《金融投资学》教学资源（英文文献）can investors profit from the prophets

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THE JOURNAL OF FINANCE.VOL.LVI.NO.2 APRIL 2001 Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns BRAD BARBER,REUVEN LEHAVY,MAUREEN McNICHOLS, and BRETT TRUEMAN* ABSTRACT We document that purchasing(selling short)stocks with the most(least)favorable consensus recommendations,in conjunction with daily portfolio rebalancing and a timely response to recommendation changes,yield annual abnormal gross returns greater than four percent.Less frequent portfolio rebalancing or a delay in react- ing to recommendation changes diminishes these returns;however,they remain significant for the least favorably rated stocks.We also show that high trading levels are required to capture the excess returns generated by the strategies ana- lyzed,entailing substantial transactions costs and leading to abnormal net returns for these strategies that are not reliably greater than zero. THIS STUDY EXAMINES WHETHER INVESTORS can profit from the publicly available recommendations of security analysts.Academic theory and Wall Street practice are clearly at odds regarding this issue.On the one hand,the semi- strong form of market efficiency posits that investors should not be able to trade profitably on the basis of publicly available information,such as ana- lyst recommendations.On the other hand,research departments of broker- age houses spend large sums of money on security analysis,presumably because these firms and their clients believe its use can generate superior returns. Barber is an associate professor at the Graduate School of Management,University of California,Davis;Lehavy is an assistant professor at the Haas School of Business,University of California,Berkeley;MeNichols is a professor at the Graduate School of Business,Stanford University;and Trueman is the Donald and Ruth Seiler Professor of Public Accounting at the Haas School of Business,University of California,Berkeley.We thank Jeff Abarbanell,Sudipto Basu,Bill Beaver,George Foster,Charles Lee,Terry Odean,Sheridan Titman,Russ Wermers, Kent Womack,the editor,Rene Stulz,and participants at the October 1998 NBER(Behavioral Finance)conference,the ninth annual Conference on Financial Economics and Accounting at NYU,the Berkeley Program in Finance(Behavioral Finance)conference,Barclay's Global In- vestors,Baruch College,Mellon Capital Management,Stanford University,Tel Aviv University, the Universities of British Columbia,Florida,and Houston,and UCLA,for their valuable com- ments,and Zacks Investment Research for providing the data used in this study.Lehavy and Trueman also thank the Center for Financial Reporting and Management at the Haas School of Business and McNichols thanks the Financial Research Initiative of the Stanford Graduate School of Business for providing research support.All remaining errors are our own. 531

Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns BRAD BARBER, REUVEN LEHAVY, MAUREEN McNICHOLS, and BRETT TRUEMAN* ABSTRACT We document that purchasing ~selling short! stocks with the most ~least! favorable consensus recommendations, in conjunction with daily portfolio rebalancing and a timely response to recommendation changes, yield annual abnormal gross returns greater than four percent. Less frequent portfolio rebalancing or a delay in reacting to recommendation changes diminishes these returns; however, they remain significant for the least favorably rated stocks. We also show that high trading levels are required to capture the excess returns generated by the strategies analyzed, entailing substantial transactions costs and leading to abnormal net returns for these strategies that are not reliably greater than zero. THIS STUDY EXAMINES WHETHER INVESTORS can profit from the publicly available recommendations of security analysts. Academic theory and Wall Street practice are clearly at odds regarding this issue. On the one hand, the semistrong form of market efficiency posits that investors should not be able to trade profitably on the basis of publicly available information, such as analyst recommendations. On the other hand, research departments of brokerage houses spend large sums of money on security analysis, presumably because these firms and their clients believe its use can generate superior returns. * Barber is an associate professor at the Graduate School of Management, University of California, Davis; Lehavy is an assistant professor at the Haas School of Business, University of California, Berkeley; McNichols is a professor at the Graduate School of Business, Stanford University; and Trueman is the Donald and Ruth Seiler Professor of Public Accounting at the Haas School of Business, University of California, Berkeley. We thank Jeff Abarbanell, Sudipto Basu, Bill Beaver, George Foster, Charles Lee, Terry Odean, Sheridan Titman, Russ Wermers, Kent Womack, the editor, Rene Stulz, and participants at the October 1998 NBER ~Behavioral Finance! conference, the ninth annual Conference on Financial Economics and Accounting at NYU, the Berkeley Program in Finance ~Behavioral Finance! conference, Barclay’s Global Investors, Baruch College, Mellon Capital Management, Stanford University, Tel Aviv University, the Universities of British Columbia, Florida, and Houston, and UCLA, for their valuable comments, and Zacks Investment Research for providing the data used in this study. Lehavy and Trueman also thank the Center for Financial Reporting and Management at the Haas School of Business and McNichols thanks the Financial Research Initiative of the Stanford Graduate School of Business for providing research support. All remaining errors are our own. THE JOURNAL OF FINANCE • VOL. LVI, NO. 2 • APRIL 2001 531

