Industries, Analysts, and Price Momentum
LESLIE BONI and Kent L. Womack*
Preliminary Version 1.02, please do not cite without permission
Comments Welcome
* Boni is at the University of New Mexico and Womack is at the Tuck School of Business at Dartmouth. We especially acknowledge the expert research assistance of Bob Burnham and helpful comments by Bhaskaran Swaminathan and seminar participants at Babson, Colorado and Penn State. We thank Keith Ferry at Validea.com for data on analyst recommendations. We also appreciate data provided by I/B/E/S and Ken French. Comments and questions should be sent to or . An updated version of the paper will be found at http://mba.tuck.dartmouth.edu/pages/faculty/kent.womack/workpaper.htm and http://www.unm.edu/~boni/RPAWP/RPAWP.htm.
Industries, Analysts, and Price Momentum
Abstract
This paper examines the competition among sell-side analysts, who are predominately industry specialists, to provide investment value through their recommendations within their followed industries. Earlier “unconditional” research using event studies and consensus levels suggested that modest abnormal returns before transactions costs were available to investors using recommendation information. Our industry-based analysis shows the potential for even larger and more consistent abnormal returns. We also distinguish between analysts’ value for intra-industry stock picking and the value of their information in identifying industry momentum or sector rotation strategies.
We find that long-short industry-based portfolios using buy-sell consensus levels are ineffective at garnering significant excess returns, but monthly recommendation changes (upgrades and downgrades) within industries provide significant market neutral one-month-ahead returns that are 1.3%, or 18.9% annualized. However, we show that the value of these analyst opinion changes dissipates rapidly, over about 2 to 3 months.
At an industry level, aggregated analyst information precedes excess industry returns in the subsequent months. This suggests considerable promise for the use of analyst recommendations in explaining a substantial portion of price momentum as an industry phenomenon and in implementing sector rotation strategies.
JEL Classification: G14, G24
Keywords: SellSide Research; Value of Brokerage Recommendations; Market Efficiency; Price Momentum; Sector Rotation.
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Brokerage research (often called “sell-side research”) departments are predominantly organized by industry. Thus, with few exceptions, the Wall Street analysts that write reports, estimate earnings, help underwrite new issues, and issue buy and sell recommendations are considered industry specialists. Not surprisingly then, analysts are regularly compared and evaluated relative to their competitors in the same industry. The Institutional Investor All-Star rankings within each industry published each October are a prime example of the reputational rewards that accrue to the most highly regarded analysts in each industry. Krigman, Shaw, and Womack (2001) show that Institutional Investor All-Star reputations of industry-specific analysts are instrumental in garnering highly profitable investment-banking business for the star analyst’s firm.
This study examines recommendation upgrades and downgrades of sell-side security analysts that are one of the most prominent sources of the information flow that investors appear to use in revising their portfolios. And, they use them with good reason. Earlier research has shown that trading volume doubles and returns increase 1-3%, on average, on days when analysts from the major U.S. brokerage firms upgrade their recommendations on individual stocks. Abnormal volumes and price decreases are even larger when stocks are downgraded (for example, from “buy” to “hold”). Barber, Lehavy, McNichols, and Trueman (2001), Stickel (1995), and Womack (1996) provide estimates of the value changes in this increased activity: returns for stocks upgraded continue to increase (after risk and market adjustments) and stocks downgraded continue to decrease for a month or more after the recommendation change. But, it should be noted that this challenge to available-information market efficiency is not unique to analysts’ recommendations. Bernard and Thomas (1989) and Gleason and Lee (2002) for example, show that the market is slow to incorporate and incorrectly interprets the information contained in earnings estimate revisions as well. And, the literature on price momentum begun by Jegadeesh and Titman (1993) possibly suggests that the market incorporates “some information” into market prices slowly, with a lag of at least a few months. “Some information” may be, in part, the opinions of sell-side analysts.
The focus of this paper is the value investors may derive by observing the competition among industry analysts to provide timely and accurate opinions on the stocks they follow. For small capitalization companies, this competition may include only one, two, or three analysts; but, for heavily-traded, large capitalization stocks such as Microsoft and General Electric, the “herd” may be as many as 30 or more analysts. Higher reputation and presumably higher pay reward the winners of this stock-picking competition. Indeed, the All-American research poll conducted by Institutional Investor has a special recognition category for “stock picking ability”.
