JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 45, No. 5, Oct. 2010, pp. 1111-1131
COPYRIGHT 2010, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195
doi: 10.1017/S0022109010000426
Can Mutual Fund Managers Pick Stocks?
Evidence from Their Trades Prior to
Earnings Announcements
Malcolm Baker, Lubomir Litov, Jessica A. W?chter, and
Jeffrey Wurgler*
Abstract
Recent research finds that the stocks that mutual fund managers buy outperform the stocks
that they sell (e.g., Chen, Jegadeesh, and Wermers (2000)). We study the nature of this
stock-picking ability. We construct measures of trading skill based on how the stocks held
and traded by fund managers perform at subsequent corporate earnings announcements.
This approach increases the power to detect skilled trading and sheds light on its source.
We find that the average fund's recent buys significantly outperform its recent sells around
the next earnings announcement, and that this accounts for a disproportionate fraction of
the total abnormal returns to fund trades estimated in prior work. We find that mutual fund
trades also forecast earnings surprises. We conclude that mutual fund managers are able to
trade profitably in part because they are able to forecast earnings-related fundamentals.
I. Introduction
Can mutual fund managers pick stocks? This question has long interested
financial economists due to its practical implications for investors and for the light
it sheds on market efficiency. Two broad conclusions from the literature stand
out. Many studies since Jensen (1968) find that the average returns of mutual
fund portfolios tend to underperform passive benchmarks, especially net of fees.
* Baker, mbaker@hbs.edu, Harvard Business School, Soldiers Field, Boston, MA 02163, and
NBER; Litov, litov@wustl.edu, Washington University in St. Louis, Olin Business School, Campus
Box 1133, St. Louis, MO 63130; W?chter, jwachter@wharton.upenn.edu, University of Pennsylvania,
Wharton School, 3620 Locust Walk, Ste. SH-DH 2300, Philadelphia, PA 19104, and NBER; Wurgler,
jwurgler@stern.nyu.edu, New York University, Stern School of Business, 44 W. 4th St., Ste. 9-190,
New York, NY 10012, and NBER. We thank Stephen Brown (the editor), Susan Christoffersen, Marcin
Kacperczyk, Andrew Metrick, Lasse Pedersen, Robert Stambaugh, Russell Wermers (the referee), Lu
Zheng, and seminar participants at New York University, Yale University, the 2005 European Finance
Association Meeting, the 2005 University of Colorado Investment Conference, and the 2005 Western
Finance Association Meeting for helpful comments. We thank Christopher Blake, Russell Wermers,
and Jin Xu for assistance with data. Baker gratefully acknowledges the Division of Research of the
Harvard Business School for financial support, and all authors thank the Glucksman Institute at NYU
Stern School of Business.
1111
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1112 Journal of Financial and Quantitative Analysis
At the same time, in recent results that are far more encouraging for active fund
managers, Chen, Jegadeesh, and Wermers (2000) find that the individual trades
made by mutual fund managers illustrate some stock-picking skill. In particular,
the stocks that funds buy have higher returns than those that they sell over the next
few quarters.1
Some of the gap between these 2 results simply reflects transaction costs and
management fees. Nonetheless, given the evidence of skilled trading by mutual
fund managers, it is natural to turn to the question of how they manage to distin
guish winners from losers in their trades. We address this question. We build on
the findings of Chen et al. (2000) and other studies of the performance of mutual
fund trades, such as Grinblatt and Titman (1989) and Wermers (1999), by con
structing an alternative method of identifying trading skill. We associate trading
skill with the ability to buy stocks that are about to enjoy high returns upon their
upcoming quarterly earnings announcement and to sell stocks that are about to
suffer low returns upon that announcement.
This approach is complementary to traditional tests using long-horizon re
turns, but it has some advantages. First, it may have more power to detect
trading skill, as it exploits segments of the returns data?returns at earnings
announcements?that contain the most concentrated information about a firm's
earnings prospects. Second, taking as a given the results of Chen et al. (2000) and
others about the abnormal performance of trades over long horizons, the approach
helps identify the source of such abnormal returns?whether they are due to an
ability to forecast fundamental news released around earnings announcements or,
say, proprietary technical signals. Of course, by definition, these benefits come at
the cost of not trying to measure the total returns to trading skill, so the approach
is best seen as a complement to traditional tests.
