Gender, Learning, and Overconfidence : Why Females Create More Accurate Earnings Estimates

We analyze the underlying source of gender differences in earnings estimates on a crowd-sourcing platform with low barriers to entry. This platform allows us to examine gender differences within earnings estimates among a sample of non-professional analysts in an effort to better understand the development of analyst ability. Estimates made by females are more accurate than those made by males. We eliminate explanations of more talented females joining the platform, an innate ability of females to process information, females utilizing more up-to-date information, superior stock selection among females, and survivor ship bias. Rather, our evidence is consistent with females learning faster and males exhibiting greater overconfidence. Our findings provide new insight into the mechanisms behind the increase in accuracy documented among professional female analysts. Finally, we observe a positive market response when females provide more optimistic estimates