We examine two particular questions that have relevance to investors trying to gain a risk-reward edge. First, we examine whether industry analysts are able to identify future winners and losers within their industry specializations. Since analysts typically follow a small number (usually from 5 to 30) stocks in one industry, it would be reasonable to assume that their stock picking ability, if any exists, should most likely be in ranking the stocks in their industry sector as winners and losers. Earlier research, which focused primarily on portfolios and event studies, were not well industry-diversified. Barber, Lehavy, McNichols, and Trueman (hereafter Barber, et.al., 2001) found excess returns before transactions costs by constructing unconstrained “buy” and “sell/unattractive” portfolios. Indeed, we find that such an approach, choosing portfolios based on the highest and lowest absolute consensus levels is quite industry-unbalanced and loads heavily on momentum. Stickel (1995) and Womack (1996) identified upgraded and downgrades stocks but did little industry-based analysis. Hence, earlier analyses did little to answer the question of industry “skill”.
While we do not exhaust all or even the most optimal strategies for maximizing future return, we find that a simple long-short strategy of buying upgraded firms and selling downgraded firms within each industry in any calendar month yields a substantial 1.3% per month in the next calendar month and cumulatively 2.4% over 3 months. These returns are well diversified, computed across all industry-months, and have no large common risk factor loadings. This is in contrast to earlier portfolio-based attempts that have significant risk factor loadings and industry imbalances.
The second investment question that we address is whether observing the collective recommendation changes or consensus levels by all analysts within an industry provides value in capturing sector rotation or industry momentum. As mentioned earlier, one of the largest challenges to market efficiency is the phenomenon of price momentum. Moskowitz and Grinblatt (1999) argue that much of the observed price momentum previously documented by Jegadeesh and Titman (1993) and others is industry based. Our data provide an indication of the momentum (correlation) in industry information that may be driving industry price momentum. We find that conditioning long-short portfolios on the basis of “hot” industries with recommendation momentum (whether the industry was net upgraded or downgraded in the formation month) earns 1.56% per month, or about 3% more annually than the “all industry” case in the previous paragraph. Our findings that future intra-industry and industry sector returns are predictable and significant when using the information content in brokerage upgrades and downgrades suggests a possible informational explanation for price momentum that has not been previously explored.
Our examination, of course, does not exhaust all the potential strategies that investors might employ using recommendation information, and it does not provide evidence that these returns are fully realizable after transactions costs. However, the magnitude of our returns, relative to earlier price momentum and post-earnings announcement drift studies, shows considerable promise of the potential for predicting future returns from analysts’ pronouncements. We conjecture that recommendations are a proxy for the primary source of information Jegadeesh and Titman (1993) claim as a fundamental explanation for price momentum.
The rest of the paper proceeds as follows. Section 1 provides the institutional background on analysts and their recommendations and Section 2 describes the data and sample selection methods used in the paper. Section 3 examines the question of analysts’ intra-industry stock-picking ability, and Section 4 analyzes analysts’ collective pronouncements as a mechanism for predicting industry/sector rotation. Section 5 discusses the implications of our findings and concludes.
1. Analyst Recommendation Activity: The Institutional Setting and Prior Literature
Recommendations are only one of the informational products that analysts provide for investors. Others include building pro forma valuation models, forecasting future earnings, cash flows and price targets. The analyst’s specific information snooping and dissemination tasks can be categorized as 1) gathering new information on the industry or individual stock from customers, suppliers, and firm managers; 2) analyzing these data and forming earnings estimates and recommendations; and 3) presenting recommendations and financial models to buy-side customers in presentations and written reports.
The analyst’s dissemination of information to investment customers is usually disseminated through a morning research conference call. Such conference calls are held at most brokerage firms about two hours before the stock market opens for trading in New York. Analysts and portfolio strategists speak about, interpret, and possibly change opinions on firms or sectors they follow. Both institutional and retail salespeople at the brokerage firm listen to this call, take notes, and ask questions. Alternatively, urgent communications may be made following a surprising quarterly earnings announcement or some type of other corporate announcement while the market is open for trading. In both cases, analysts notify the salespeople at the brokerage firm, who in turn call customers who they believe might care (and potentially transact) on the basis of the change. Once the sales force is notified, the analyst may directly call, fax, or send e-mail to the firm’s largest customers if the analyst knows of their interest in the particular stock or sector. The information is sometimes retransmitted via the Dow Jones News Service, Reuters, CNN, or other news sources, especially when the price response in the market is significant.
