51 Pages Posted: 18 Mar 2011 Last revised: 13 Aug 2012
Date Written: August 13, 2012
Analysts who provide more accurate earnings forecasts also issue more profitable recommendations. We demonstrate how investors can profit from this contemporaneous link by differentiating between “able” and “lucky” analysts. In line with previous studies, we find that past track records alone are not sufficient to identify profitable recommendations. Only skilled analysts working in a superior environment provide consistently profitable recommendations. The overall profitability of their recommendations is not driven by a post-announcement drift effect. We find that an implementable, i.e., look-ahead bias free, trading strategy based on the projected – rather than past – earnings accuracy yields substantial excess returns.
The main difference of our approach is that we combine analysts’ past earnings accuracy with a broad range of analyst characteristics to assess their ability. This approach enables us to identify ex ante analysts who provide superior recommendations. We show that it is essential to take into account the ability of analysts in addition to their past performance, i.e., to look beyond their track record.
Our results suggest that identifying the more skilled processors of information is crucial, while following analysts based on only their past performance alone is not profitable. Moreover, our approach provides a promising avenue for research that uses analysts’ outputs to capture the expectations of market participants or evaluates incentives and compensation of financial intermediaries.
Keywords: analysts, portfolio management, profitability of recommendations
JEL Classification: G14, G17, G24
Suggested Citation: Suggested Citation
Hess, Dieter and Kreutzmann, Daniel and Pucker, Oliver, The Good, The Bad, and The Lucky: Projected Earnings Accuracy and Profitability of Stock Recommendations (August 13, 2012). AFA 2012 Chicago Meetings Paper. Available at SSRN: https://ssrn.com/abstract=1786608 or http://dx.doi.org/10.2139/ssrn.1786608
By Craig Brown