A Comparison of Buy-Side and Sell-Side Analysts

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A Comparison Of Buy-Side And Sell-Side Analysts

By: Jeffrey Hobbs and Vivek Singh

Abstract There is very little research on the topic of buy-side analyst performance, and that which does exist yields mixed results. We use a large sample from both the buy-side and the sell-side and report several new results. First, while the contemporaneous returns to portfolios based on sell-side recommendations are positive, the returns for buy- side analysts, proxied by changes in institutional holdings, are negative. Second, the buy-side analysts' underperformance is accentuated when they trade against sell-side analysts' recommendations. Third, abnormal returns positively relate to both the portfolio size and the portfolio turnover of buy-side analysts' institutions, suggesting that large institutions employ superior analysts and that superior analysts frequently change their recommendations. Abnormal returns are also positively related to buy-side portfolios with stocks that have higher analyst coverage, greater institutional holding, and lower earnings forecast dispersion. Fourth, there is substantial persistence in buy-side performance, but even the top decile performs poorly. These ﬁndings suggest that sell-side analysts still outperform buy-side analysts despite the severe conﬂicts of interest documented in the literature.

Hobbs, J. and V. Singh (2015). "A comparison of buy-side and sell-side analysts." Review of Financial Economics 24: 42-51. https://doi.org/10.1016/j.rfe.2014.12.004. Publisher version of record available at: https:// www.sciencedirect.com/science/article/pii/S1058330015000038 A comparison of buy-side and sell-side analysts

Jeffrey Hobbs a,1, Vivek Singh b,⁎ a Department of Finance, Banking and Insurance, Walker College of Business, Appalachian State University, United States b Department of Accounting and Finance, University of Michigan, Dearborn, United States article info abstract

There is very little research on the topic of buy-side analyst performance, and that which does exist yields mixed results. We use a large sample from both the buy-side and the sell-side and report several new results. First, while the contemporaneous returns to portfolios based on sell-side recommendations are positive, the returns for buy- side analysts, proxied by changes in institutional holdings, are negative. Second, the buy-side analysts' underperformance is accentuated when they trade against sell-side analysts' recommendations. Third, abnormal returns positively relate to both the portfolio size and the portfolio turnover of buy-side analysts' institutions, suggesting that large institutions employ superior analysts and that superior analysts frequently change their recommendations. Abnormal returns are also positively related to buy-side portfolios with stocks that have higher analyst coverage, greater institutional holding, and lower earnings forecast dispersion. Fourth, there is substantial persistence in buy-side performance, but even the top decile performs poorly. These findings suggest that sell-side analysts still outperform buy-side analysts despite the severe conflicts of interest documented in Keywords: the literature. Sell-side analysts Buy-side analysts Institutional investors Analyst recommendations Market efficiency

1. Introduction their firms) are known to rely heavily on their analysts.2 It is under the assumption that buy-side trading is influenced largely by buy-side There is much research on the profitability of sell-side analysts' analyst recommendations that we use institutional portfolio data as recommendations. By contrast, there are only a few papers that our proxy for buy-side analyst behavior. To the extent that institutional investigate the performance of buy-side analysts' recommendations. investors have access to recommendations from sell-side analysts, one Some of the more recent papers in this area have examined buy-side could argue that our proxy is a dirty measure of buy-side analyst recom- performance in the context of sell-side behavior, but most use a lim- mendations. We agree with this argument and we address this concern ited sample, suffer from survivorship bias, or do not fully examine the by concentrating on the buy-side analysts' induced institutional trades link between the buy and sell sides to quantify the part of institutions' that explicitly go against the recommendations of sell-side analysts for profit or loss that is incremental to analyst recommendations. In the much of our