DP15057 Do Analysts Learn from Each Other? Evidence from Analysts' Location Diversity

Author(s):	Ling Cen, Yuk Ying Chang, Sudipto Dasgupta
Publication Date:	July 2020
Keyword(s):	Analyst Forecasts, Herding, Information Diversity, learning
JEL(s):	D83, G24
Programme Areas:	Financial Economics
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=15057

Consistent with the idea that some of the noise in analysts' earnings forecasts originates in their geographic locations, we find that when analysts' locations are geographically more dispersed, the consensus forecast is more accurate, suggesting a diversification effect. Importantly, analysts' individual forecasts are also more accurate, implying that analysts incorporate idiosyncratic (private) information in their peer's forecasts when generating their own forecasts. Moreover, in line with efficient weighted average forecasting behavior, the weights assigned to peer forecasts vary with measures of the precision of the analyst's signal and those of the peers. Overall, we find strong evidence of analyst learning.