Institutional Investors and Stock Prices: Destabilizing and Stabilizing Herds

Transcription

1 Pacific Northwest Finance Conference October 2007 Institutional Investors and Stock Prices: Destabilizing and Stabilizing Herds Roberto C. Gutierrez Jr. and Eric K. Kelley Abstract From 1980 to 2005, institutional trading destabilizes stock prices. Specifically, stocks with herds of institutional buying in a given quarter suffer price declines of nearly three percent from four to eight quarters after the herding. Moreover, institutions do not suffer losses from the destabilization; they exit before prices reverse. These results extend the riding the bubble findings of Brunnermeier and Nagel (2004) beyond just hedge funds trading of technology stocks in the 1990s. Evidence that institutions are aware of the overpricings of the stocks they enter extends across time, across stocks, and across institutions. We also find that the entry buys of institutions are the drivers of the destabilization, while sell herds do not destabilize. In fact, exit sells actually stabilize prices. In other words, entries tend to push prices above their intrinsic levels while exits tend to push prices toward their correct levels. However, on net, the destabilization effect of institutional trading dominates. Gutierrez is at the Lundquist College of Business, University of Oregon. College of Management, University of Arizona. Kelley is at the Eller

2 Institutional investors are increasingly larger players in the stock market. Their equity ownership has more than doubled in the past twenty years to over 60% of the total market value, and their trading volume accounts for over 90% of the total dollar volume. 1 Much interest therefore arises about the effects of institutional investors on stock prices (as well as on other aspects of the financial marketplace and on firms decision making). Beginning most notably with Lakonishok, Shleifer, and Vishny (1992), economists have recognized the possibility that many institutions relying on similar information and facing similar incentives might trade in the same direction. 2 Such herding by institutions can possibly push prices away from intrinsic values. 3 However, there is scant empirical evidence suggesting that such destabilization occurs when institutions trade together (e.g., Wermers (1999) and Sias (2004)). As many have noted, herding is not a sufficient condition for price destabilization. For example, several institutions might correctly respond to signals of undervaluation by purchasing shares. Their herding, in this case, can adjust prices to appropriate levels. In short, institutional herding might have a stabilizing effect on stock prices, consistent with the common perception of institutions as sophisticated and better-informed traders. 1 Institutional ownership is estimated from 13F filings provided by Thomson Financial. The trading volume estimates come from Kaniel, Saar, and Titman (2006) who examine all orders executed on the NYSE from 2000 to 2003 for all common U.S. stocks. 2 Hirshleifer and Teoh (2003) and Brunnermeier (2001) provide a detailed and rich review of the large literature on herding, providing overviews of theories as well as the evidence from financial markets. We can separate herding into two general types: coincidental and mimicking. If institutions observe or acquire the same (or positively correlated) information about stock valuation, they can be expected to trade similarly and so coincidentally herd. Due to agency issues and other concerns, institutional money managers generically may favor stocks with certain characteristics, for example prudent, liquid, and better-known stocks, and so coincidentally herd. Money managers wanting to maintain or develop their reputations as good managers will tend to mimic the trades of other managers, since being wrong and trading with the herd is less damaging to reputation than being wrong and trading against the herd who is expected to be partially informed. Mimicking may also stem from institutions inferring stock-valuation signals from others trades; the more institutions observed purchasing a stock raises the probability of that stock being undervalued. Some specific references regarding these theories are: Scharfstein and Stein (1990) for reputation concerns; Banerjee (1992), Welch (1992), Bikhchandani, Hirshleifer, and Welch (1992), and Avery and Zemsky (1998) for signal inferences; Froot, Scharfstein, and Stein (1992), Hirshleifer, Subrahamanyam, and Titman (1994), and Irvine, Lipson, and Puckett (2007) for common information; and Falkenstein (1996), Del Guercio (1996), and Gompers and Metrick (2001) for characteristic preferences. 3 Chan and Lakonishok (1995), Keim and Madhavan (1997), Kaniel, Saar, and Titman (2006), Campbell, Ramadorai, and Schwartz (2007), and others, find evidence of institutional trades impacting prices. Griffin, Harris, and Topaloglu (2003), however, find little evidence of price pressure from institutional trades. Kaniel, Saar, and Titman (2006) suggest that this difference might emanate from Griffin, Harris, and Topaloglu s (2003) imprecision in identifying trader identity, as they must rely on broker identity and trade venue to characterize trade parties as institutional, individual, or market maker. 1 PNWFC 2007