The main data set merges a comprehensive sample of mutual fund portfolio
holdings with the respective returns that each holding realized at its next quarterly
earnings announcement. The holdings are drawn from mandatory, periodic SEC
filings tabulated by Thomson Financial. For each fund-date-stock holding obser
vation in these data, we merge in the stock return over the 3-day window around
the next earnings announcement. The sample of several million fund-report date
holding observations covers 1980 through 2005.
We begin,the analysis by tabulating the earnings announcement returns re
alized by fund holdings, but as mentioned above, our main results involve fund
trades. Studying trades allows us to difference away unobserved risk premiums
by comparing the subsequent performance of stocks that funds buy with those
they sell, thus reducing Fama's (1970) joint hypothesis problem. Further, trading
1 Obviously, the literature on mutual fund performance is vast and cannot be summarized here.
An abbreviated set of other important studies includes: Ippolito (1989) and Carhart (1997), who con
clude that mutual fund managers have little or no stock-picking skill; Grinblatt and Titman (1993),
Daniel, Grinblatt, Titman, and Wermers (1997), and Wermers (2000), who conclude that a significant
degree of skill exists; and Lehman and Modest (1987) and Ferson and Schadt (1996), who empha
size the sensitivity of results to methodological choices. More recently, Cohen, Covai, and Pastor
(2005), Kacperczyk and Seru (2007), and Kacperczyk, Sialm, and Zheng (2008) have developed
other measures of skill based on holdings, returns that are not observable from Securities and
Exchange Commission (SEC) filings, and the correlation between trades and changes in analyst
recommendations.
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Baker, Litov, W?chter, and Wurgler 1113
incurs costs and perhaps the realization of capital gains, so it is likelier to be driven
by new information than an ongoing holding is. One of our main findings is that
the average mutual fund displays stock-picking skill in that the subsequent earn
ings announcement returns on its weight-increasing stocks are significantly higher
than those on its weight-decreasing stocks. The difference is about 10 basis points
(bp) over the 3-day window around the quarterly announcement, or, multiplying
by 4, about 38 annualized bp. We also benchmark a stock's announcement returns
against those earned by stocks with similar characteristics in that calendar quarter.
The results are not much diminished, with the advantage of buys relative to sells
falling to 9 bp and 34 bp, respectively. This gap reflects skill in both buying and
selling: Stocks bought by the average fund earn significantly higher subsequent
announcement returns than matching stocks, while stocks sold earn lower returns
than matching stocks.
There are interesting differences in performance across funds and across
time. Fund performance measured using earnings announcement returns tends to
persist over time, and funds that do well are more likely to have a growth-oriented
style. These patterns tend to match those from long-horizon studies of fund per
formance, supporting the view that they reflect information-based trading. We
also consider the impact of SEC Regulation Fair Disclosure, which since October
2000 has banned the selective disclosure of corporate information to a preferred
set of investors. After the issuance of this regulation, funds have been less suc
cessful in terms of the earnings announcement returns of their trades, although
the performance of their holdings shows no clear trend.
These results support and extend the evidence of Chen et al. (2000) and oth
ers that fund trades are made with an element of skill. In addition, they strongly
suggest that trading skill derives in part from skill at forecasting earnings fun
damentals. To confirm this link, we test whether trades by mutual funds forecast
quarterly earnings per share (EPS) surprises of the underlying stocks. They do. In
22 of the 22 years in our sample of EPS surprise data, the EPS surprise of stocks
that funds are buying exceeds the EPS surprise of stocks that funds are selling.
When put beside the results from returns, it seems very clear that some portion of
the abnormal returns from fund trades identified in prior work can be attributed to
skill at forecasting fundamentals.
The last question we address is one of economic significance. We ask whether
the abnormal returns to trading around earnings announcements represent a dis
proportionate share of the estimated total abnormal returns earned by stocks that
funds trade. Our analysis suggests that it does. The point estimates are that earn
ings announcement returns constitute between 18% and 51% of the total abnor
mal returns earned by stocks that funds trade. Or, expressed differently, earnings
announcement days are roughly 4-10 times more important than typical days in
terms of their contribution to the abnormal performance of stocks traded by mu
tual funds.
In summary, we present a new methodology that further confirms that the
average mutual fund manager has some ability to pick winners and losers, which
supports and extends prior results; more importantly, we find that a substantial
fraction of the abnormal returns earned by fund trades derives from skill at fore
casting the economic fundamentals of firms (i.e., earnings). The paper proceeds
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1114 Journal of Financial and Quantitative Analysis
as follows. Section II reviews some related literature. Section III presents data.
Section IV presents empirical results. Section V concludes.
