Analyst Recommendations,  Mutual Fund Herding,  and Overreaction in Stock Prices Nerissa C. Brown  University of Southern ...
Relevant Quotes by the Media <ul><li>“ Mutual fund managers are extremely focused on the short term”  </li></ul><ul><ul><l...
Motivation <ul><li>Mutual funds tend to “herd” or exhibit correlated trading patterns (e.g. Grinblatt, Titman and Wermers ...
Main objectives <ul><li>We examine herding around an important price-setting mechanism in U.S. equity markets –  recommend...
Theories of Herding <ul><li>Principal-agent problem: money managers mimic others to avoid reputational risks. </li></ul><u...
Empirical Predictions of Herding Theories <ul><li>“ Rational” herding stories (e.g., HST, BHW) </li></ul><ul><ul><li>Stock...
Recent Empirical Work <ul><li>Lakonishok, Shleifer, and  Vishny (1992; JFE) </li></ul><ul><ul><li>Pension fund herding </l...
Recent Empirical Work contd. <ul><li>Wermers (1999; JoF) </li></ul><ul><ul><li>Sample period:  1975 to 1994 </li></ul></ul...
Empirically Measured Herding <ul><li>“ Trading together” is labeled “herding,” although it may be due to: </li></ul><ul><u...
Data <ul><li>Quarterly portfolio holdings for all domestic-equity mutual funds between 1994 and 2003. </li></ul><ul><ul><l...
Sample Selection <ul><li>Include only actively managed domestic funds, i.e., exclude index, international, bond, metals fu...
Measuring Herding <ul><li>LSV (1992) herding measure: </li></ul><ul><li>  the proportion of funds trading stock  i  during...
Limitations of the Measure <ul><li>“A trade is a trade,” no matter how big. </li></ul><ul><li>A proxy must be chosen for <...
Conditional Herding Measures <ul><li>Buy- and sell-herding measures: </li></ul><ul><li>is recomputed conditionally for eac...
Measuring Consensus Analyst Recommendation Changes  <ul><ul><li>  </li></ul></ul><ul><ul><li>  = mean analyst recommendati...
Summary Statistics (Table I)
Summary Statistics (Table II)
Buy- and Sell-Herd Measures (Table III)
Buy- and Sell-Herd Measures (Table III contd.)
Multivariate Tests <ul><li>Controls: </li></ul><ul><ul><li>ULEVEL  ( DLEVEL ) = “1” for stocks with consecutive strong buy...
Multivariate Tests (Table IV)
Herding and DGTW Returns (Table V)
DGTW Returns, Sorted by Recommendation Revisions (Table VI)
 
Alternative Herding Measure:  Dollar Trade Imbalances (Table VII) <ul><li>Average quarterly price is used to compute $ buy...
Winner vs. Loser Funds <ul><ul><li>Do losing funds herd more? </li></ul></ul><ul><ul><li>Funds are classified based on the...
Winner Funds (Panel A: Table VIII)
Loser Funds (Panel B: Table VIII)
Robustness Tests <ul><li>1. We control for other investment signals to make sure that herding is driven by analyst revisio...
Conclusions <ul><li>Herding much higher during 1994 to 2003 period than during 1975 to 1994. </li></ul><ul><li>Strong reve...
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Analyst Recommendations, Mutual Fund Herding,

  1. 1. Analyst Recommendations, Mutual Fund Herding, and Overreaction in Stock Prices Nerissa C. Brown University of Southern California Kelsey D. Wei University of Texas – Dallas Russ Wermers University of Maryland The Seventh Maryland Finance Symposium
  2. 2. Relevant Quotes by the Media <ul><li>“ Mutual fund managers are extremely focused on the short term” </li></ul><ul><ul><li>Jason Zweig, Money Magazine </li></ul></ul><ul><li>“ They (large investors) buy the same stocks at the same time and sell the same stocks at the same time” </li></ul><ul><ul><li>Louis Rukeyser, Wall $treet Week </li></ul></ul>
  3. 3. Motivation <ul><li>Mutual funds tend to “herd” or exhibit correlated trading patterns (e.g. Grinblatt, Titman and Wermers 1995; Wermers 1999; Sias 2004). </li></ul><ul><li>Mutual fund herds speed up the incorporation of information in stock prices in prior-studied periods (Wermers 1999). </li></ul><ul><li>Prior studies provide little evidence on: </li></ul><ul><ul><li>why funds herd, beyond that they herd on certain stock characteristics (e.g., Falkenstein (1996), Wermers (1999) </li></ul></ul><ul><ul><li>whether funds herd on public vs. private information </li></ul></ul>
  4. 4. Main objectives <ul><li>We examine herding around an important price-setting mechanism in U.S. equity markets – recommendation revisions by sell-side analysts. </li></ul><ul><li>We examine how analyst revision-induced herding impacts stock prices. </li></ul><ul><li>We focus on analyst recommendations because: </li></ul><ul><ul><li>“ clear and unequivocal” public signal of fundamental value (Elton, Gruber, & Grossman 1986). </li></ul></ul><ul><ul><li>has short-lived investment value (Barber et al. 2001). </li></ul></ul><ul><ul><li>institutional investors are sensitive to recommendation revisions and correct for potential biases (Chen and Cheng 2005, Mikhail et al. 2006). </li></ul></ul>
