New Algorithms for Evaluating Equity Analysts’ Estimates and Recommendations

Sammanfattning: The purpose of this study is to find improved algorithms to evaluate the work of equity analysts. Initially the study describes how equity analysts work with forecasting earnings per share, and issuing recommendations on whether to invest in stocks. It then goes on to discuss techniques and evaluation algorithms used for evaluating estimates and recommendations found in financial literature. These algorithms are then compared to existing methods in use in the equity research industry. Weaknesses in the existing methods are discussed and new algorithms are proposed. For the evaluation of estimates the main difficulties are concerned with adjusting for the reducing uncertainty over time as new information becomes available, and the problem of identifying which analysts are leading as opposed to herding. For the evaluation of recommendations, the difficulties lie mainly in how to risk-adjust portfolio returns, and how to differentiate between stock-picking ability and portfolio effects. The proposed algorithms and the existing algorithms are applied to a database with over 3500 estimates and 7500 recommendations and an example analyst ranking is constructed. The results indicate that the new algorithms are viable improvements on the existing evaluation algorithms and incorporate new information into the evaluation of equity analysts.