II. Related Literature on Trading around Earnings
Announcements
We are not the first to recognize that earnings announcement returns may be
useful for detecting informed trading. Our contribution is to apply this approach
to evaluate the trading skill of mutual funds.
Ali, Durtschi, Lev, and Trombley (2004) examine how changes in institu
tional ownership, broadly defined, forecasts earnings announcement returns. As
this is the study most closely related to ours, it is worth noting some key differ
ences. First, our N-30D data allow us to study performance of individual mutual
funds; Ali et al. use SEC 13F data, which are aggregated at the institutional in
vestor level (e.g., fund family). Second, the 13F data do not permit a reliable
breakdown even among aggregates such as mutual fund families and other insti
tutions of perhaps less interest to retail investors: Many giant fund families, such
as Fidelity, Schwab, and Eaton Vance, are classified in an "other" category, along
with college endowments, pension funds, private foundations, hedge funds, etc.
Third, Ali et al. benchmark announcement returns against size only, while we
use a larger set of adjustments such as book-to-market (BM), an important differ
ence given that La Porta, Lakonishok, Shleifer, and Vishny (1997) find that such
characteristics are associated with higher earnings announcement returns. These
and other differences mean that our approach is more revealing about the stock
picking abilities of individual mutual fund managers, while Ali et al.'s approach
is more useful for an investor who wishes to predict future returns based on recent
changes in total institutional ownership.
The skill of other types of investors has also been assessed from the perspec
tive of earnings announcement returns. Seasholes (2004) examines this dimension
of performance for foreign investors who trade in emerging markets. Ke, Huddart,
and Petroni (2003) track the earnings announcement returns that follow trading by
corporate insiders. Christophe, Ferri, and Angel (2004) perform a similar analysis
for short sellers.
III. Data
A. Data Set Construction
The backbone of our data set is the mutual fund holdings data from Thom
son Financial (also known as CDA/Spectrum S12). Thomson's main source is
the portfolio snapshot contained in the N-30D form each fund periodically files
with the SEC. Prior to 1985, the SEC required each fund to report its portfolio
quarterly, but starting in 1985 it required only semiannual reports.2 The exact re
port dates are set by the fund as suits its fiscal year. At a minimum, the Thomson
2In February 2004, the SEC decided to return to a quarterly reporting requirement. See Elton,
Gruber, and Blake (2010) for a study of the performance of fund holdings using a subset of mutual
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Baker, Litov, W?chter, and Wurgler 1115
data give us semiannual snapshots of all equity holdings for essentially all mu
tual funds. A sample fund-report date-holding observation is as follows: Fidelity
Magellan, as of March 31, 1992, held 190,000 shares of Apple Computer.
Wermers (1999) describes this data set in detail. We extract all portfolio hold
ings reported between the 2nd quarter of 1980 and the 3rd quarter of 2005. Again,
to be clear, we are focused on the fund-level report dates found in the Thomson
data; the particular cut of the Thomson data, the "file date," is not relevant for us.3
To these holdings data we merge in earnings announcement dates from the
CRSP/Compustat merged industrial quarterly database. Specifically, for each
fund-report date-holding observation, we merge in the first earnings announce
ment date that follows that holding's report date.4 We drop observations for which
we can find no earnings announcement date within 90 days after the report date.
Next we add stock returns around each earnings announcement. From CRSP,
we merge in the raw returns over the [-1,+1] trading day interval around each
announcement. We define a market-adjusted event return (MAR) as the raw an
nouncement return minus the contemporaneous return on the CRSP value
weighted market index. We also define a benchmark-adjusted event return (BAR)
as the raw return minus the average [-1, +1] earnings announcement return on
stocks of similar BM, size, and momentum that also announced earnings in the
same calendar quarter as the holding in question. Our approach is similar to that
in Daniel et al. (1997).5 We exclude fund-report dates that do not have at least
1 benchmark-adjusted earnings announcement return; our results are unchanged
if we restrict attention to fund-report dates containing at least 10 or 20 such
returns.
For a subset of the remaining observations, we can obtain fund charac
teristics data. Russell Wermers and Wharton Research Data Services (WRDS)
provided links between the Thomson holdings data and the CRSP mutual fund
database, as described in Wermers (2000). From the CRSP mutual fund data we
funds for which Morningstar requested and obtained monthly holdings data. Elton, Gruber, Blake,
Krasny, and Ozelge (2010) find that defining trades based on changes in quarterly holdings misses
20% of the trades revealed by changes in the Morningstar monthly data. The benefit of the quarterly
holdings data is that it covers a far broader set of funds than does Morningstar.
