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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 WermersUniversity of Maryland
The Seventh Maryland Finance Symposium
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 2
Relevant Quotes by the Media
• “Mutual fund managers are extremely focused on the short term” – Jason Zweig, Money Magazine
• “They (large investors) buy the same stocks at the same time and sell the same stocks at the same time”– Louis Rukeyser, Wall $treet Week
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 3
Motivation
• Mutual funds tend to “herd” or exhibit correlated trading patterns (e.g. Grinblatt, Titman and Wermers 1995; Wermers 1999; Sias 2004).
• Mutual fund herds speed up the incorporation of information in stock prices in prior-studied periods (Wermers 1999).
• Prior studies provide little evidence on: – why funds herd, beyond that they herd on certain stock
characteristics (e.g., Falkenstein (1996), Wermers (1999)
– whether funds herd on public vs. private information
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 4
Main objectives• We examine herding around an important price-setting
mechanism in U.S. equity markets – recommendation revisions by sell-side analysts.
• We examine how analyst revision-induced herding impacts stock prices.
• We focus on analyst recommendations because: – “clear and unequivocal” public signal of fundamental
value (Elton, Gruber, & Grossman 1986).– has short-lived investment value (Barber et al. 2001).– institutional investors are sensitive to recommendation
revisions and correct for potential biases (Chen and Cheng 2005, Mikhail et al. 2006).
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 5
Theories of Herding• Principal-agent problem: money managers mimic others to
avoid reputational risks.– Scharfstein and Stein (1990; AER)
• Money managers receive correlated private information– Some perhaps before others.– Hirshleifer, Subrahmanyam, and Titman (1994; JF)
• Managers infer private information from trades of others.– Bikhchandani, Hirshleifer, and Welch (1992; JPE)
• Institutional investors prefer highly liquid or low transaction-cost stocks – Falkenstein (1996)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 6
Empirical Predictionsof Herding Theories
• “Rational” herding stories (e.g., HST, BHW)– Stock prices permanently adjust after fund herding– Stabilizing
• “Irrational” herding stories (e.g., Scharfstein and Stein)– Stock prices temporarily adjust after fund herding– Destabilizing
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 7
Recent Empirical Work• Lakonishok, Shleifer, and Vishny (1992; JFE)
– Pension fund herding– Found little herding or momentum investing, except in
small stocks• Grinblatt, Titman, and Wermers (1995; AER)
– Mutual funds use momentum investing strategies– Did not test long-term stock returns
• Sias (2004; RFS)– Institutional trading is more strongly related to the past
trades of others than to past returns.
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 8
Recent Empirical Work contd.
• Wermers (1999; JoF)– Sample period: 1975 to 1994– Average level of fund herding is similar to LSV results– More herds among growth- than income-oriented funds– Similar herding on the buy- and sell-sides, except
• Stronger herding in small stocks, especially on the sell-side
• Stronger herding in high (or low) past-return stocks– Herding is followed by a permanent price adjustment– Biggest price adjustment in small stocks and during first 10
years (1975-1984).
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 9
Empirically Measured Herding
• “Trading together” is labeled “herding,” although it may be due to:– Exogenous changes in # shares
• Controlled for– Random occurrences
• Herding measure adjusts for this– Herding on same information
• “Rational”– Pure mimicry
• “Irrational
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 10
Data
• Quarterly portfolio holdings for all domestic-equity mutual funds between 1994 and 2003.– does not allow us to capture intra-quarter round-
trip trades.• Thomson Financial (Available via WRDS).• Matched with
– CRSP mutual fund returns and stock prices and returns.
– I/B/E/S analyst stock recommendations
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 11
Sample Selection• Include only actively managed domestic funds, i.e.,
exclude index, international, bond, metals funds.• New issues excluded for one year; delisted stocks
excluded for prior year.• Stock splits and other share adjustments “reversed”
from end-of-quarter holdings and share prices.• Each stock must be:
– traded by at least 5 funds.– covered by at least 2 analysts.
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 12
Measuring Herding• LSV (1992) herding measure:
the proportion of funds trading stock i during quarter t that are buyers.E| pi,t - E[pi,t]| = adjustment factor for random variation
• Herding by a subgroup of funds is studied by limiting the herding measure calculation to that subgroup.
• Herding in a subset of stock-quarters is studied by averaging the measure over only that subset.
