Human Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations - Coleman, Braiden; Merkley, Kenneth; Pacelli, Joseph (The Accounting Review, 2022)
ABSTRACT We provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and incorporate information from complex periodic filings. Third, while Robo-Analysts' recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that automation in the sell-side research industry can benefit investors. JEL Classifications: G14; G24.