532 The Journal of Finance These observations provide a compelling empirical motivation for our in- quiry and distinguish our analysis from many recent studies of stock return anomalies.1 In contrast to many of these studies,which focus on corporate events,such as stock splits,or firm characteristics,such as recent return performance,that are not directly tied to how people invest their money,we analyze an activity-security analysis-that is undertaken by investment professionals at hundreds of major brokerage houses with the express pur- pose of improving the return performance of their clients. The possibility that there could exist profitable investment strategies based on the publicly available recommendations of security analysts is suggested by the findings of Stickel (1995)and Womack (1996),who show that favor- able (unfavorable)changes in individual analyst recommendations are ac- companied by positive(negative)returns at the time of their announcement.2 Additionally,they document a post-recommendation stock price drift,which Womack finds to last up to one month for upgrades and six months for downgrades. Our paper's perspective,however,is different from that of Stickel and Womack.Their primary goal is to measure the average price reaction to changes in individual analysts'recommendations;therefore,they take an analyst and event-time perspective.This approach can only provide evidence as to whether,absent transactions costs,profitable investment strategies could potentially be designed around those recommendations.In contrast, we take a more investor-oriented,calendar-time perspective.This permits us to directly measure the abnormal gross returns to a number of invest- ment strategies and to estimate portfolio turnover and the associated trans- actions costs incurred in implementing them.Consequently,we are able to determine whether investors can earn positive abnormal profits on these strategies after accounting for transactions costs. By measuring turnover and assessing whether investors can generate ab- normal returns net of trading costs on the various stock market investment strategies we examine,our analysis contributes to the market efficiency debate. Our methodology could easily be extended to the study of other strategies, such as those based on price momentum or the post-earnings announcement drift. We focus on the profitability of investment strategies involving consensus (average)analyst recommendations.The consensus is a natural choice,as it takes into account the information implicit in the recommendations of all the analysts following a particular stock.It is arguably the analyst statistic that is most easily accessed by investors,as it appears on many Internet 1 See Fama(1998)for a review and critique of this body of work. 2Other papers examining the investment performance of security analysts'stock recommen- dations are Diefenbach(1972),Bidwell(1977),Groth et al.(1979),Dimson and Marsh(1984), and Barber and Loeffler(1993).Copeland and Mayers(1982)study the investment performance of the Value Line Investment Survey and Desai and Jain(1995)analyze the return from fol- lowing Barron's annual roundtable recommendations

These observations provide a compelling empirical motivation for our inquiry and distinguish our analysis from many recent studies of stock return anomalies.1 In contrast to many of these studies, which focus on corporate events, such as stock splits, or firm characteristics, such as recent return performance, that are not directly tied to how people invest their money, we analyze an activity—security analysis—that is undertaken by investment professionals at hundreds of major brokerage houses with the express purpose of improving the return performance of their clients. The possibility that there could exist profitable investment strategies based on the publicly available recommendations of security analysts is suggested by the findings of Stickel ~1995! and Womack ~1996!, who show that favorable ~unfavorable! changes in individual analyst recommendations are accompanied by positive ~negative! returns at the time of their announcement.2 Additionally, they document a post-recommendation stock price drift, which Womack finds to last up to one month for upgrades and six months for downgrades. Our paper’s perspective, however, is different from that of Stickel and Womack. Their primary goal is to measure the average price reaction to changes in individual analysts’ recommendations; therefore, they take an analyst and event-time perspective. This approach can only provide evidence as to whether, absent transactions costs, profitable investment strategies could potentially be designed around those recommendations. In contrast, we take a more investor-oriented, calendar-time perspective. This permits us to directly measure the abnormal gross returns to a number of investment strategies and to estimate portfolio turnover and the associated transactions costs incurred in implementing them. Consequently, we are able to determine whether investors can earn positive abnormal profits on these strategies after accounting for transactions costs. By measuring turnover and assessing whether investors can generate abnormal returns net of trading costs on the various stock market investment strategies we examine, our analysis contributes to the market efficiency debate. Our methodology could easily be extended to the study of other strategies, such as those based on price momentum or the post-earnings announcement drift. We focus on the profitability of investment strategies involving consensus ~average! analyst recommendations. The consensus is a natural choice, as it takes into account the information implicit in the recommendations of all the analysts following a particular stock. It is arguably the analyst statistic that is most easily accessed by investors, as it appears on many Internet 1 See Fama ~1998! for a review and critique of this body of work. 2 Other papers examining the investment performance of security analysts’ stock recommendations are Diefenbach ~1972!, Bidwell ~1977!, Groth et al. ~1979!, Dimson and Marsh ~1984!, and Barber and Loeffler ~1993!. Copeland and Mayers ~1982! study the investment performance of the Value Line Investment Survey and Desai and Jain ~1995! analyze the return from following Barron’s annual roundtable recommendations. 532 The Journal of Finance

