Are robo-analysts, who apply technology to mass-produce recommendations with limited human intervention, better stock pickers than traditional human analysts? In their January 2020 preliminary (and incomplete) paper entitled “Man Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations”, Braiden Coleman, Kenneth Merkley and Joseph Pacelli compare distribution, revision frequency and performance for stock recommendations from robo-analysts versus traditional analysts. In measuring performance, they consider 3-factor (adjusting for market, size and book-to-market factors) and 5-factor (additionally adjusting for profitability and investment factors) alphas of daily rebalanced portfolios of buy or sell recommendations, with a lag of one day between recommendations and trades. Using 134,781 reports issued by seven prominent Robo-Analyst firms and by traditional analysts for 1,002 stocks covered by at least three analysts for at least five years during 2003 through 2018, they find that:
- Robo-analysts collectively produce a more balanced distribution of buy, hold, and sell recommendations than do traditional analysts. For example, 32% (24%) of robo-analyst recommendations tallied at year ends are buys (sells), compared to 47% (6%) for traditional analysts.
- Robo-analysts revise recommendations more frequently than traditional analysts, relying less on earnings announcements and more on data in annual reports. Specifically, robo-analysts issue nearly one more revision per covered firm per year than traditional analysts.
- Upward (downward) robo-analyst recommendation revisions elicit less positive (negative) short-term market reactions than those of traditional analysts, suggesting less market attention to the former.
- A portfolio of robo-analyst buy recommendations generates 6.9% (6.4%) annualized gross 3-factor (5-factor) alphas, compared to 1.2% (1.7%) for that of traditional analysts. However, for sell recommendations, 3-factor and 5-factor alphas are near zero for both robo-analysts and traditional analysts.
In summary, evidence suggests that investors can exploit buy recommendations of robo-analysts.
Cautions regarding findings include:
- Returns and alphas are gross, not net. Accounting for trading frictions from daily portfolio rebalancing would reduce all returns. Frictions may differ between robo-analysts and traditional analysts. Costs of obtaining recommendations may be material for many investors.
- As indicated, neither robo-analysts nor tradition analysts reliably predict which stocks will underperform.
- As noted, findings are preliminary and incomplete. There may be material revisions.
See also “Robo Advisor Expected Performance and Acceptance” and “Investors vs. Matched Robo-investors”.