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May 2, 2023 Analyst Recommendations and Mutual Fund Herding 13
Limitations of the Measure
• “A trade is a trade,” no matter how big.
• A proxy must be chosen for– we choose a cross-sectional average, but
another approach would be a time-series average.
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May 2, 2023 Analyst Recommendations and Mutual Fund Herding 14
Conditional Herding Measures
• Buy- and sell-herding measures:
• is recomputed conditionally for each of these measures
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May 2, 2023 Analyst Recommendations and Mutual Fund Herding 15
Measuring Consensus Analyst Recommendation Changes
= mean analyst recommendation (1 through 5) at the end of quarter t–i (i = 1,2)
– measured in quarter t–1 to mitigate possible spurious relations between herding and analyst revisions.
– Recommendations are brought forward a maximum of 180 days.– If no recommendation update, CHGREC = 0
• No recommendation change is treated as informative– We use only the most recent recommendation issued by an
analyst during a quarter.
211 ititit RECRECCHGREC
REC
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 16
Summary Statistics(Table I)
1994 1997 2000 2003Proportion of buys (in percent) 52.51 55.87 49.56 56.46No. of stocks traded by
≥ 1 fund 2,644 3,132 2,756 2,583≥ 5 fund 1,878 2,441 2,234 2,302≥ 10 fund 1,366 1,872 1,855 2,034≥ 20 fund 776 1,236 1,348 1,625≥ 30 fund 490 863 1,034 1,287≥ 50 fund 201 436 647 815≥ 100 fund 31 141 245 293≥ 200 fund 0 19 74 76
Year
Panel B: Trading statistics (fourth quarter) for stocks with recommendations and traded by at least 5 funds
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 17
Summary Statistics(Table II)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 18
Buy- and Sell-Herd Measures(Table III)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 19
Buy- and Sell-Herd Measures(Table III contd.)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 20
Multivariate Tests• Controls:
– ULEVEL (DLEVEL) = “1” for stocks with consecutive strong buy (strong sell) recommendations.
– LAGBUY (LAGSELL) = “1” if stock is classified as a buy- (sell-) herd stock in quarter t–1.
– ADD (DROP) = “1” if stock added (dropped) from S&P 500 index.– RET = prior-quarter stock return.– SIZE = log of market capitalization.– BM = log of book-to-market ratio.– DISP = std. dev. of quarter t–1 analyst earnings forecasts (scaled by
end-of-quarter price).– STD = std. dev. of daily stock returns during quarter t–1.– TURN = average daily trading volume divided by shares outstanding
during quarter t–1.
Multivariate Tests (Table IV)
Herding and DGTW Returns (Table V)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 23
DGTW Returns, Sorted by Recommendation Revisions (Table VI)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 24
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 25
Alternative Herding Measure: Dollar Trade Imbalances (Table VII)
• Average quarterly price is used to compute $buys and $sells.
• Dollar-weighted, rather than # of funds weighted.• Weaker relation between dollar trades and past
returns.– Future return reversals are similar to those for the LSV
herding measure.
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tititi sellsbuys
sellsbuysDratio
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May 2, 2023 Analyst Recommendations and Mutual Fund Herding 26
Winner vs. Loser Funds
– Do losing funds herd more?– Funds are classified based on their past-year
Carhart four-factor alpha– Above-mean alpha are “winner funds”; below-
mean are “loser funds.”– Then, look at DGTW returns to herding within
each subgroup of funds.
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 27
Winner Funds (Panel A: Table VIII)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 28
Loser Funds (Panel B: Table VIII)
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 29
Robustness Tests
1. We control for other investment signals to make sure that herding is driven by analyst revisions (Table IX)– Result: relation between herding and past analyst
revisions becomes even stronger!
2. We substitute analyst earnings forecast revisions for recommendation revisions (Tables X and XI)– Result: similar to results using recommendation
revisions.
May 2, 2023 Analyst Recommendations and Mutual Fund Herding 30
Conclusions• Herding much higher during 1994 to 2003 period
than during 1975 to 1994.• Strong reversals in abnormal returns, especially
when herds follow analyst revisions.• Herding stronger on sell-side; reversals also stronger
when sell-herds follow analyst downgrades (relative to buy-herds following upgrades)
• Losing funds herd and trend-follow more than winning funds; seem to drive reversal.
• Herds of funds overreact to public information signals; partly driven by reputational effects.