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 This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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. This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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 This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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 This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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). This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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. This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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 This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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. This content downloaded from 168.245.150.131 on Mon, 20 Aug 2018 16:14:18 UTC All use subject to https://about.jstor.org/terms 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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