3The only reason to care about the file date is that Thomson's practice is to report the number of
shares including the effect of any splits that occur between the fund's report date and the file date. To
recover the correct number of shares as of the report date, we undo the effect of such splits using the
Center for Research in Security Prices (CRSP) share adjustment factors.
4Prior to this merge, we create place holder observations for "liquidating" observations in the
holdings data set (i.e., situations in which no holdings of a given stock are reported for the current
report date but positive holdings were reported at the prior report date). This allows us to examine
whether closing a position entirely portends especially poor earnings announcement returns.
5 Specifically, we form the value-weighted average earnings announcement return for each of 125
benchmark portfolios (5x5x5 sorts on BM, size, and momentum) in each calendar quarter. BM
is defined following Fama and French (1995). Market value of equity is computed using the CRSP
monthly file as the close times shares outstanding as of December of the calendar year preceding the
fiscal year data. The BM ratio is then matched from fiscal years ending in year (t - 1) to earnings
announcement returns starting in July of year (t) and from fiscal years ending in (t - 2) to earnings
announcement returns in January through June of year ( . Size is matched from June of calendar
year (t) to returns starting in July of year (i) through June of year (r + 1). Momentum is the return
from month t - 12 through month t - 2. The breakpoints to determine the quintiles on BM, size, and
momentum are based on the New York Stock Exchange (NYSE). The benchmark portfolios include
only stocks with positive book equity that are ordinary common stocks (CRSP share codes 10 or 11).
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1116 Journal of Financial and Quantitative Analysis
take investment objective codes as well as total net assets, turnover, and expense
ratios.6 Christopher Blake shared the data on incentive fees, originally from the
Lipper TASS database, as studied in Elton, Gruber, and Blake (2003). Fee struc
tures are similar across the funds that use them, so we simply study whether the
fund has an incentive fee in place.
Finally, we apply a set of screens to obtain an appropriate sample. Based
on key words in the name of the fund and on reported investment objectives, we
exclude funds that cannot be predominantly characterized as actively managed
U.S. equity funds, such as index, bond, international, and precious metals funds.
We exclude funds with less than $10 million in net asset value. We also exclude
each fund's first report date, as some of our analysis requires lagged portfolio
weights.
B. Summary Statistics
Our final sample consists of several million fund-report date-holding obser
vations with associated earnings announcement returns, spread across 110,236
fund-report dates. Table 1 presents summary statistics. In the first column, the
number of funds has increased dramatically over the sample period. Almost half
of the usable fund-report dates occur in the last 5 years. The next 3 columns in
dicate the distribution of investment objectives. The subsequent 5 columns give
fund holdings and trading activity. For the average fund-report date, we are able
to identify and benchmark 90.0 holdings. Portfolio breadth has increased steadily
over time. On average, 54.0 holdings receive an increase in weight in the portfolio
over that in the prior report, of which 18.7 are first buys. We see that 53.1 holdings
receive a decrease in weight, on average, and 17.0 of these decrease to 0. We also
distinguish the performance of first buys and last sells, since it is particularly clear
that these reflect a deliberate trading decision.
The last columns summarize fund characteristics. Fund size is the total mar
ket value of the fund's reported equity holdings for which we also have earnings
announcement return data. Average size peaks at $85.5 million in 1999. Turnover
is available for 71% of the sample, averages 96.6% per year for the subsample
for which it is available, increases through 2000, and then falls somewhat. The
expense ratio is available for about 76% of the sample, averages 1.27% per year
for the subsample for which it is available, and increases by 42 bp over the pe
riod. The last column indicates the percentage of funds using incentive fees. In the
average year, 1.9% of funds use fees. Elton et al. (2003) report that these funds
account for around 10% of all mutual fund assets. Some of these characteristics
display trends, so we sort funds into quintiles within each reporting period in
some analyses.
6Turnover data for 1991 are missing from the CRSP database. Also, CRSP sometimes reports
several classes of shares for a given fund, corresponding to different fee structures for the same port
folio of stocks (e.g., A, B, C, institutional, and no-load). In these cases, we take the highest reported
value for turnover across all classes to use as the value for turnover, and the value-weighted average
of expenses across all classes as the value for the expense ratio.
