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

Advances in financial technology (FinTech) have revolutionized various product offerings in the financial services industry. One area of particular interest for this technology is the production of investment recommendations. Our study provides 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 dimensions. First, Robo-Analysts collectively produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts, consistent with them being less subject to behavioral biases and conflicts of interest. Second, consistent with automation facilitating a greater scale of research production, Robo-Analysts revise their recommendations more frequently than human analysts and also adopt different production processes. Their recommendation revisions rely less on earnings announcements, and more on the large volumes of data released in firms’ annual reports. Third, Robo-Analysts’ recommendation revisions exhibit weaker short-window return reactions, suggesting that investors do not trade on their signals. Importantly, portfolios formed based on the buy recommendations of Robo-Analysts appear to outperform those of human analysts, suggesting that their buy calls are more profitable. Overall, our results suggest that Robo-Analysts are a valuable, alternative information intermediary to traditional sell-side analysts for investment advice.

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