How Analysts Learn from Peers’ Earnings Forecast Errors

Summary

This study examines whether sell-side analysts use peer forecasts on other companies in their own coverage portfolios to inform estimates for a target company. Using quarterly earnings forecasts and actual results, it measures optimism through forecast errors and deviations from consensus, and classifies forecasts as aggressive when they move beyond both the analyst’s prior estimate and consensus in the same direction.

The reported results link peers’ prior forecast errors to analysts’ later optimism: analysts become more conservative after peers overestimate earnings on other covered stocks. Analysts also tend to issue aggressive forecasts when peers previously made aggressive forecasts in the same direction, with stronger imitation reported for negative calls. The study further finds evidence that industry peers’ forecast errors contain information about subsequent consensus errors, while same-state evidence is positive but not statistically significant. The authors report that adjusting for peers’ errors is associated with improved forecast accuracy. The evidence comes from an observational panel covering 1984–2017, so it describes associations and does not establish that peer imitation itself causes better forecasts.

Key ideas

• Analysts’ target-company optimism is negatively related to peers’ prior forecast errors on other stocks in their coverage portfolios.
• Peer forecast errors may provide information that analysts use to revise their own estimates.
• Aggressive forecasts are more likely after peers issue aggressive forecasts in the same direction.
• The reported peer imitation effect is stronger for aggressive negative forecasts.
• Industry forecast errors predict later consensus errors, while the same-state relationship is not statistically significant.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.