Security Analyst Recommendations and Stock Returns 533 financial Web sites (such as CBS.MarketWatch.com and Yahoo!Finance)and is incorporated into the databases of several financial information providers (such as Dow Jones Interactive). The data used in this paper come from the Zacks database for the period 1985 to 1996,which includes over 360,000 recommendations from 269 bro- kerage houses and 4,340 analysts.As such,our study uses a much larger sample of analyst recommendations than has been employed in past re- search.Stickel,by comparison,studies the price impact of 16,957 changes in analyst recommendations over the 1988 to 1991 period,and Womack ana- lyzes the impact of 1,573 changes in analyst recommendations for the top 14 U.S.brokerage research departments during the 1989 to 1991 period. With the Zacks database,we track in calendar time the investment per- formance of firms grouped into portfolios according to their consensus ana- lyst recommendations.Every time an analyst is reported as initiating coverage, changing his or her rating of a firm,or dropping coverage,the consensus recommendation of the firm is recalculated and the firm moves between portfolios,if necessary.Any required portfolio rebalancing occurs at the end of the trading day.This means that investors are assumed to react to a change in consensus recommendation at the close of trading on the day that the change took place.Consequently,any return that investors might have earned from advance knowledge of the recommendations(or from trading in the recommended stocks at the start of the trading day)is excluded from the return calculations. For our sample period we find that buying the stocks with the most favor- able consensus recommendations earns an annualized geometric mean re- turn of 18.8 percent,whereas buying those with the least favorable consensus recommendations earns only 5.78 percent (see Figure 1).As a benchmark, during the same period an investment in a value-weighted market portfolio earns an annualized geometric mean return of 14.5 percent.Alternatively stated,the most highly recommended stocks outperform the least favorably recommended ones by 102 basis points per month. After controlling for market risk,size,book-to-market,and price momen- tum effects,a portfolio comprised of the most highly recommended stocks provides an average annual abnormal gross return of 4.13 percent whereas a portfolio of the least favorably recommended ones yields an average an- nual abnormal gross return of-4.91 percent.Consequently,purchasing the securities in the top portfolio and selling short those in the lowest portfolio yields an average abnormal gross return of 75 basis points per month.3 By comparison,over the same period,high book-to-market stocks outperform low book-to-market stocks by a mere 17 basis points,and large firms out- 3 If large institutional clients were to gain access to,and trade on,analysts'recommenda- tions before they were made public,their investment value would be even greater.This is due to the strong market reaction that immediately follows the announcement of a recommenda- tion.(The magnitude of this reaction for our sample of analyst recommendations is documented in Table III.)

financial Web sites ~such as CBS.MarketWatch.com and Yahoo!Finance! and is incorporated into the databases of several financial information providers ~such as Dow Jones Interactive!. The data used in this paper come from the Zacks database for the period 1985 to 1996, which includes over 360,000 recommendations from 269 brokerage houses and 4,340 analysts. As such, our study uses a much larger sample of analyst recommendations than has been employed in past research. Stickel, by comparison, studies the price impact of 16,957 changes in analyst recommendations over the 1988 to 1991 period, and Womack analyzes the impact of 1,573 changes in analyst recommendations for the top 14 U.S. brokerage research departments during the 1989 to 1991 period. With the Zacks database, we track in calendar time the investment performance of firms grouped into portfolios according to their consensus analyst recommendations. Every time an analyst is reported as initiating coverage, changing his or her rating of a firm, or dropping coverage, the consensus recommendation of the firm is recalculated and the firm moves between portfolios, if necessary. Any required portfolio rebalancing occurs at the end of the trading day. This means that investors are assumed to react to a change in consensus recommendation at the close of trading on the day that the change took place. Consequently, any return that investors might have earned from advance knowledge of the recommendations ~or from trading in the recommended stocks at the start of the trading day! is excluded from the return calculations. For our sample period we find that buying the stocks with the most favorable consensus recommendations earns an annualized geometric mean return of 18.8 percent, whereas buying those with the least favorable consensus recommendations earns only 5.78 percent ~see Figure 1!. As a benchmark, during the same period an investment in a value-weighted market portfolio earns an annualized geometric mean return of 14.5 percent. Alternatively stated, the most highly recommended stocks outperform the least favorably recommended ones by 102 basis points per month. After controlling for market risk, size, book-to-market, and price momentum effects, a portfolio comprised of the most highly recommended stocks provides an average annual abnormal gross return of 4.13 percent whereas a portfolio of the least favorably recommended ones yields an average annual abnormal gross return of 24.91 percent. Consequently, purchasing the securities in the top portfolio and selling short those in the lowest portfolio yields an average abnormal gross return of 75 basis points per month.3 By comparison, over the same period, high book-to-market stocks outperform low book-to-market stocks by a mere 17 basis points, and large firms out- 3 If large institutional clients were to gain access to, and trade on, analysts’ recommendations before they were made public, their investment value would be even greater. This is due to the strong market reaction that immediately follows the announcement of a recommendation. ~The magnitude of this reaction for our sample of analyst recommendations is documented in Table III.! Security Analyst Recommendations and Stock Returns 533