3 We revisit the issue of whether or not institutional herding destabilizes stock prices. Using stock returns from 1980 to 2005, we find that stocks with a high imbalance of buying by 13F institutions in a given quarter suffer negative abnormal returns in the subsequent two years, consistent with the buy herd pushing prices beyond intrinsic values. Specifically, prices of buy-herd stocks decline nearly three percent from four to eight quarters after the herding, adjusting for size, book-to-market equity, and momentum effects. Our finding of a subsequent price reversal contrasts with prior studies because the reversal comes largely in the second year, whereas prior studies consider at most one year after the herding. Our result complements similar findings by Brown, Wei, and Wermers (2007) and Puckett and Yan (2007) using recent subperiods and subsets of institutions. We then extend our analysis of herding, and distinguish ourselves from these two current studies, by decomposing herding into two types of trades: entry buys and exit sells. Badrinath and Wahal (2002) find that institutions entries into stocks are on average in the direction of prior returns, while their exits are on average contrarian to prior returns. Hence, entries are the potentially destabilizing trades, and exits are the potentially stabilizing ones. To investigate this, we decompose our herding measure according to these two trade types. We find that the link between institutional herding and price reversal that we initially document is attributable only to the herding of entries. In stark contrast, the herding of exits is in the direction of future stock returns, meaning that a greater imbalance of exits forecasts lower returns. These results suggest that exits push prices toward their lower correct levels while entries push prices above their intrinsic values. Isolating these trade types allows us to see the disparate effects of institutional entry and exit on stock prices. One cursory interpretation of these findings is that entries are less informed trades and exits are more informed trades. We can think of reasons to suspect this. First, research efforts are possibly greater for stocks currently held. Second, it is reasonable to believe that one s ability to value a specific stock increases as his experience with that 2 PNWFC 2007

4 stock increases. For example, Mikhail, Walther, and Willis (1997) find that earningsforecast errors of analysts decline as the number of prior quarters an analyst forecasts a given firm increases, incremental to industry and overall experience. However, the literature also suggests a distinctly different possibility to explain the destabilization of entries. Skilled, informed managers may be choosing to participate in the destabilization, instead of trading against it. De Long, Shleifer, Summers, and Waldmann (1990) and Abreu and Brunnermeier (2003) show that the presence of uninformed noise traders can make it optimal for rational and informed traders to buy an overpriced stock. In such a case, riding the bubble is more profitable than trading against it. Brunnermeier and Nagel (2004) provide evidence of hedge funds trading technology stocks in this manner in the late 1990s. To investigate the relations between the herd effects above and the skill of the participating managers, we employ prior performance (gross of trading costs) of an institution s stock portfolio as a proxy for stock-pricing skill. First of all, it is useful to note that separating institutions based on above-median and below-median five-year performance leads to reliable differences in future portfolio performance, with the better institutions generating abnormal returns over the next two years that are roughly 10 basis points per quarter greater than those of the worse institutions. We then examine the herds of entering institutions and find that both the better-performing and the worse-performing institutions participate in the destabilization. However, neither institutional type suffers losses. In short, both types of institutions exit before losses set in. This suggests that institutions in general are, or become, sufficiently informed about the overvaluation of the entry-herd stocks. These results extend the riding the bubble findings of Brunnermeier and Nagel (2004) across stocks, time, and institutions. For the herds of exiting institutions, we find that both the better-performing and the worse-performing institutions are stabilizing prices when they exit. Why entry trades differ so drastically from exit trades in their effects on stock prices is a focus for future research. 3 PNWFC 2007

5 1. Data and Methodology The data on institutional stock holdings are obtained from Thomson Financial and are gathered from 13F filings of institutional investors from 1980 to We gather stock price, shares outstanding, and return data from CRSP and book value of equity from Compustat. After merging these data sources and cleaning the holdings data (details are available upon request), we have a sample of 4,115 institutions Herding Measure Our measure of herding is based on Lakonishok, Shleifer, and Vishny s (1992) and is commonly used in the literature. For each institution and each stock in quarter t, we first determine the change in the number of shares held from quarter t 1 to quarter t, adjusted for stock splits. Herding by institutions for each stock in quarter t is then defined as follows. HERD t = number of net buyers number of net buyers + number of net sellers (1) This variable measures the imbalance of institutional trading between buys and sells. For all of our tabulated results, we require a stock to have at least 10 institutional traders in quarter t. Varying this filter from 1 to 20 has little effect on our findings. We consider a second measure of herding using the number of shares bought and sold, and our main findings remain. 4 4 For several reasons we prefer to measure herding based on the numbers of buyers and sellers instead of the numbers of shares bought and sold. First, Jones, Kaul, and Lipson (1994) find that stock price movements are due more to the number of trades than to the size of trades. Second, the reputational motive to follow the herd considers the imbalance in the number of institutions buying or selling. And, although less clear, the signal-inference motive would seem to weight the number of traders more than the volume traded since the sizes of managed portfolios can vary greatly. Last, the price impact of a single trader with a given trade size should be lower than that of a number of traders with a collectively similar trade size, as the single trader can strategically work his order over time to reduce price impact. 4 PNWFC 2007