analysis. Additionally, by using the Thomson 13F dataset, following study, we fill in this gap by examining a large sample of we restrict ourselves to using quarterly observations, whereas some survivorship-bias-free data from both sides of the market to measure studies using proprietary data (see Frey & Herbst, 2014 or Crawford, the profitability of buy-side analysts in four ways: (1) overall, Gray, Johnson, & Price, 2012, both of which employ daily data) have ac- (2) cross-sectionally, (3) temporally (in terms of both absolute and rel- cess to more detailed information on trading behavior, though for limit- ative persistence), and (4) with respect to changes in sell-side analyst ed time periods. While we acknowledge that the main disadvantages of recommendations, to determine the relative value of the two sides' our dataset and sample construction are not eliminated, we also have behavior. the advantage of more comprehensively comparing sell-side and buy- How do institutions decide which securities to buy and which to sell? side analysts over a long period in a way that contains less selection- The final say goes to the fund manager, but fund managers (as well as or survivorship-bias than is present in much of the recent literature. the investment committees that make trading recommendations to Although some evidence shows that institutional investors in general and mutual funds in particular, and by extension the buy-side analysts that they employ, do not generate positive abnormal returns ⁎ Corresponding author. Tel.: +1 313 583 6533; fax: +1 313 271 9837. E-mail addresses: [email protected] (J. Hobbs), [email protected] (V. Singh). 1 Tel.: +1 828 262 6241; fax: +1 828 262 6612. 2 Analysts are usually members of the investment committees. and thus could be considered non-helpful (or downright harmful, information. Similarly, Frey and Herbst (2014), using a sample from depending on the study) to investors,3 there is considerable evidence one firm, find that fund trading is driven more by buy-side recommen- thatsell-sideanalysisisbeneficial. Among others, Logue and Tuttle dations than by sell-side recommendations or fund flows, and that (1973), Dimson and Marsh (1984), Elton, Gruber, and Grossman those trades that are triggered by buy-side recommendations yield the (1986), Womack (1996), Barber, Lehavy, McNichols, and Trueman highest excess returns. Last, Crawford et al. (2012) use data from an (2001, 2003),andJegadeesh, Kim, Krische, and Lee (2004) find changes invitation-only, Facebook-style event and conclude that buy-side rec- in analysts' recommendations to be consistent with subsequent abnor- ommendations do have investment value, particularly for firms with mal stock returns. Jegadeesh and Kim (2006) find that analyst recom- small capitalizations and high book-to-market ratios. mendations are profitable, particularly for those analysts based in the In contrast, Groysberg, Healy, Serafeim, Shanthikumar, and Gui U.S., while Howe, Unlu, and Yan (2009) find recommendations to (2010) compare buy-siders at one large firm to sell-siders on I/B/E/S contain profitable information extending beyond other variables that and conclude that buy-side recommendations are less optimistic, are related to future returns. Despite the evidence in support of sell- owing to fewer conflicts of interest, and interestingly are less profitable. side analysts, there are also papers that document some problems, usu- However, the authors attribute this lower profitability to the fact that ally stemming from optimism bias or conflicts of interest, with analyst buy-siders recommend larger firms, on average, than do sell-siders. recommendations. Both Lin and McNichols (1998) and Michaely and Similarly, Busse, Green, and Jegadeesh (2012) examine a more compre- Womack (1999) document such conflicts; for example, the latter hensive sample of buy- and sell-side activities and find that sell-side an- study finds that underwriter analysts exhibit significant optimism alyst recommendations are profitable while buy-side activity is largely bias for those stocks that they've underwritten. Malmendier and alpha-neutral. The authors conclude that institutional trading offers no Shanthikumar (2014) find that strategic optimism bias (the bias created incremental value to sell-side stock recommendations. There are at by conflicts of interest) appears to be intentional, widespread, and eco- least two shortcomings of the papers that rely on limited samples. nomically substantial. Conrad, Cornell, Landsman, and Rountree (2006) First, the data are often provided by the institutions themselves