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Baker, Litov, W?chter, and Wurgler 1117
TABLE 1
Summary Statistics (1980Q2-2005Q3)
The sample is the intersection of the Spectrum Mutual Fund holdings database, Compustat, and the Center for Research in
Security Prices (CRSP). To be included in the sample, a mutual fund holding must have matched earnings announcement
date and book value from CRSP, and a valid return, market value of equity (price times shares outstanding), past momentum
(return from months ? 12 through f - 2), and 3-day return in the earnings announcement window from CRSP. We compute
terminal holdings for stocks that exit the portfolio. Where possible, we include the investment objective from the CRSP
mutual fund database as determined by CDA Weisenberger or Standard & Poor's (S&P). The investment objective growth
includes codes G, MCG, and LTG from CDA and codes LG and AG from S&P. The investment objective growth and income
includes G-l and GCI from CDA and Gl and IN from S&P. The investment objective income includes I, IEQ, and IFL from
CDA and IN from S&P. We classify each holding as a weight increase or weight decrease. We also record those weight
increases that are first buys (from 0 to positive weight), and those weight decreases that are last sells (from positive weight
to 0). We measure fund size as the total market value (price shares outstanding) of its reported equity holdings; fund
turnover and fund expense ratio from the CRSP mutual fund database; and incentive fees (whether or not the fund has
such a structure) from Elton et al. (2003) and the Upper TASS database. Turnover is missing in CRSP in 1991.
?
1980
385
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
All
828
1,107
908
1,082
1,233
1,375
1,556
1,753
1,853
1,892
2,028
2,253
2,492
2,688
3,237
3,428
3,938
4,819
5,068
6,168
8,414
8,608
9,755
10,913
12,775
10,065
110,236
480
421
507
568
647
727
843
915
954
896
1,025
1,038
1,144
1,252
1,371
1,628
Characteristics
Fund Activity
o
>
Fund
Average
Fund-Report
Date Observations
B? LT
116
148
132
153
185
211
247
285
308
284
371
400
502
454
489
519
579
659
2,087
738
2,326
756
2,619
826
2,929
796
2,819
876
3,295
995
3,912
4,178
1,031
792
3,130
42,096 12,852
22
23
26
47
58
81
139
155
147
140
121
118
178
148
151
143
158
182
216
222
193
150
185
464
493
350
4,310
49.9
50.0
51.0
57.2
57.8
58.7
60.1
62.2
63.8
64.2
64.2
67.1
72.0
79.9
81.0
85.4
84.8
87.0
88.0
85.9
91.3
96.3
101.3
103.6
106.5
108.0
27.5
29.8
30.1
32.1
33.8
34.6
35.0
36.2
38.1
37.9
36.9
37.9
41.0
45.1
47.6
51.1
51.4
54.4
53.3
50.2
56.0
60.1
62.8
61.3
64.6
62.6
28.7 7.0
27.2 6.7
29.6 9.1
34.4 10.5
9.9
33.8
34.2 11.1
36.0 11.8
37.7 12.7
35.7 10.9
37.1 11.9
38.4 11.1
40.8 12.9
43.6 .13.1
49.3 16.2
50.3 18.4
53.7 21.5
6.4
7.0
8.6
9.3
9.9
10.1
10.9
11.7
9.9
10.7
11.2
11.6
12.6
14.5
16.9
19.4
14.2
13.7
14.3
20.1
18.3
21.2
25.6
31.3
26.3
28.9
27.7
32.4
39.3
45.6
39.6
49.8
72.9
68.4
72.6
75.0
71.2
80.5
78.6
94.5
82.2
77.5
88.2
79.5
79.4
81.6
88.5
91.6
91.2
89.0
87.1
118.9
117.0
114.2
112.5
53.8 22.1 20.4 56.4
53.1 23.1 20.5 66.6
54.6 21.4 19.9 81.2
53.5 20.6 17.8 85.5
52.9 19.9 17.6 82.4
55.1 21.8 18.8 60.8
58.1 21.3 19.6 52.6
61.0 20.3 18.7 51.1
60.2 19.3 18.3 65.1 110.1
0.89
0.84
0.88
0.88
0.91
0.92
0.94
1.00
1.14
1.14
1.16
1.06
1.22
1.22
1.21
1.24
1.28
1.27
0.6
1.3
1.7
1.8
1.6
1.8
1.9
2.3
2.3
1.7
1.8
1.7
2.4
2.4
2.0
2.0
2.1
1.7
1.28 1.8
1.30 1.6
1.29 1.5
1.32 1.6
1.38 1.9
1.39 2.0
1.38 2.4
63.0 18.9 17.5 67.6 86.8 1.31 1.5
90.0 54.0 53.1 18.7 17.0 56.6 96.6 1.27 1.?