6 1.2. Abnormal Returns With a measure of herding in hand, we can examine the relation between herding and future returns. If herding does in fact destabilize prices by pushing them from intrinsic values, we should see prices reversing in the future as the market corrects the mispricing. To determine if herding leads to price reversal, we employ monthly cross-sectional regressions of future abnormal returns on herding. Our estimates of abnormal returns account for size, book-to-market equity, and momentum effects. As done by Daniel, Grinblatt, Titman, and Wermers (1997), and many others, we identify a benchmark portfolio for each stock each month. We form these benchmark portfolios each July using the following three-way dependent sorting procedure. First, we sort all available stocks from CRSP into five size groups according to their market value of equity at the end of June, with breakpoints based on NYSE stocks only. Then, within each of these size groups, we sort stocks into five groups based on their book-to-market ratios, where the book value of equity is from the fiscal year-end in the calendar year preceding the July formation of the benchmarks and the market value of the equity is from the prior December. Finally, we sort stocks in each size/book-to-market group into quintiles based on their 12-month return ending in the prior May. We calculate the monthly equally weighted returns for each of the 125 benchmark portfolios and subtract each stock s corresponding benchmark return from its monthly return to arrive at an abnormal return. We use equally weighted benchmark returns since we examine equally weighted portfolios of stocks where institutions have herded Monthly Cross-Sectional Regressions To examine the relation between herding and future abnormal returns, we estimate the following cross-sectional regression each month. AR t+k,m = a + b 1 HERD t + b 2 r t + b 3 r t 2,t 1 + ɛ t, (2) 5 PNWFC 2007

7 where AR t+k,m is the benchmark-adjusted return for a given stock in month m = {1, 2, 3} of quarter t + k, with k varying from one to eight quarters in the future, HERD t is the measure of herding defined in equation (1), r t is the raw return of the stock in quarter t, and r t 2,t 1 is the raw return over the two prior quarters. Even though we control somewhat for momentum effects in returns on the left-hand side, we include r t and r t 2,t 1 since various horizons of prior returns are known to display marginal effects on future returns (Gutierrez and Kelley (2007)). Also, much evidence indicates that institutions on average have a tendency to chase prior returns. Since the relation between prior returns and institutional trading is not precisely defined, we choose to be conservative and control for several windows of prior returns. One more methodological decision deserves comment as well. Our use of monthly regressions instead of quarterly ones produces more powerful tests. However, using quarterly regressions, we obtain the same inferences on our main tests. Finally, our test statistics are formed using the efficient-weighting procedure of Ferson and Harvey (1999) to account for heteroskedasticity. To test the null hypothesis that herding has no relation to future returns (b 1 = 0), we first divide each month s regression coefficient estimate by its cross-sectional variance. The t-statistic is calculated by dividing the mean of the adjusted time series by the time-series standard error. When we examine multiple-quarter windows of future returns, for example quarters five through eight (event-year two after the herding), we pool the time series of coefficients from each single-quarter analysis. That is, for abnormal returns in the month of October 1988, there are four estimates of b 1 corresponding to that month being in quarters five, six, seven, and eight, respectively. We account for any contemporaneous correlations in the b 1 estimates by clustering the standard errors within each calendar month. 6 PNWFC 2007

8 Table 1 Regressions of Future Abnormal Stock Return on Lagged Herding Each month from April 1980 to December 2005, we regress various windows of future stock returns (adjusted for size, book-to-market equity, and prior year s return using 5x5x5 portfolios) on lagged HERD and two horizons of lagged returns. HERD t is the number of institutions that are net buyers of a given stock during quarter t divided by the number of institutions with net changes in their holdings of that stock. Stocks with less than ten institutions making net changes in holdings during quarter t are excluded. Simple returns over quarter t and over quarters [t 2, t 1] are denoted r t and r t 2,t 1 respectively. The efficient-weighted average of the monthly time series of each coefficient and the corresponding t-statistic, which is in parentheses, are calculated following Ferson and Harvey (1999). In addition, the standard errors are clustered by calendar month when the future-return window exceeds one quarter. Coefficient estimates are multiplied by 100. Future-Return Window Qtr Qtr Qtr Qtr Qtrs Intercept ( 3.42) (0.01) (0.75) (2.36) (2.04) HERD t (2.68) ( 0.77) ( 0.65) ( 2.49) ( 2.05) r t (1.15) (1.82) (1.95) ( 0.02) (0.28) r t 2,t (2.05) (0.62) ( 2.16) ( 2.07) ( 0.68) Avg N Avg R PNWFC 2007