and, find that optimism bias is also related to past events and is reflected in as Busse et al. (2012) point out, survivorship bias plagues much of the the asymmetric response of analysts to the directions of large stock buy-side research. Second, although some of these studies report that price moves in the past. Jegadeesh et al. (2004) find that analyst recom- buy-side analysts generate superior recommendations, much of the mendation levels relate positively to future returns only for those stocks research finds that the typical institution that hires buy-side analysts with upward momentum or contrarian characteristics. Additionally, the does not generate positive abnormal returns. This paper addresses authors find that analyst recommendation levels relate inversely to fu- these issues. In particular, we attempt to provide a more comprehensive ture returns for other stocks in their dataset. Last, Altinkilic and Hansen understanding of the link between buy-side and sell-side analysts along (2009) and Altinkilic, Balashov, and Hansen (2013) find that revisions three dimensions. First, we examine the nature of buy-side induced in- to analyst recommendations are typically reactive rather than predic- stitutional trading and sell-side analyst recommendations individually tive and thus are of little trading value to investors. with respect to the same-quarter movements of those stocks. Second, In addition to the evidence regarding aggregate performance, there we examine the behavior of the two sides with respect to one another are also well-documented cross-sectional differences between stock in the context of the stocks that they trade or recommend. Third, we analysts. Analyst performance has been tied to the accuracy of earnings examine the persistence of performance among buy-side analysts. We forecasts (Ertimur, Sunder, & Sunder, 2007 and Loh & Mian, 2006), past believe that our approach offers new evidence, from a more compre- performance (Fang & Yasuda, 2009 and Loh & Stulz, 2011), the frequen- hensive dataset including different types of institutions as well as ana- cy of recommendation revisions (Hobbs, Kovacs, & Sharma, 2012), lyst recommendations, on all of these important topics. and incentive distortions caused by investment banking affiliations The rest of our paper is structured as follows. Section 2 describes the (Barber, Lehavy, & Trueman, 2007). data and methods that we use. Section 3 addresses the topic of buy- and Many recent studies have compared buy-side profitability to sell- sell-side behaviors individually and with respect to one another. side recommendations to obtain a clearer picture of the overall process Section 4 investigates the issue of persistence. Section 5 concludes the of institutional trading. Although much of this research uses proprietary study. data from other parts of the buy-side previously ignored by the mutual fund studies, they often use limited datasets and yield differing results. 2. Material and methods Cheng, Liu, and Qian (2006) find that funds rely more on buy-side research than on sell-side or independent research, and that abnormal 2.1. Sample data returns increase in buy-side reliance and experience. These funds also value buy-side research more when the sell-side focuses on small stocks Our institutional (“buy-side”) data come from the Thomson Finan- or has larger forecast errors and forecast dispersion. Kacperczyk and cial 13-F filings. These filings include information on the quarterly Seru (2007) report that the funds that have more skilled managers stock holdings, beginning in the first quarter of 1994, of all financial in- tend to prefer private (buy-side) information to public (sell-side) stitutions that manage more than $100 million in securities.4 Although only equity positions of more than either 10,000 shares or $200,000 in market capitalization are required to be reported, many institutions 3 The research on the investment performance of institutions (the “buy-side”) dates back to Jensen (1968) and continues through well-known studies by Lakonishok, Shleifer, voluntarily provide data on all of their holdings. We then examine the and Vishny (1992), Carhart (1997), Fama and French (2008), and Busse et al. (2010), changes in these buy-side investment portfolios over 64 quarters, among others. There is some debate over how much of the persistence in performance from the first quarter of 1994 through the fourth quarter of 2009. We (good or bad) is attributable to the momentum effect of stocks remaining in their respec- use the Institutional Brokers' Estimates System (hereafter denoted