The type of announcement analyzed extensively here is a change of opinion rating level by an analyst on a stock. New “buy” recommendations are usually scrutinized by a research oversight committee or the legal department of the brokerage firm before release. Thus, a new added-to-buy recommendation may have been in the planning stage for several days or weeks before an announcement. Sudden changes in recommendations (especially, removals of “buy” recommendations) may occur in response to new and significant information by or about the company.
Several earlier academic studies form our priors as to how investors and the collective market respond to recommendation changes. Womack (1996) identified significant price drift in short time periods following recommendation upgrades and downgrades by the largest US brokerage firms in the 1989-1991 time period. For upgrades to “buy” or “strong buy”, he found using an event-study methodology that the market reacted strongly to the news announcement of the upgrade and continued to drift in the predicted upward direction for another one to two months. For downgrades from a buy category and for outright sell recommendations, he documented the negative returns with somewhat higher intensity: larger immediate reactions and larger amounts of drift over somewhat longer periods, three to six months. Two issues related to market efficiency were addressed that we examine more carefully here. First, adjustments for standard Fama-French and momentum risk factors were used and did not significantly affect the magnitudes of the short-term (1 to 3 month) returns. Second, in Figure 1, Womack demonstrates the lesser reactions of larger companies relative to smaller ones. Price reactions and drift were almost twice as large in the 5 smallest market capitalization deciles as in the top two.
Barber, Lehavy, McNichols, and Trueman (2001), take an importantly different perspective. Using a different dataset (Zacks) of recommendation information, they address the issue of whether forming portfolios through calendar time can capture the returns suggested by event study results in Womack (1996) and Stickel (1995). Two features distinguish the Barber et.al. results. First, they form portfolios by choosing stocks below and above two time-invariant cutoff levels (to identify attractive and unattractive stocks). To form long portfolios, they choose all stocks with analyst consensus level ratings of 1.5 or less. For the short portfolio stocks, they include stocks with ratings higher than 3.0. Second, they rebalance these portfolios daily, as stocks move into and out of these cutoff ranges. Their results suggest significant long-short returns before transactions cost (slightly less than 1% per month) for the 1986 to 1996 period, but implied transactions costs eat up these “profits”.
Jegadeesh and Titman (1993) and others document price momentum over periods of 3 months to one year and suggest that the evidence is consistent with delayed reactions to firm specific information. Our paper offers causal confirmation, showing that one type of or proxy for firm-specific information is analysts’ valuation changes.
Hong, Lim, and Stein (2000) link the phenomenon of price momentum with analyst following. This gives support to the hypothesis of Hong and Stein (1999) that momentum is a symptom of investors’ collective underreaction to individual pieces of private information. They use analysts as a proxy for intensity of information dissemination, and, after controlling for size, find that price momentum is greater where analyst coverage is lower.
2. Data and Sample Selection
The data used in this study were originally developed by Validea.com, a now defunct Internet startup company that had planned to develop a product for institutional and individual investors to rank analysts and their recommendations. Validea’s choice of recommendations data (after searching for the most timely and accurate source with historical data back to at least 1995) was IBES, which constructs two recommendations databases used here. The first is a monthly Summary History-Recommendation file that compiles a monthly snapshot of each company followed by sell-side analysts whose brokerage firms provides data to IBES. This database tracks at mid-calendar month (similar to the Summary History-EPS file) the number of analysts following the stock, the average consensus rating level on a 1 to 5 scale (where 1 is a “strong buy” and 5 is a “sell”) and its standard deviation for the stock, and the number of analysts upgrading and downgrading their opinion level in the month. The Detail History-Recommendation file provides a database entry for each recommendation change made by each analyst. Important variables include the date of the change, the analyst and the brokerage firm’s name, to what level the change was made. Table 1 shows the brokerage firm level characteristics of the 150,873 recommendation changes we analyze. There were 7,960 companies followed during at least one month of the January 1996 to June 2001 time frame that we examine.