  5. 5. Theories of Herding <ul><li>Principal-agent problem: money managers mimic others to avoid reputational risks. </li></ul><ul><ul><li>Scharfstein and Stein (1990; AER) </li></ul></ul><ul><li>Money managers receive correlated private information </li></ul><ul><ul><li>Some perhaps before others. </li></ul></ul><ul><ul><li>Hirshleifer, Subrahmanyam, and Titman (1994; JF) </li></ul></ul><ul><li>Managers infer private information from trades of others. </li></ul><ul><ul><li>Bikhchandani, Hirshleifer, and Welch (1992; JPE) </li></ul></ul><ul><li>Institutional investors prefer highly liquid or low transaction-cost stocks </li></ul><ul><ul><li>Falkenstein (1996) </li></ul></ul>
  6. 6. Empirical Predictions of Herding Theories <ul><li>“ Rational” herding stories (e.g., HST, BHW) </li></ul><ul><ul><li>Stock prices permanently adjust after fund herding </li></ul></ul><ul><ul><li>Stabilizing </li></ul></ul><ul><li>“ Irrational” herding stories (e.g., Scharfstein and Stein) </li></ul><ul><ul><li>Stock prices temporarily adjust after fund herding </li></ul></ul><ul><ul><li>Destabilizing </li></ul></ul>
  7. 7. Recent Empirical Work <ul><li>Lakonishok, Shleifer, and Vishny (1992; JFE) </li></ul><ul><ul><li>Pension fund herding </li></ul></ul><ul><ul><li>Found little herding or momentum investing, except in small stocks </li></ul></ul><ul><li>Grinblatt, Titman, and Wermers (1995; AER) </li></ul><ul><ul><li>Mutual funds use momentum investing strategies </li></ul></ul><ul><ul><li>Did not test long-term stock returns </li></ul></ul><ul><li>Sias (2004; RFS) </li></ul><ul><ul><li>Institutional trading is more strongly related to the past trades of others than to past returns. </li></ul></ul>
  8. 8. Recent Empirical Work contd. <ul><li>Wermers (1999; JoF) </li></ul><ul><ul><li>Sample period: 1975 to 1994 </li></ul></ul><ul><ul><li>Average level of fund herding is similar to LSV results </li></ul></ul><ul><ul><li>More herds among growth- than income-oriented funds </li></ul></ul><ul><ul><li>Similar herding on the buy- and sell-sides, except </li></ul></ul><ul><ul><ul><li>Stronger herding in small stocks, especially on the sell-side </li></ul></ul></ul><ul><ul><ul><li>Stronger herding in high (or low) past-return stocks </li></ul></ul></ul><ul><ul><li>Herding is followed by a permanent price adjustment </li></ul></ul><ul><ul><li>Biggest price adjustment in small stocks and during first 10 years (1975-1984). </li></ul></ul>
  9. 9. Empirically Measured Herding <ul><li>“ Trading together” is labeled “herding,” although it may be due to: </li></ul><ul><ul><li>Exogenous changes in # shares </li></ul></ul><ul><ul><ul><li>Controlled for </li></ul></ul></ul><ul><ul><li>Random occurrences </li></ul></ul><ul><ul><ul><li>Herding measure adjusts for this </li></ul></ul></ul><ul><ul><li>Herding on same information </li></ul></ul><ul><ul><ul><li>“Rational” </li></ul></ul></ul><ul><ul><li>Pure mimicry </li></ul></ul><ul><ul><ul><li>“Irrational </li></ul></ul></ul>
  10. 10. Data <ul><li>Quarterly portfolio holdings for all domestic-equity mutual funds between 1994 and 2003. </li></ul><ul><ul><li>does not allow us to capture intra-quarter round-trip trades. </li></ul></ul><ul><li>Thomson Financial (Available via WRDS). </li></ul><ul><li>Matched with </li></ul><ul><ul><li>CRSP mutual fund returns and stock prices and returns. </li></ul></ul><ul><ul><li>I/B/E/S analyst stock recommendations </li></ul></ul>
  11. 11. Sample Selection <ul><li>Include only actively managed domestic funds, i.e., exclude index, international, bond, metals funds. </li></ul><ul><li>New issues excluded for one year; delisted stocks excluded for prior year. </li></ul><ul><li>Stock splits and other share adjustments “reversed” from end-of-quarter holdings and share prices. </li></ul><ul><li>Each stock must be: </li></ul><ul><ul><li>traded by at least 5 funds. </li></ul></ul><ul><ul><li>covered by at least 2 analysts. </li></ul></ul>
  12. 12. Measuring Herding <ul><li>LSV (1992) herding measure: </li></ul><ul><li> the proportion of funds trading stock i during quarter t that are buyers. </li></ul><ul><li>E | p i,t - E [ p i,t ]| = adjustment factor for random variation </li></ul><ul><li>Herding by a subgroup of funds is studied by limiting the herding measure calculation to that subgroup. </li></ul><ul><li>Herding in a subset of stock-quarters is studied by averaging the measure over only that subset. </li></ul>