Security Analyst Recommendations and Stock Returns 535 The second set of alternative strategies retains daily portfolio rebalancing but assumes a delayed reaction by investors to all changes in analysts'con- sensus recommendations-of either one week,a half-month,or a full month. We show that a delay of either one week or a half month decreases the average annual abnormal gross return on the portfolio of the most highly recommended stocks to around two percent,whereas a month's delay re- duced it to less than one percent.None of these returns is reliably greater than zero.In contrast,the average annual abnormal gross return on the portfolio of the least favorably rated stocks remains significantly negative for all delay periods examined,standing at over -4 percent for a one-week delay and about -2 percent for either a half month's or a full month's delay.These results highlight the importance to investors of acting quickly to capture the gross returns on the highest rated stocks. None of the returns documented thus far take into account transactions costs,such as the bid-ask spread,brokerage commissions,and the market impact of trading.As we show,under the assumption of daily rebalancing, purchasing the most highly recommended securities or shorting the least favorably recommended ones requires a great deal of trading,with turnover rates at times in excess of 400 percent annually.After accounting for trans- actions costs,these active trading strategies do not reliably beat a market index.Restricting these trading strategies to the smallest firms (whose ab- normal gross returns are shown to be the highest)does not alter this con- clusion;transactions costs remain very large,and abnormal net returns are not significantly greater than zero.Rebalancing less frequently does reduce turnover significantly (falling below 300 percent for monthly rebalancing). But,because the abnormal gross returns fall as well,abnormal net returns are still not reliably greater than zero,in general.Despite the lack of posi- tive net returns to the strategies we examine,analyst recommendations do remain valuable to investors who are otherwise considering buying or sell- ing.Ceteris paribus,an investor would be better off purchasing shares in firms with more favorable consensus recommendations and selling shares in those with less favorable consensus ratings. Although a large number of trading strategies are investigated and none are found to yield positive abnormal net returns,our analysis by no means rules out the possibility that profitable trading strategies exist.It remains an open question whether other strategies based on analysts'recommenda- tions (or based on a subset of analysts'recommendations,such as those of the top-ranked analysts or the largest brokerage houses),or even whether the strategies studied here,but applied to different time periods or different stock recommendation data,will be able to generate positive abnormal net returns. The plan of this paper is as follows.In Section I,we describe the data and our sample selection criteria.A discussion of our research design follows in Section II.In Section III,we form portfolios according to consensus analyst recommendations and analyze their returns.The impact of investment de- lays on the returns available to investors is considered in Section IV.In

The second set of alternative strategies retains daily portfolio rebalancing but assumes a delayed reaction by investors to all changes in analysts’ consensus recommendations—of either one week, a half-month, or a full month. We show that a delay of either one week or a half month decreases the average annual abnormal gross return on the portfolio of the most highly recommended stocks to around two percent, whereas a month’s delay reduced it to less than one percent. None of these returns is reliably greater than zero. In contrast, the average annual abnormal gross return on the portfolio of the least favorably rated stocks remains significantly negative for all delay periods examined, standing at over 24 percent for a one-week delay and about 22 1 2 _ percent for either a half month’s or a full month’s delay. These results highlight the importance to investors of acting quickly to capture the gross returns on the highest rated stocks. None of the returns documented thus far take into account transactions costs, such as the bid-ask spread, brokerage commissions, and the market impact of trading. As we show, under the assumption of daily rebalancing, purchasing the most highly recommended securities or shorting the least favorably recommended ones requires a great deal of trading, with turnover rates at times in excess of 400 percent annually. After accounting for transactions costs, these active trading strategies do not reliably beat a market index. Restricting these trading strategies to the smallest firms ~whose abnormal gross returns are shown to be the highest! does not alter this conclusion; transactions costs remain very large, and abnormal net returns are not significantly greater than zero. Rebalancing less frequently does reduce turnover significantly ~falling below 300 percent for monthly rebalancing!. But, because the abnormal gross returns fall as well, abnormal net returns are still not reliably greater than zero, in general. Despite the lack of positive net returns to the strategies we examine, analyst recommendations do remain valuable to investors who are otherwise considering buying or selling. Ceteris paribus, an investor would be better off purchasing shares in firms with more favorable consensus recommendations and selling shares in those with less favorable consensus ratings. Although a large number of trading strategies are investigated and none are found to yield positive abnormal net returns, our analysis by no means rules out the possibility that profitable trading strategies exist. It remains an open question whether other strategies based on analysts’ recommendations ~or based on a subset of analysts’ recommendations, such as those of the top-ranked analysts or the largest brokerage houses!, or even whether the strategies studied here, but applied to different time periods or different stock recommendation data, will be able to generate positive abnormal net returns. The plan of this paper is as follows. In Section I, we describe the data and our sample selection criteria. A discussion of our research design follows in Section II. In Section III, we form portfolios according to consensus analyst recommendations and analyze their returns. The impact of investment delays on the returns available to investors is considered in Section IV. In Security Analyst Recommendations and Stock Returns 535

536 The Journal of Finance Section V we estimate the transactions costs of following the strategies of buying the most highly rated stocks and selling short those that are least favorably rated and discuss the profitability of these strategies.We partition our sample by firm size and reexamine the returns to our strategies in Sec- tion VI.A summary and conclusions section ends the paper. I.The Data,Sample Selection Criteria, and Descriptive Statistics The analyst recommendations used in this study were provided by Zacks Investment Research,which obtains its data from the written and electronic reports of brokerage houses.The recommendations encompass the period from 1985(the year that Zacks began collecting this data)through 1996. Each database record includes,among other items,the recommendation date, identifiers for the brokerage house issuing the recommendation and the an- alyst writing the report (if the analyst's identity is known),and a rating between 1 and 5.A rating of 1 reflects a strong buy recommendation,2 a buy,3 a hold,4 a sell,and 5 a strong sell.This five-point scale is commonly used by analysts.If an analyst uses a different scale,Zacks converts the analyst's rating to its five-point scale.Ratings of 6 also appear in the Zacks database and signify termination of coverage. Another characteristic of the database,one that has not been explicitly acknowledged in any prior study as far as we are aware,is that the data made available to academics does not constitute Zacks'complete set of rec- ommendations.According to an official at Zacks,some individual brokerage houses have entered into agreements that preclude their recommendations from being distributed by Zacks to anyone other than the brokerage houses' clients.Consequently,although the recommendations of most large and well- known brokers are included,the recommendations of several large broker- age houses are not part of this academic database (although they are represented in Zacks'consensus statistics).4,5 The Zacks database contains 378,326 observations for the years 1985 through 1996.Dropping those for the 1,286 firms not appearing on the CRSP file leaves a final sample of 361,620 recommendations.Table I provides descrip- tive statistics for these recommendations.As shown in column 3,the number of firms covered by Zacks has increased steadily over the years.For the year 4 For the first year in which we compute recommendation returns,1986,the Zacks database includes the recommendations of 12 of the 20 largest brokerage houses,in terms of capital employed.(Capital levels are taken from the Securities Industry Yearbook (1987,1997).)The capital of these 12 brokerage houses comprises 54 percent of the total capital of these largest houses.For the last year of recommendation returns,1996,the Zacks database includes the recommendations of 12 of the 19 largest brokerage houses(the 20th does not prepare analyst recommendations),whose capital comprises 49 percent of the total capital of these largest houses. 5 Supplementary tests performed using the First Call database(which includes these large brokerage house recommendations)suggest that these omissions do not have a significant effect on our results.See footnote 20

