Analyst Recommendations, Mutual Fund Herding,

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


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


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).


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)


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


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.


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).


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


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


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.


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.

][][ ,,,,, tititititi pEpEpEpHM

tip ,


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.

][ ,tipE


Conditional Herding Measures

• Buy- and sell-herding measures:

• is recomputed conditionally for each of these measures

][|

][|

,,,,

,,,,

titititi

titititi

pEpHMSHM

pEpHMBHM

][ ,, titi pEpE


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


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


Summary Statistics(Table II)


Buy- and Sell-Herd Measures(Table III)


Buy- and Sell-Herd Measures(Table III contd.)


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)


DGTW Returns, Sorted by Recommendation Revisions (Table VI)



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.

titi

tititi sellsbuys

sellsbuysDratio

,,

,,, $$

$$


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.


Winner Funds (Panel A: Table VIII)


Loser Funds (Panel B: Table VIII)


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.


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.

Economy & Finance

Analyst Recommendations, Mutual Fund Herding,