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Discussion Paper Details

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Title: Do Analysts Learn from Each Other? Evidence from Analysts' Location Diversity

Author(s): Ling Cen, Yuk Ying Chang and Sudipto Dasgupta

Publication Date: July 2020

Keyword(s): Analyst Forecasts, Herding, Information Diversity and learning

Programme Area(s): Financial Economics

Abstract: 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.

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Bibliographic Reference

Cen, L, Chang, Y and Dasgupta, S. 2020. 'Do Analysts Learn from Each Other? Evidence from Analysts' Location Diversity'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=15057