Section V we estimate the transactions costs of following the strategies of buying the most highly rated stocks and selling short those that are least favorably rated and discuss the profitability of these strategies. We partition our sample by firm size and reexamine the returns to our strategies in Section VI. A summary and conclusions section ends the paper. I. The Data, Sample Selection Criteria, and Descriptive Statistics The analyst recommendations used in this study were provided by Zacks Investment Research, which obtains its data from the written and electronic reports of brokerage houses. The recommendations encompass the period from 1985 ~the year that Zacks began collecting this data! through 1996. Each database record includes, among other items, the recommendation date, identifiers for the brokerage house issuing the recommendation and the analyst writing the report ~if the analyst’s identity is known!, and a rating between 1 and 5. A rating of 1 reflects a strong buy recommendation, 2 a buy, 3 a hold, 4 a sell, and 5 a strong sell. This five-point scale is commonly used by analysts. If an analyst uses a different scale, Zacks converts the analyst’s rating to its five-point scale. Ratings of 6 also appear in the Zacks database and signify termination of coverage. Another characteristic of the database, one that has not been explicitly acknowledged in any prior study as far as we are aware, is that the data made available to academics does not constitute Zacks’ complete set of recommendations. According to an official at Zacks, some individual brokerage houses have entered into agreements that preclude their recommendations from being distributed by Zacks to anyone other than the brokerage houses’ clients. Consequently, although the recommendations of most large and wellknown brokers are included, the recommendations of several large brokerage houses are not part of this academic database ~although they are represented in Zacks’ consensus statistics!. 4,5 The Zacks database contains 378,326 observations for the years 1985 through 1996. Dropping those for the 1,286 firms not appearing on the CRSP file leaves a final sample of 361,620 recommendations. Table I provides descriptive statistics for these recommendations. As shown in column 3, the number of firms covered by Zacks has increased steadily over the years. For the year 4 For the first year in which we compute recommendation returns, 1986, the Zacks database includes the recommendations of 12 of the 20 largest brokerage houses, in terms of capital employed. ~Capital levels are taken from the Securities Industry Yearbook ~1987, 1997!.! The capital of these 12 brokerage houses comprises 54 percent of the total capital of these largest houses. For the last year of recommendation returns, 1996, the Zacks database includes the recommendations of 12 of the 19 largest brokerage houses ~the 20th does not prepare analyst recommendations!, whose capital comprises 49 percent of the total capital of these largest houses. 5 Supplementary tests performed using the First Call database ~which includes these large brokerage house recommendations! suggest that these omissions do not have a significant effect on our results. See footnote 20. 536 The Journal of Finance

Table I Descriptive Statistics on Analyst Recommendations from the Zacks Database, 1985 to 1996 The number of listed firms includes all firms listed on the CRSP NYSE0AMEX0Nasdaq stock return file, by year. The number of covered firms is the number of firms with at least one valid recommendation in the Zacks database, by year. The number of covered firms is also expressed as the percent of the number of listed firms. The market capitalization of covered firms as a percent of the total market capitalization is the average daily ratio between the sum of the market capitalizations of all covered firms and the market value of all securities used in the CRSP daily value-weighted indices. The mean and median number of analysts issuing recommendations for each covered firm is shown, as is the mean and median number of firms covered by each analyst in the database, by year. This is followed by the number of brokerage houses and number of analysts with at least one recommendation during the year. The last column is the average of all analyst recommendations in the database for the year. Covered Firms Analysts per Covered Firm Covered Firms per Analyst Year ~1! No. of Listed Firms ~2! No. of Covered Firms ~3! As a % of Listed Firms ~4! Market Cap. As % of Market ~5! Mean ~6! Median ~7! Mean ~8! Median ~9! No. of Brokers ~10! No. of Analysts ~11! Average Rating ~12! 1985 6,826 1,841 27.0 68.8 2.66 2 10 7 26 492 2.52 1986 7,281 2,989 41.1 85.3 4.25 3 13 10 61 960 2.37 1987 7,575 3,163 41.8 89.0 4.53 3 13 10 74 1,080 2.28 1988 7,573 3,226 42.6 90.5 4.75 3 13 10 96 1,171 2.32 1989 7,304 3,066 42.0 91.2 4.15 3 12 9 95 1,032 2.35 1990 7,138 3,105 43.5 92.3 4.50 3 13 10 98 1,082 2.34 1991 7,171 3,201 44.6 93.0 5.18 3 13 11 120 1,270 2.36 1992 7,459 3,546 47.5 93.8 5.09 3 12 10 131 1,452 2.23 1993 7,964 4,097 51.4 93.5 5.50 3 13 11 151 1,700 2.22 1994 8,494 4,611 54.3 93.9 5.61 3 13 11 169 2,007 2.09 1995 8,857 5,129 57.9 94.6 5.37 3 13 11 188 2,144 2.11 1996 9,408 5,628 59.8 95.6 5.27 3 13 11 195 2,367 2.04 Average All Years 7,754 3,634 46.1 90.1 4.74 3 13 10 117 1,396 2.27 Security Analyst Recommendations and Stock Returns 537

