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Analyzing the Analysts: When Do Recommendations Add Value?


  • Narasimhan Jegadeesh,

  • Joonghyuk Kim,

  • Susan D. Krische,

  • Charles M. C. Lee

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    • Jegadeesh is from Emory University; Kim is from Case Western Reserve University; Krische is from the University of Illinois at Urbana-Champaign; and Lee is from Cornell University. This paper subsumes two earlier manuscripts titled “Stock Characteristics and Analyst Stock Recommendations” and “The Information Content of Analyst Stock Recommendations.” We have benefited from the comments of Bill Beaver; Louis Chan; Tom Dyckman; Wayne Ferson; Rick Green (the editor); Paul Irvine; Josef Lakonishok; Jay Ritter; Michael Weisbach; Kent Womack; an anonymous referee; and workshop participants at Case Western Reserve University, Cornell University, Dartmouth College, Emory University, Georgetown University, Rice University, and the Universities of California at Berkeley, Colorado, Illinois at Urbana-Champaign, Pennsylvania, Rochester, and Southern California, as well as the 2002 AFA Annual Meetings, the AIMR Equity Valuation and Research Seminar, and the Prudential Securities Quantitative Research Conference. We would also like to thank Zacks Investment Research for providing the analyst recommendation data and I/B/E/S for providing the analyst earnings forecasts. First Call Corporation also provided us with recommendations data used in an earlier manuscript.


We show that analysts from sell-side firms generally recommend “glamour” (i.e., positive momentum, high growth, high volume, and relatively expensive) stocks. Naïve adherence to these recommendations can be costly, because the level of the consensus recommendation adds value only among stocks with favorable quantitative characteristics (i.e., value stocks and positive momentum stocks). In fact, among stocks with unfavorable quantitative characteristics, higher consensus recommendations are associated with worse subsequent returns. In contrast, we find that the quarterly change in consensus recommendations is a robust return predictor that appears to contain information orthogonal to a large range of other predictive variables.

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