tive mutual funds after those funds have been designated winners or losers on the basis of “ ” “ ” the past performance of those same stocks. Carhart (1997), Barras, Scaillet, and Wermers IBES ) to gather our analyst recommendation ( sell-side ) data over (2009),andFama and French (2008) show very little (if any) persistence or predictability the same period. We match the 8-character CUSIP variable in the detailed in the performance of mutual funds that is incremental to the momentum effect of the recommendation file in IBES to that in the Center for Research in Security stocks in them. However, Berk and Xu (2007), Bollen and Busse (2005), Cohen, Coval, Prices (“CRSP”, hereafter) database and then use the 6-character CUSIP if and Pastor (2005), Avramov and Wermers (2006),andKosowski, Timmermann, Wermers, and White (2006) all find evidence that mutual fund performance is to some degree predictable. Binay (2005) uses Thomson 13F holdings data and finds that while mutual funds do not trade profitably, other institutional investors do. Chen et al. (2000), 4 The start of the data period is dictated by the IBES detailed recommendation files (re- however, use 13F quarterly holdings data to find some evidence of mutual fund trading quired to infer buy-side recommendations), whose first complete year of data begins in success. 1994. that fails to produce a match. Our sample excludes ADRs (American seasoned equity offering in the current quarter.5 We remove such Depositary Receipts), SBI (Shares of Beneficial Interest), Units (Deposito- stock-quarters from our final sample. ry Units, Units of Beneficial Interest, Units of Limited Partnership Interest, Because the performance of many institutions is benchmarked Depository Receipts, etc.), REITs (Real Estate Investment Trusts), and against stock market indexes, part of the institutional trading in stocks closed-end mutual funds. For both the buy-side and the sell-side, we may be explained by indexing. To remove the potentially confounding do not censor or winsorize any of the variables in our dataset. effect of indexing on our results, we eliminate all Inst(i,t) that are affect- ed by additions and deletions to the S&P 500 index during the sample 2.2. Classifying buy-side behavior period. In order to do this we download a file containing all additions and deletions to the S&P 500 index from Professor Jeffrey Wurgler's For each quarter, we infer buy-side analyst activity by observing the website. This file contains each stock's company identifier, addition or information-based trading done by the institution that employs the deletion flag, and the announcement date and effective date for its addi- analyst. Specifically, if a stock is bought (sold) by the institution, we tion to (or deletion from) the index, among other variables. If the institu- assume that the affiliated buy-side analyst(s) must have upgraded tional trading in a stock is related to that stock's addition to (or deletion (downgraded) that stock in the same quarter. The exact procedure to from) the S&P 500 index, we expect most of the trading to occur around identify stocks that are upgraded (downgraded) by the analysts is the quarter of the addition/deletion. We determine the quarter and year outlined as follows. First we identify for each institutional investor of the addition or deletion for all stocks in this sample using the effective whether each stock is bought (sold) in a quarter by measuring the date and then merge it with our sample of stocks. We exclude from our change in its holding in the same quarter. Since institutional investors sample all stock-quarters in which additions or deletions occurred. do not buy or sell a stock due to informational reasons only, we employ We acknowledge that although we make attempts to identify several adjustments to ensure that our identification of trades is not information-driven trades only and use the above-mentioned filters, contaminated by other motives for institutional trading. The first adjust- we may be misclassifying some trades that are driven by other motives ments control for the return on the existing holding of stock i and such as managerial risk shifting due to differential performance across the net flow of funds to the institutional investor during the current parts of the year because of compensation incentives, employment quarter, t. For each quarter, we use the following formula, which was risk motives or economic activity (Chevalier & Ellison, 1997; Brown, inspired by Barber, Odean, and Zhang (2005), and Griffin, Harris, Shu, Harlow, & Starks, 1996; Chevalier & Ellison, 