538 The Journal of Finance 1996,59.8 percent of all firms on the NYSE,AMEX,or Nasdag have at least one recommendation in the database(column 4).The market capitalization of these firms constitute 95.6 percent of the capitalization of all firms in the market (column 5).This is consistent with the conventional wisdom that analysts tend to cover larger firms,because they offer more liquidity and allow the analysts'clients to more easily take large positions in the firms' shares (which,in turn,generates larger commissions revenues for the bro- kerage houses). From 1986 onward,the mean number of analysts per covered firm has generally been increasing (column 6),whereas the median number has re- mained constant (column 7).The mean and median number of covered firms per analyst has also been stable(columns 8 and 9).Additionally,the number of brokerage houses contributing recommendations to Zacks and the number of analysts providing forecasts has steadily increased over time(columns 10 and 11).The last column of the table reports the average of all of the analyst ratings,by year.It shows a rather steady decrease over time,indicating that analysts'recommendations have become more favorable.6 A 6 x 6 transition matrix of the analysts'recommendations appears in Table II.Each cell fi,j}of the matrix contains two numbers.The top one is the number of observations in the database in which an analyst moved from a recommendation of i to one of j;the bottom number is the median number of calendar days between the announcement of a recommendation of i and a revised recommendation ofj.The diagonal elements of the matrix reflect reiterations of analyst recommendations.Most of the entries in this matrix are concentrated in the upper 3 x 3 cells.This is to be expected,given the conventional wisdom that analysts are reluctant to issue sell recommenda- tions.Within this region,the bulk of the observations represent reiterations. The mean time between a recommendation and its reiteration is a little less than 300 days.This is much longer than the mean time between a recom- mendation and a revision by the analyst to a new rating,which is generally in the low 100-day range.To the extent that the Zacks database does not record all reiterations,such a difference is not surprising. The line entitled "First Zacks Recommendation"records the first recom- mendation in the database for a given analyst-company pair.Consistent with McNichols and O'Brien (1998),the first recommendation is usually a buy (1 or 2),less often a hold,and rarely a sell(4 or 5).This again reflects the reluctance of analysts to issue sell recommendations.This observation is also consistent with the numbers in the last two lines of the table.Of all the recommendations in the database,47.1 percent are buys whereas only 5.7 per- cent are sells.Excluding observations with a rating of 6,buys constitute 54.1 percent of the total,whereas sells make up only 6.5 percent. 6 The year 1985 has,by far,the smallest number of covered firms,brokerage houses,and analysts,likely because it is the first year that Zacks began tracking recommendations.Because the 1985 data is so sparse,we do not include the investment returns from that year in our analysis

1996, 59.8 percent of all firms on the NYSE, AMEX, or Nasdaq have at least one recommendation in the database ~column 4!. The market capitalization of these firms constitute 95.6 percent of the capitalization of all firms in the market ~column 5!. This is consistent with the conventional wisdom that analysts tend to cover larger firms, because they offer more liquidity and allow the analysts’ clients to more easily take large positions in the firms’ shares ~which, in turn, generates larger commissions revenues for the brokerage houses!. From 1986 onward, the mean number of analysts per covered firm has generally been increasing ~column 6!, whereas the median number has remained constant ~column 7!. The mean and median number of covered firms per analyst has also been stable ~columns 8 and 9!. Additionally, the number of brokerage houses contributing recommendations to Zacks and the number of analysts providing forecasts has steadily increased over time ~columns 10 and 11!. The last column of the table reports the average of all of the analyst ratings, by year. It shows a rather steady decrease over time, indicating that analysts’ recommendations have become more favorable.6 A 6 3 6 transition matrix of the analysts’ recommendations appears in Table II. Each cell $i, j% of the matrix contains two numbers. The top one is the number of observations in the database in which an analyst moved from a recommendation of i to one of j; the bottom number is the median number of calendar days between the announcement of a recommendation of i and a revised recommendation of j. The diagonal elements of the matrix reflect reiterations of analyst recommendations. Most of the entries in this matrix are concentrated in the upper 3 3 3 cells. This is to be expected, given the conventional wisdom that analysts are reluctant to issue sell recommendations. Within this region, the bulk of the observations represent reiterations. The mean time between a recommendation and its reiteration is a little less than 300 days. This is much longer than the mean time between a recommendation and a revision by the analyst to a new rating, which is generally in the low 100-day range. To the extent that the Zacks database does not record all reiterations, such a difference is not surprising. The line entitled “First Zacks Recommendation” records the first recommendation in the database for a given analyst–company pair. Consistent with McNichols and O’Brien ~1998!, the first recommendation is usually a buy ~1 or 2!, less often a hold, and rarely a sell ~4 or 5!. This again reflects the reluctance of analysts to issue sell recommendations. This observation is also consistent with the numbers in the last two lines of the table. Of all the recommendations in the database, 47.1 percent are buys whereas only 5.7 percent are sells. Excluding observations with a rating of 6, buys constitute 54.1 percent of the total, whereas sells make up only 6.5 percent. 6 The year 1985 has, by far, the smallest number of covered firms, brokerage houses, and analysts, likely because it is the first year that Zacks began tracking recommendations. Because the 1985 data is so sparse, we do not include the investment returns from that year in our analysis. 538 The Journal of Finance