IV. Results
A. Earnings Announcement Returns of Holdings
We start by summarizing the average performance of mutual fund holdings
around earnings announcements. For reasons discussed in the Introduction, we
are most interested in subsequent earnings announcement performance of stocks
that funds trade and do not just continue to hold, but starting with holdings allow
us to develop the methodology step by step.
Column 1 of Table 2 reports the average raw return over the 3-day window
around earnings announcement dates. Specifically, we take the equal-weighted
average earnings announcement return for each fund-report date, annualize it
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1118 Journal of Financial and Quantitative Analysis
TABLE 2
Annualized Announcement Effects
For each periodic mutual fund holdings report, we compute the average subsequent quarterly earnings announcement
return: raw, market-adjusted return (MAR), and benchmark-adjusted return (BAR); and equal weighted (EW) and value
weighted (VW) across all holdings by fund. The characteristics benchmark return is the corresponding 5x5x5 size, book
to-market, and momentum average earnings announcement return in the matched quarter. We annualize these returns.
(multiplying by 4) and average across all funds within a year, f-statistics in brackets are based on quarterly means and
standard deviations thereof. Returns are Winsorized at the top and bottom 1%.
VW Earnings
Announcement Alpha
EW Earnings
Announcement Alpha
Year
Return,
MAR
BAR
Return
MAR
BAR
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
1.00
0.53
1.29
-0.37
1.37
1.04
1.88
-2.25
0.06
0.04
1.55
1.32
1.83
0.69
0.92
2.24
2.63
3.02
1.63
3.10
-0.76
2.49
0.34
2.23
0.62
1.89
-0.27
0.49
0.23
0.21
-0.16
-0.41
0.38
0.34
-0.13
-0.50
0.51
0.74
0.68
0.74
0.38
0.76
1.65
1.14
0.41
2.46
0.42
0.86
0.91
1.38
0.00
-0.26
0.49
0.25
0.41
-0.15
0.27
-0.76
-0.73
0.15
0.01
-0.31
-0.22
0.05
-0.42
-0.31
-0.09
0.18
0.38
0.84
1.58
-0.62
-0.28
0.17
-0.02
-0.35
0.78
0.29
0.13
-0.03
-0.11
0.63
0.45
0.04
-0.35
0.59
0.53
0.56
0.65
0.47
0.78
1.71
1.18
0.36
2.47
0.50
0.64
1.11
1.05
0.31
0.97
0.23
-0.01
0.50
0.28
0.52
0.11
0.40
-0.80
-0.54
0.26
0.16
-0.35
-0.21
0.04
-0.34
-0.29
-0.02
0.18
0.37
0.83
1.48
-0.46
0.05
-0.04
1.07
0.91
0.84
1.22
-0.45
1.44
1.28
2.03
-2.20
0.21
0.19
1.66
1.15
1.66
0.61
0.97
2.16
2.61
2.90
1.59
3.03
-0.75
2.14
0.55
1.94
0.38
1.67
Avg.
1.16
[3.6]
0.56
[4.7]
1.14
0.59
[5.6]
M
0.45
0.35
0.04
[0.5]
[3.8]
0.12
-0.19
0.09
[1.3]
(multiplying by 4), average these across all fund-report dates within each cal
endar quarter from 1980Q1 through 2005Q3, and, finally, average the quarterly
averages. That is, the average raw return of 1.16 is
1 ?2005Q3 1 _ 1 _ _1
(1) Return = V
4 ?J? 103
> - >^1980Q1
? > > rijti
h
where / indexes mutual funds from 1 to NJ indexes the holdings of mutual fund
from 1 to Ki, and t measures days around the earnings announcement of stock
We treat each quarterly average as a single data point in computing an overal
average. We compute the standard deviation (SD) of the quarterly averages to giv
a r-statistic of 3.6. This is in the spirit of Fama and MacBeth (1973). Taking sim
averages across the pooled data, which gives more weight to the last 5 years of
the sample, leads to similar conclusions.
7Because the sample starts in the 2nd quarter of 1980 and ends in the 3rd quarter of 2005, t
average return for 1980 is for the last 3 quarters, while the average return for 2005 is for the firs
quarters.
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Baker, Litov, W?chter, and Wurgler 1119
Columns 2 and 3 adjust the raw returns. Column 2 reports market-adjusted
returns (MARs), where we subtract the CRSP value-weighted market return over
the earnings announcement window. The average MAR of 0.56 is
(2) MAR = 4.- 0 ^
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