1999; Kempf, Ruenzi, and and Topaloglu (2011), to determine whether for each stock i in quarter Thiele, 2009; Pagani, Hu, Kale, and Subramanian, 2011; Olivier and t an institutional investor K is a buyer (Inst(i,t) is positive) or a seller Tay, 2009). Similarly some trading could be attributed to herding (Inst(i,t) is negative). (Grinblatt, Titman, & Wermers, 1995; Nofsinger & Sias, 1999; Wermers, 1999). Some trading may also be related to Window dressing NX¼m NX¼m and tax-loss selling (Sias, 2007). − ðÞþ Si;tPi;t Si;tPi;t−1 1 r S ; P ; −S ; − P ; − ðÞ1 þ r ¼ ¼ InstðÞ¼ i; t i t i t i t 1 i t 1 − i 1 i 1 NX¼m Si;t−1Pi;t−1 2.3. Classifying sell-side analyst behavior Si;t−1Pi;t−1 ¼ i 1 To determine whether a stock was upgraded or downgraded by sell-side analysts in a quarter, we measure the change in the consensus An institution K that is a net buyer (seller) of stock i in quarter t will recommendation at the end of each quarter using the IBES detailed yield a positive (negative) value for the above expression. In other analyst recommendation files. To compute the consensus recommenda- words a positive (negative) value is consistent with buy-side analysts tion for a stock, we use all outstanding recommendations by all analysts upgrading (downgrading) the stock in the same quarter. S and S − i,t i,t 1 that are less than one year old, per Jegadeesh et al. (2004). We focus on are the number of shares held by institutional investor K in stock i at upgrades and downgrades rather than on overall levels since the change the end of quarters t and t−1 respectively. P and P − are the share i,t i,t 1 in recommendations has been found to be more informative than the price of stock i at the end of quarters t and t−1, respectively. The return level of those recommendations (Jegadeesh et al., 2004). Additionally, on stock i in the current quarter t is captured by r. Thus the first term in prior research shows that analysts issue optimistic recommendations the expression in (2) represents net buying or selling in stock i in quar- in the year that a stock first comes to the market. To mitigate the influ- ter t by institutional investor K as a fraction of its initial holding of the ence of this bias in analyst recommendations, we remove any recom- stock, adjusted for the return on stock i in the current quarter. The sec- mendation that is issued within one year from the date that a firm ond term represents the net buying or selling by the same institution went public. across all stocks traded in quarter t. If institutional investor K faces an in- flow (outflow) of funds in quarter t, we expect the second term to be positive (negative). Therefore this expression measures, for each stock 2.4. Methods in each quarter, the buying or selling that is in excess of the net flow of funds to institutional investor K. We also adjust the share holdings Our examination of buy-side analysts is based on their institutions' and prices for outside factors that affect the number of shares, such as portfolios of stocks, whose abnormal returns we derive by estimating stock splits and stock dividends. time-series regressions of the contemporaneous and subsequent Further, we construct the following filters to ensure that our results quarter's excess return as benchmarked against the CAPM, Fama and based on Inst(i,t) are unaffected by trades that occurred for non- French (1993) three-factor model, and Carhart (1997) four-factor information-based reasons. For example, as a new stock comes to the model. For sell-side analysts, we simply examine the abnormal perfor- market via an initial public offering in a given quarter, most institutional mance of portfolios of stocks upgraded or downgraded as implied by investors will be net buyers. To correct for this problem, we consider the change in those analysts' recommendations. We use aggregate per- Inst(i,t) for only those stocks that had also existed in the previous quarter, formance rather than a typical sell-side analyst's performance because t−1. For similar reasons, we exclude Inst(i,t) for those cases in which a there is much research indicating that the typical sell-side analyst gen- stock ceases to exist because of a merger,acquisition,delistingetc. erates positive alpha. However, the issue of aggregate buy-side analyst Similar to IPOs, seasoned equity offerings may also affect Inst(i,t). performance is far from settled. In addition, it could be argued that the We assume that if the number of shares outstanding (adjusted for stock splits, stock dividends, etc.) for a stock has increased by more 5 We also use a more liberal 10% increase