Security Analyst Recommendations and Stock Returns 539 Table II Transition Matrix of Analyst Recommendations (Number,Median Calendar Days),1985 to 1996 This table shows the number and the median calendar days between changes in or reiterations of recommendations.The first row reports all changes from a recommendation of 1 ("strong buy")to 1,2("buy"),3("hold"),4("sell"),5("strong sell")or discontinuation of coverage,and the total across the columns.The sixth and seventh rows identify recommendations for firms that were previously dropped from coverage and for firms for which coverage was initiated in the database.Fractional recommendations are rounded to the nearest whole value. To Recommendation of: From Recommendation of: 2 ￥ 5 Dropped Total 34,939 15,269 16,887 538 805 9,802 78,240 293 109 128 140 135 121 2 14,010 21.936 17.581 1,349 468 8,177 63,521 95 299 115 106 111 121 3 12.945 14,492 52,813 3,971 2,958 15,332 102,511 113 112 291 114 116 123 4 480 1,180 3,913 2,936 668 1,097 10,274 132 103 98 245 98 135 5 396 316 2,739 439 1.409 1,143 6,442 95 105 94 90 301 99 Dropped 4,951 3,507 5,999 546 400 5,013 20,416 73 65 92 102 110 59 First Zacks 26,053 19,817 24,458 2.392 1,531 5.965 80.216 recommendation Total 93,774 76,517 124,390 12,171 8,239 46,529 361.620 of total 25.9 21.2 34.4 3.4 2.3 12.9 of non-drops 29.8 24.3 39.5 3.9 2.6 We also compute the average three-day announcement period return for changes in or initiations of analyst recommendations.These returns are pre- sented in Table III.Similar to the results of Stickel (1995)and Womack (1996),we find that the compound(size-adjusted)return for the three-day period centered on the day a rating change is announced is,in general,sig- nificantly positive for upgrades and significantly negative for downgrades.7 7 Using the First Call database,Womack(1996)reports three-day returns that are much higher in magnitude than those documented here.This is consistent with his assertion that there are occasional delays in the recording of some of the recommendations in the Zacks data- base.(The difference may also be because Womack's sample consists only of large brokerage house recommendations.If these recommendations are accorded more publicity,this could lead to the market reaction being larger in the few days around their announcement.)As we report in footnote 20,though,supplementary tests using First Call data suggest that any timing issues surrounding Zacks do not have a significant effect on our main results

We also compute the average three-day announcement period return for changes in or initiations of analyst recommendations. These returns are presented in Table III. Similar to the results of Stickel ~1995! and Womack ~1996!, we find that the compound ~size-adjusted! return for the three-day period centered on the day a rating change is announced is, in general, significantly positive for upgrades and significantly negative for downgrades.7 7 Using the First Call database, Womack ~1996! reports three-day returns that are much higher in magnitude than those documented here. This is consistent with his assertion that there are occasional delays in the recording of some of the recommendations in the Zacks database. ~The difference may also be because Womack’s sample consists only of large brokerage house recommendations. If these recommendations are accorded more publicity, this could lead to the market reaction being larger in the few days around their announcement.! As we report in footnote 20, though, supplementary tests using First Call data suggest that any timing issues surrounding Zacks do not have a significant effect on our main results. Table II Transition Matrix of Analyst Recommendations (Number, Median Calendar Days), 1985 to 1996 This table shows the number and the median calendar days between changes in or reiterations of recommendations. The first row reports all changes from a recommendation of 1 ~“strong buy”! to 1, 2 ~“buy”!, 3 ~“hold”!, 4 ~“sell”!, 5 ~“strong sell”! or discontinuation of coverage, and the total across the columns. The sixth and seventh rows identify recommendations for firms that were previously dropped from coverage and for firms for which coverage was initiated in the database. Fractional recommendations are rounded to the nearest whole value. To Recommendation of: From Recommendation of: 1 2 3 4 5 Dropped Total 1 34,939 15,269 16,887 538 805 9,802 78,240 293 109 128 140 135 121 2 14,010 21,936 17,581 1,349 468 8,177 63,521 95 299 115 106 111 121 3 12,945 14,492 52,813 3,971 2,958 15,332 102,511 113 112 291 114 116 123 4 480 1,180 3,913 2,936 668 1,097 10,274 132 103 98 245 98 135 5 396 316 2,739 439 1,409 1,143 6,442 95 105 94 90 301 99 Dropped 4,951 3,507 5,999 546 400 5,013 20,416 73 65 92 102 110 59 First Zacks recommendation 26,053 19,817 24,458 2,392 1,531 5,965 80,216 Total 93,774 76,517 124,390 12,171 8,239 46,529 361,620 % of total 25.9 21.2 34.4 3.4 2.3 12.9 % of non-drops 29.8 24.3 39.5 3.9 2.6 Security Analyst Recommendations and Stock Returns 539

