. Are institutional investors informed traders? In Section 3.3, we find that the positive relation between ownership changes and event period returns mainly results from inter-day price impact of herding (price pressure). There are two possible reasons to suspect that institutional trading may in fact impact security prices (Nofsinger, 2001). Either institutions face a liquidity cost when they trade or institutions have better related information that is revealed when they trade. If temporary liquidity constraints are responsible for the price pressure, then the price changes should be temporary, nevertheless, if the price pressure induced by institutional trading results from informed trading, then price changes associated with changes in institutional ownership should be permanent. We can analyze the relation between institutional ownership changes and post-event period abnormal return to distinguish between the informed trading hypothesis and the liquidity cost hypothesis. Recall that the large increase portfolios (from Panel C of Table 2) show positive abnormal returns over the various post-event periods from day t = 2 to 30 with no return reversal and that some evidence of return continuations in the buy herding portfolios, at least 30 days after analysts’ earnings forecast release events was found. The lack of subsequent return reversals is consistent with the hypothesis that the event period returns are due to information and changes in institutional ownership are correlated with information. Of course, the sources of institutional informed trading might be attributed to institutional skill at interpreting publicly available information, or their ability to collect and process information that is not as readily available to the market (Ivkovic and Jegadeesh, 2004). It also might be, at least partially, attributed to brokers’ passing their best, most timely information to their largest clients first (e.g., institutions), and later disseminate that information to their smaller clients (individuals). For example, Fong et al. (2004) using Australian daily data extend the existing framework to show that brokers facilitate information transfer between managers resulting in a substantially higher level of herding. Although, our database provides different insights into institutional trading activities from Fong, Gallagher, Gardner, and Swan’s, the shown proclivity for institutions to trade together is similar. To investigate whether institutional herding around analysts’ earnings forecast release events have a destabilizing influence on stock price, we look at the relation between institutional ownership changes and post-event period abnormal return. If further price drops follow large institutional sales, we conclude that net selling by institutional investors is destabilizing. In other words, post-event period return patterns may tell us something about whether institutional herding destabilizes asset prices. It is possible that institutional herding over event period drives prices away from fundamental values. If this is the case, then we may observe subsequent price reversals as stock prices eventually revert toward their fundamental values. Our results for the large decrease portfolios show return reversals and significant positive abnormal returns after about five days. This being the case, we conclude overreaction in selling by institutions to the earnings forecast event. However, since price drops do not follow large institutional sales, the net selling by institutional investors around earnings forecast event is not destabilizing.

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4. Summary and conclusions Extant empirical literature on herding evaluates herding by focusing on quarterly or annual changes in ownership and data that either partitions shareholders into institutional and individual investors or just focus on mutual funds or pension funds. In this paper, we localize the analysis around analysts’ earnings release event day and make use of daily changes in ownership data. The results can be summarized as follows: (1) there exists a relation between company specific attributes and institutional herding, (2) observed changes in institutional ownership and contemporaneous return are mainly the results of inter-day price impact of herding, (3) institutional investors show evidence of being informed traders in buying but not selling. Results show that institutions tend to herd into larger stocks/lower surprise stocks (both earnings surprises and relative surprises) and out of smaller stocks/higher surprise stocks during the event period. In addition, there is more herding (both in buying and selling) in firms with higher systematic risk, higher ROE, more liquidity and more volatility. Our analyses reveal a strong positive relation between institutional ownership changes and returns, both immediately prior to and during the event-day. The observed positive relation between changes in institutional ownership and returns during the event day suggests that firms with high levels of institutional herding (both in buying and selling) have larger price reactions. This is consistent with Nofsinger and Sias (1999) and Chakravarty’s (2001) findings that daily changes in security price are correlated with daily changes in institutional ownership. Moreover, analysis of event period abnormal returns, controlling for momentum effect, documents an apparent effect of institutional herding on stock returns. Results also show a positive correlation between institutional ownership changes and pre-event returns. The observed institutional activity prior to the event-day is suggestive of some sort of information leakage from the analysts making the earnings forecast to institutional investors ‘‘prior’’ to the public earnings forecast release event-day. Analyses of post event-day returns reveal that the securities institutional investors purchase show sustained positive abnormal returns in large increase portfolios, but with return reversal appearing in large decrease portfolios a few days later. As noted by Nofsinger (2001), investor buying and selling behavior is frequently different. We find no evidence of subsequent return reversals in buy induced herding portfolios. This is consistent with the hypothesis that the event period returns are due to information, that the changes in institutional ownership are correlated with information. The continued positive abnormal returns of the securities institutional investors purchase suggest no evidence of overshooting and irritionality by institutions. This is consistent with Sias and Starks (1997) evidence that institutional trading reflects information and increases the speed of daily stock price adjustments. Additionally, institutional ownership changes observed during the event day are also related to the following day’s abnormal return. This implies (1) the event-day information release is still informative to institutions, (2) not all trading by institutions had been done prior to the event day, (3) there were still noticeable adjustments made by institutions during the event day, (4) only some institutions were able to access the leaked analysts’ earnings forecast prior to the event day. On the other hand, we find some evidence of return reversals in the sell induced herding portfolios, suggesting a possible selling overreaction by institutions to the earnings forecast event. However, we find that net selling by institutional investors around earnings forecast event is not destabilizing.

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