9 Table 2 Stocks with Extreme Buy Herds Each quarter of 1980 to 2005, we identify the stocks in the highest decile of HERD, defined in Table 1. For this equally weighted portfolio of buy-herd stocks, we calculate monthly stock returns (adjusted for size, book-to-market equity, and momentum effects). Panel A reports the mean alphas for various holding periods. The t-statistic is in parentheses, and standard errors are clustered by calendar month when the futurereturn window exceeds one quarter. Monthly alpha estimates are multiplied by 100. Panel B reports statistics for the stocks in the highest quintile of HERD. The average number of stocks in the portfolio each quarter as well as the averages for number of institutions with nonzero changes in holdings in these stocks, for HERD, and for net change in percent institutional ownership in these stocks as a percentage of shares outstanding ( IO) are given. These averages are the means of the quarterly time series of the means. The means of the medians are in parentheses. Panel A. Abnormal Returns Qtr Qtr Qtr Qtr Qtrs Buy Herd (1.91) ( 0.25) ( 0.86) ( 3.11) ( 3.36) Sell Herd ( 1.47) ( 2.05) ( 1.05) ( 0.56) (0.72) Panel B. Descriptive Statistics Num. of N Institutions HERD t IO t (%) Buy Herd (22) 0.75 (0.73) 5.36 (3.11) Sell Herd (23) 0.32 (0.34) 3.53 ( 1.64) 8 PNWFC 2007

10 Table 3 Regressions of Future Abnormal Returns on Lagged Herding of Entries and Exits Each month from April 1980 to December 2005, we regress various windows of future stock returns (adjusted for size, book-to-market equity, and momentum effects) on lagged HERD Entry, HERD Exit, and two horizons of lagged returns. HERD Entry t is the number of institutions that are entering a given stock during quarter t divided by the number of institutions with net changes in their holdings of that stock. HERDt Exit is the number of institutions that are exiting a given stock during quarter t divided by the number of institutions with net changes in their holdings of that stock. Stocks with less than ten institutions making net changes in holdings during quarter t are excluded. Simple returns over quarter t and over quarters [t 2, t 1] are denoted r t and r t 2,t 1 respectively. The efficient-weighted average of the monthly time series of each coefficient and the corresponding t-statistic, which is in parentheses, are calculated following Ferson and Harvey (1999). In addition, the standard errors are clustered by calendar month when the future-return window exceeds one quarter. Coefficient estimates are multiplied by 100. Future-Return Window Qtr Qtrs Qtrs Intercept (2.13) (3.82) (3.58) HERD Entry t (0.31) ( 3.21) ( 3.84) HERD Exit t ( 6.04) ( 5.75) ( 3.56) r t (0.70) (2.53) (0.40) r t 2,t (2.08) (0.14) ( 0.35) Avg N Avg R PNWFC 2007

11 Table 4 Stocks with Extreme Herds of Entries or Exits Each quarter of 1980 to 2005, we identify the stocks in the highest decile of HERD Entry, defined in Table 3. For this equally weighted portfolio of entry-herd stocks, we calculate monthly abnormal returns (adjusted for size, book-to-market equity, and momentum effects, and multiplied by 100) for various holding periods using a calendar-time method and report these in Panel A. The same is done for HERD Exit. The t-statistics are in parentheses. Panel B reports statistics for the stocks in the highest decile of HERD Entry and HERD Exit. The average number of stocks in the portfolio each quarter as well as the averages for number of institutions with nonzero changes in holdings in these stocks, for the respective herding measure HERDt k where k = {Entry, Exit}, and for net change in percent institutional ownership in these stocks as a percentage of shares outstanding ( IO) are given. These averages are the means of the quarterly time series of the means. The means of the medians are in parentheses. Panel A. Abnormal returns Qtr Qtr Qtr Qtr Qtrs Entry herds (2.32) (0.19) ( 1.32) ( 2.87) ( 2.63) Exit herds ( 2.67) ( 2.54) ( 2.54) ( 1.37) ( 0.58) Panel B. Descriptive statistics Num. of N Institutions HERD k t HERD t IO t (%) Entry herds (24) 0.40 (0.37) 0.68 (0.67) 5.29 (3.42) Exit herds (22) 0.14 (0.13) 0.41 (0.41) 3.29 ( 1.70) 10 PNWFC 2007