540 The Journal of Finance Table II Three-day Percentage Market-adjusted Returns Associated with Announcements of Changes in and Reiterations of Analyst Recommendations,1985 to 1996 This table shows the percentage market-adjusted returns measured for the day before,the day of,and the day following changes in and reiterations of analyst recommendations.For example, the first row reports the returns associated with all changes from a recommendation of 1 (strong buy)to 1,2(buy),3(hold),4(sell),5(strong sell),or discontinuation of coverage.Re- turns are measured as the three-day buy and hold return less the return on a value-weighted NYSE/AMEX/Nasdaq index.The sixth and seventh rows show the returns associated with recommendations for firms that were previously dropped from coverage,and for firms for which coverage was initiated,respectively.Fractional recommendations are rounded to the nearest whole value.t-statistics,estimated using cross-sectional standard errors,are shown below the returns.Each t-statistic pertains to the hypothesis that the mean size-adjusted abnormal re- turn is zero.(The number of observations in each cell is shown in Table II.) To Recommendation of: From Recommendation of: 1 2 4 分 Dropped 1 0.177 -0.889 -2.192 -1.305 -3.021 -0.020 7.525 -17.448 -32.841 -4.129 -6.792 -0.364 1.059 0.114 -1.415 -0.638 -0.999 0.115 21.565 3.809 -25.876 -3.154 -2.187 2.135 1.488 1.066 0.015 -1.054 -0.976 0.112 27.895 22.877 0.788 -10.195 -5.926 2.630 4 0.723 0.610 0.610 -0.130 -0.336 0.393 3.388 4.105 6.908 -1.399 -1.226 2.347 0.607 1.296 0.400 -0.283 -0.005 0.207 2.113 4.384 3.487 -0.964 -0.032 0.999 Dropped 0.637 0.301 0.051 -1.168 -0.474 8.586 3.533 -0.810 -4.728 -1.463 First Zacks 1.093 0.479 -0.149 -0.209 -0.650 recommendation 29.445 13.150 -4.736 -2.135 -4.384 Furthermore,for the set of initial analyst-company recommendations in the database,a buy rating (1 or 2)is accompanied by a significantly positive return,as expected,whereas a hold or sell rating (3,4,or 5)is associated with a significantly negative return. II.Research Design A.Portfolio Construction To determine whether investors can profit from analysts'consensus rec- ommendations,we construct calendar-time portfolios based on the consen- sus rating of each covered firm.The average analyst rating,Ar,for firm i on date-1 is found by summing the individual ratings,Ar,of the

Furthermore, for the set of initial analyst–company recommendations in the database, a buy rating ~1 or 2! is accompanied by a significantly positive return, as expected, whereas a hold or sell rating ~3, 4, or 5! is associated with a significantly negative return. II. Research Design A. Portfolio Construction To determine whether investors can profit from analysts’ consensus recommendations, we construct calendar-time portfolios based on the consensus rating of each covered firm. The average analyst rating, AN it21, for firm i on date t 2 1 is found by summing the individual ratings, Aijt21, of the Table III Three-day Percentage Market-adjusted Returns Associated with Announcements of Changes in and Reiterations of Analyst Recommendations, 1985 to 1996 This table shows the percentage market-adjusted returns measured for the day before, the day of, and the day following changes in and reiterations of analyst recommendations. For example, the first row reports the returns associated with all changes from a recommendation of 1 ~strong buy! to 1, 2 ~buy!, 3 ~hold!, 4 ~sell!, 5 ~strong sell!, or discontinuation of coverage. Returns are measured as the three-day buy and hold return less the return on a value-weighted NYSE0AMEX0Nasdaq index. The sixth and seventh rows show the returns associated with recommendations for firms that were previously dropped from coverage, and for firms for which coverage was initiated, respectively. Fractional recommendations are rounded to the nearest whole value. t-statistics, estimated using cross-sectional standard errors, are shown below the returns. Each t-statistic pertains to the hypothesis that the mean size-adjusted abnormal return is zero. ~The number of observations in each cell is shown in Table II.! To Recommendation of: From Recommendation of: 1 2 3 4 5 Dropped 1 0.177 20.889 22.192 21.305 23.021 20.020 7.525 217.448 232.841 24.129 26.792 20.364 2 1.059 0.114 21.415 20.638 20.999 0.115 21.565 3.809 225.876 23.154 22.187 2.135 3 1.488 1.066 0.015 21.054 20.976 0.112 27.895 22.877 0.788 210.195 25.926 2.630 4 0.723 0.610 0.610 20.130 20.336 0.393 3.388 4.105 6.908 21.399 21.226 2.347 5 0.607 1.296 0.400 20.283 20.005 0.207 2.113 4.384 3.487 20.964 20.032 0.999 Dropped 0.637 0.301 0.051 21.168 20.474 8.586 3.533 20.810 24.728 21.463 First Zacks recommendation 1.093 0.479 20.149 20.209 20.650 29.445 13.150 24.736 22.135 24.384 540 The Journal of Finance

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