  13. 13. Limitations of the Measure <ul><li>“A trade is a trade,” no matter how big. </li></ul><ul><li>A proxy must be chosen for </li></ul><ul><ul><li>we choose a cross-sectional average, but another approach would be a time-series average. </li></ul></ul>
  14. 14. Conditional Herding Measures <ul><li>Buy- and sell-herding measures: </li></ul><ul><li>is recomputed conditionally for each of these measures </li></ul>
  15. 15. Measuring Consensus Analyst Recommendation Changes <ul><ul><li> </li></ul></ul><ul><ul><li> = mean analyst recommendation (1 through 5) at the end of quarter t – i ( i = 1,2) </li></ul></ul><ul><ul><li>measured in quarter t – 1 to mitigate possible spurious relations between herding and analyst revisions. </li></ul></ul><ul><ul><li>Recommendations are brought forward a maximum of 180 days. </li></ul></ul><ul><ul><li>If no recommendation update, CHGREC = 0 </li></ul></ul><ul><ul><ul><li>No recommendation change is treated as informative </li></ul></ul></ul><ul><ul><li>We use only the most recent recommendation issued by an analyst during a quarter. </li></ul></ul>
  16. 16. Summary Statistics (Table I)
  17. 17. Summary Statistics (Table II)
  18. 18. Buy- and Sell-Herd Measures (Table III)
  19. 19. Buy- and Sell-Herd Measures (Table III contd.)
  20. 20. Multivariate Tests <ul><li>Controls: </li></ul><ul><ul><li>ULEVEL ( DLEVEL ) = “1” for stocks with consecutive strong buy (strong sell) recommendations. </li></ul></ul><ul><ul><li>LAGBUY ( LAGSELL ) = “1” if stock is classified as a buy- (sell-) herd stock in quarter t – 1. </li></ul></ul><ul><ul><li>ADD ( DROP ) = “1” if stock added (dropped) from S&P 500 index. </li></ul></ul><ul><ul><li>RET = prior-quarter stock return. </li></ul></ul><ul><ul><li>SIZE = log of market capitalization. </li></ul></ul><ul><ul><li>BM = log of book-to-market ratio. </li></ul></ul><ul><ul><li>DISP = std. dev. of quarter t – 1 analyst earnings forecasts (scaled by end-of-quarter price). </li></ul></ul><ul><ul><li>STD = std. dev. of daily stock returns during quarter t – 1. </li></ul></ul><ul><ul><li>TURN = average daily trading volume divided by shares outstanding during quarter t – 1. </li></ul></ul>
  21. 21. Multivariate Tests (Table IV)
  22. 22. Herding and DGTW Returns (Table V)
  23. 23. DGTW Returns, Sorted by Recommendation Revisions (Table VI)
  24. 25. Alternative Herding Measure: Dollar Trade Imbalances (Table VII) <ul><li>Average quarterly price is used to compute $ buys and $ sells. </li></ul><ul><li>Dollar-weighted, rather than # of funds weighted. </li></ul><ul><li>Weaker relation between dollar trades and past returns. </li></ul><ul><ul><li>Future return reversals are similar to those for the LSV herding measure. </li></ul></ul>
  25. 26. Winner vs. Loser Funds <ul><ul><li>Do losing funds herd more? </li></ul></ul><ul><ul><li>Funds are classified based on their past-year Carhart four-factor alpha </li></ul></ul><ul><ul><li>Above-mean alpha are “winner funds”; below-mean are “loser funds.” </li></ul></ul><ul><ul><li>Then, look at DGTW returns to herding within each subgroup of funds. </li></ul></ul>
  26. 27. Winner Funds (Panel A: Table VIII)
  27. 28. Loser Funds (Panel B: Table VIII)
  28. 29. Robustness Tests <ul><li>1. We control for other investment signals to make sure that herding is driven by analyst revisions (Table IX) </li></ul><ul><ul><li>Result: relation between herding and past analyst revisions becomes even stronger! </li></ul></ul><ul><li>2. We substitute analyst earnings forecast revisions for recommendation revisions (Tables X and XI) </li></ul><ul><ul><li>Result: similar to results using recommendation revisions. </li></ul></ul>
  29. 30. Conclusions <ul><li>Herding much higher during 1994 to 2003 period than during 1975 to 1994. </li></ul><ul><li>Strong reversals in abnormal returns, especially when herds follow analyst revisions. </li></ul><ul><li>Herding stronger on sell-side; reversals also stronger when sell-herds follow analyst downgrades (relative to buy-herds following upgrades) </li></ul><ul><li>Losing funds herd and trend-follow more than winning funds; seem to drive reversal. </li></ul><ul><li>Herds of funds overreact to public information signals; partly driven by reputational effects. </li></ul>

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