Analysts’ Use of Public Information and the Profitability of their Recommendation Revisions

I examine the relationship between analysts’ use of public information and the performance of their stock recommendations. I construct a measure of analysts’ reliance on public information (RPI) based on the sensitivity of their recommendation revisions to quantitative variables that are known to predict returns. I find a strong negative relationship between analysts’ past RPI and their future performance. A long/short trading strategy based on recommendation revisions of low RPI analysts yields an abnormal return of over 20% per year. My results suggest that high RPI analysts underperform not only because they have little ability to uncover private information, but also because they process the publicly available information poorly. *Yale University, Department of Economics and International Center for Finance (email: [email protected]). I thank Nick Barberis for his helpful suggestions, comments, and support. I would also like to thank James Choi, Martijn Cremers, Will Goetzmann, Jonathan Ingersoll, Owen Lamont, Antti Petajisto, Geert Rouwenhorst, Robert Shiller, Paul Tetlock, Frank Zhang, and seminar participants at Yale University and the Chicago Quantitative Alliance 15 Annual Conference. I gratefully acknowledge financial support from the Whitebox Advisors Doctoral Fellowship and the Chicago Quantitative Alliance academic competition. All errors are my own.