12 Table 5 Regressions of Future Abnormal Stock Return on Herding by Better and Worse Institutions Each quarter from March 1982 to December 2005, we regress various windows of abnormal returns (adjusted for size, book-to-market equity, and momentum effects) on lagged HERD Entry and HERD Exit measured across better-performing and worse-performing institutions, and two horizons of lagged returns. Institutions are ranked each quarter based on the performances of their stock portfolios over the prior five years (adjusting stock returns for size, book-to-market, and prior-one-year-return effects, requiring a minimum of two years). Institutions above the median performance are labeled H; those below are labeled L. HERD H Entry t and HERDt H Exit are the fractions of H institutions with nonzero net changes in holdings of a given stock that are entries and and HERDt L Exit are defined analogously over the subset of L institutions. Stocks with less than ten institutions making net changes in holdings during quarter t are excluded. Simple returns over quarter t and over quarters [t 2, t 1] are denoted r t and r t 2,t 1 respectively. The efficient-weighted average of the monthly time series of each coefficient and the corresponding t-statistic, which is in parentheses, are calculated following Ferson and Harvey (1999). In addition, the standard errors are clustered by calendar month when the future-return window exceeds one quarter. Coefficient estimates are multiplied by 100. exits, respectively. HERD L Entry t 11 PNWFC 2007

13 Future-Return Window Qtr Qtrs Qtrs Intercept (1.90) (3.69) (3.32) HERD H Entry t (1.73) ( 1.46) ( 2.26) HERD L Entry t ( 2.03) ( 5.32) ( 4.06) HERD H t Exit ( 4.87) ( 4.65) ( 3.40) HERD L t Exit ( 4.15) ( 5.21) ( 2.19) r t (0.92) (2.39) (0.27) r t 2,t (2.06) ( 0.12) ( 0.28) Avg N Avg R PNWFC 2007

14 Table 6 Institutional Trading of Stocks with an Entry Herd Each quarter from March 1982 to 2005, we identify the stocks in the highest decile of HERD Entry, defined in Table 3. We then identify the institutions entering these stocks. Using the abnormal returns of the entry-herd stocks (adjusted for size, book-to-market equity, and momentum effects, and multiplied by 100), we evaluate how well institutions trade the stocks they enter. Performance is calcuated in two ways, using a calendar-time method. First, in Panel A, the performance of each institution s portfolio of entry-herd stocks is estimated each quarter, and the mean performance across institutions determines the Average performance. Second, in Panel B, the amount invested by all the entering institutions in each entry-herd stock relative to the amount invested across the set of entry-herd stocks forms the weights that determine the Aggregate performance. For both aggregate and average performances, we assume that trading occurs at the end of the period (EOP) and at the beginning of the period (BOP); for the latter we remove intraquarter stock performance to back out weights for the start of the quarter. The t-statistics are in parentheses. EOP BOP Qtr Qtrs Qtrs Qtr Qtrs Qtrs Panel A. Average Performance H (2.11) (1.45) ( 1.05) (14.34) (4.09) (3.33) L (1.69) (0.53) (0.02) (14.76) (2.52) (2.53) H L (1.87) (2.64) ( 1.11) (3.72) (5.29) (0.87) Panel B. Aggregate Performance H (0.86) (0.91) (0.21) (15.37) (3.48) (2.89) L (0.32) (0.51) (1.01) (14.62) (1.66) (1.91) H L (1.44) (1.24) (1.40) (7.29) (4.60) (1.21) 13 PNWFC 2007

15 Portfolio Weight 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% Quarter H_Avg_EOP L_Avg_EOP H_Avg_BOP L_Avg_BOP H_Agg_EOP L_Agg_EOP H_Agg_BOP L_Agg_BOP Figure 1: Each quarter from March 1982 to 2005, we identify the stocks in the highest decile of HERD Entry, defined in Table 3. We then identify the institutions entering these stocks. The Average weights are determined by calculating each institution s percentage of dollars invested in their entry stocks relative to their own stock portfolio, and the mean percentage is plotted. The Aggregate weights for each stock are determined by summing the dollar amounts of holdings in these entry-herd stocks as a percentage of the amount invested by all these institutions in stocks. 14 PNWFC 2007

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