to proxy for seasoned equity offerings. It does than 25% from the previous quarter, then the firm has had a potential not materially affect our results. buy-side benefits from the research of sell-side analysts. In some of the Table 1 following sections of this paper these abnormal returns will be contem- Descriptive statistics. The table below reports some of the main features of our sample. The sample period is poraneous with the behavior of buy-side analysts, and in other cases we from the first quarter of 1994 to the last quarter of 2009. We derive buy-side analyst data compute them subsequent to the quarter in which the behavior occurs. from the changes in quarterly institutional holding data for each manager reported in 13F For each section, we clarify whether the returns that we examine are as detailed in the text, and sell-side analyst data from the IBES detailed recommendation contemporaneous or subsequent. In some tables we also provide abnor- files. We determine whether a stock was upgraded or downgraded by sell-side analysts “ ” mal returns for the first and last months of the contemporaneous (which results in a change in consensus recommendation ) for the end of each quarter. To compute the consensus recommendation for a stock, we average all outstanding ana- quarters. We alternatively compute quarterly abnormal returns using lyst recommendations that are less than one year old. In Panel A we report the distribution the CAPM, Fama and French (1993) three-factor model, and Carhart of upgrades, downgrades, and non-changes for buy-side and sell-side analysts in a typical (1997) four-factor model as our benchmarks. In all cases, we use quarter. In Panel B we report the size of the portfolio managed by a typical fund manager monthly returns data as the basis for these calculations. We download and turnover classified by the category of institution. The portfolio turnover is computed for each manager in each quarter per the formula given in the CRSP files. the factors from Kenneth French's website. For example, to compute the buy-side analysts' upgraded stocks' abnormal returns, we first iden- Panel A: Buy-side and sell-side analysts' recommendation changes tity the stocks upgraded/downgraded as detailed above and then form Trade/recommendation Buy-side analysts' Sell-side analysts' an equally-weighted and a value-weighted portfolio of those stocks. recommendations recommendations

We then regress the excess returns of these two portfolios on the factors Buy/upgrade 50.04% 39.72% prescribed under the CAPM, Fama–French three-factor model, and No change N/A 24.37% Carhart (1997) four-factor model. For instance, to determine the four- Sell/downgrade 49.96% 35.90% factor abnormal return of an equally-weighted portfolio of upgraded Panel B: Managers' portfolio sizes and turnover stocks for buy-side analyst “i”, we regress his or her portfolio's quarterly excess return on the three factors of Fama and French (1993) and the Type of #of Portfolio size Portfolio turnover momentum factor in Carhart (1997) as follows: r − r = α + β institution unique (in millions of dollars) i,t f,t i i managers (rm,t − rf,t)+γi (smbt)+δi (hmlt)+ωi (momt)+εi,t, where ri,t −rf,t Mean Median Standard Mean Median Standard is the excess return of equally-weighted portfolios of stocks upgraded deviation deviation by analyst i over the risk-free rate in quarter t; the first factor, rm, Banks 354 8470 8593 3087 23.7 20.4 8.1 Insurance 111 8465 8953 2607 31.0 29.0 10.3 t − rf,t (or RMRFt) denotes the return in excess of the value- firms weighted return on all NYSE, AMEX and NASDAQ stocks over the Mutual 139 25,781 25,308 13,809 43.9 39.6 14.5 three-month T-bill yield in quarter t; the second factor, smbt,isthe funds difference between the returns of small and large capitalization Brokerage 2298 2537 2472 793 37.5 33.6 10.9 Others 2027 3218 2183 2885 30.0 30.3 6.2 stocks in quarter t; the third factor, hmlt, is the difference between the returns of high and low book-to-market stocks in quarter t; and All 3931 4173 922 35.6 31.5 9.8 the fourth factor, momt, is the difference between the returns of stocks with high and low recent returns in quarter t. The intercept of the above time-series regressions is analyst i's alpha or abnormal given the incentives that prevail within the mutual fund industry, it return. Following a similar procedure, we compute an analyst's should be noted that multiple mutual funds within the same company Fama–French three-factor and CAPM abnormal returns. We then are aggregated, thus the extremely high portfolio sizes shown for that compute the