Herding among Investment Newsletters: Theory and Evidence


  • I am grateful to David Hirshleifer and Jaime Zender for comments that helped to substantially improve the paper. I would also like to thank Pete Kyle, Alon Brav, Doug Foster, Dan Graham, Rita Graham, Paul Harrison, Eric Hughson, Ron Lease, Mike Lemmon, Ernst Maug, Susan Monaco, Carl Moody, Barb Ostdiek, Drew Roper, Steve Slezak, René Stulz, TomSmith, Brett Trueman, Vish Viswanathan, anonymous referees, and seminar participants at Duke, Tulane, and the University of Utah for helpful comments. I amgrateful to Mark Hulbert and The Hulbert Financial Digest for providing the newsletter data, to David Hsieh for providing the daily S&P 500 index volatility estimates, and to Yunqi Han and the Federal Reserve Bank of Philadelphia for providing the data on Treasury bill forecasts. I am responsible for all remaining errors. The theoretical part of the paper was a chapter of my doctoral dissertation at Duke University. The empirical work was started while I was at the University of Utah.


A model is developed which implies that if an analyst has high reputation or low ability, or if there is strong public information that is inconsistent with the analyst's private information, she is likely to herd. Herding is also common when informative private signals are positively correlated across analysts. The model is tested using data from analysts who publish investment newsletters. Consistent with the model's implications, the empirical results indicate that a newsletter analyst is likely to herd on Value Line's recommendation if her reputation is high, if her ability is low, or if signal correlation is high.