Human Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations

• 成果类型: Article
• 署名作者: Coleman, Braiden; Merkley, Kenneth; Pacelli, Joseph
• 署名单位: University System of Georgia; University of Georgia; Indiana University System; IU Kelley School of Business; Indiana University Bloomington; Harvard University
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2020-0096
• 发表日期: 2022
• 页码: 221-244
• 关键词: CONFERENCE CALLS; information-content; OF-INTEREST; earnings; management; forecasts; performance; TECHNOLOGY; access; IMPACT

摘要

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 humanresearch 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 glamourstocks 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.