alpha for analyst i's downgraded stocks as well as for subgroup of institution. a zero-investment portfolio that goes long in analyst i's upgraded stock and short in his or her downgraded stocks. We repeat the en- 3.1.2. Buy-side analysts' recommendations and the performance of tire analysis for value-weighted portfolios. Our analysis of the invest- institutional portfolios ment performance of buy-side analysts is based on the cross- We now examine the contemporaneous returns, benchmarked sectional mean of analysts' abnormal returns as calculated above. against the CAPM, Fama–French three-factor, and Carhart four-factor For sell-side analysts, we simply compute the alpha for portfolios models, of portfolios formed on the basis of institutional trading. We of stocks that were upgraded or downgraded in a particular quarter use institutional trading as a proxy for buy-side analyst behavior. The as indicated by the change in the sell-side analysts' consensus rec- results in Table 2 are interesting: portfolios of stocks downgraded ommendations. As we did with buy-side analysts, we form zero- by buy-side analysts outperform the three benchmarks in the same investment portfolios by going long in upgraded and short in quarter, while portfolios of stocks upgraded by buy-side analysts downgraded stocks. The analysis is again conducted for both equal- underperform those benchmarks. All of these results are highly signifi- ly-weighted and value-weighted portfolios using the CAPM, Fama– cant. Although the underperformance is stronger for equally-weighted French three-factor, and Carhart four-factor models. portfolios, the value-weighted portfolios are only marginally better. Given the nature of our data it is difficult to say for sure whether buy- 3. Results and discussion side upgrades/downgrades on average lead, lag or occur at roughly the same time that the institutional portfolios are generating such ex- 3.1. When do buy-side analysts and sell-side analysts change cess returns. However, it is notable that for downgraded stocks, which recommendations? make up the majority of the overall abnormal return to institutions, the highest returns occur in the first month of the quarter (we do not 3.1.1. Descriptive statistics report returns for the second month, but they can be inferred from Table 1 provides descriptive statistics for our sample of institutions the rest of the results), and are much higher than in the third month. and analysts. Not surprisingly, the distribution among institutions is However, the returns in the first and last months are the sign that is fairly even between purchases and sales, as is the distribution among consistent with the quarterly returns for downgraded and upgraded sell-side analysts between recommendation upgrades and downgrades. stocks. These results suggest that the recommendations of buy-side Panel B gives a further breakdown of the managers, portfolio sizes, and analysts are harmful to the portfolios of the institutional traders that portfolio turnover for our subsample of institutions. Though there is a they advise. far greater number of managers within the brokerage firm and “all An alternative explanation is that buy-side analysts have a longer other” subgroups, the highest turnover and largest portfolios belong to investment horizon than do sell-side analysts or they are contrarian the mutual fund subgroup. While the turnover numbers are expected investors, and therefore one should examine the abnormal returns Table 2 Profitability of portfolios constructed from contemporaneous changes in buy-side analysts' recommendations. The table below reports the quarterly contemporaneous and one quarter ahead abnormal returns of stocks upgraded and downgraded by buy-side analysts. The sample period is from the first quarter of 1994 to the last quarter of 2009. We infer buy-side analysts' recommendations from the changes in quarterly institutional holding data for each manager reported in 13F as detailed in the text. We obtain the quarterly excess returns by regressing the equally-weighted and value-weighted excess returns of each institution's upgraded or downgraded stocks' portfolios on the excess returns from the Carhart 4-factor model, Fama–French 3-factor model, and CAPM, all downloaded from Kenneth French's website. We report the intercepts from these regressions below. We report t-statistics in parentheses.

Mean quarterly and monthly abnormal returns of equally-weighted portfolios downgraded and upgraded by buy-side analysts (%) (t-statistic)

CAPM abnormal return Fama–French abnormal return Carhart abnormal return