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

79 Pages Posted: 28 Jul 2020 Last revised: 16 Aug 2020

See all articles by Ling Cen

Ling Cen

The Chinese University of Hong Kong

Yuk Ying (Candie) Chang

Massey University

Sudipto Dasgupta

Chinese University of Hong Kong and CEPR

Date Written: July 2020

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.

Keywords: Analyst Forecasts, Herding, Information Diversity, learning

JEL Classification: D83, G24

Suggested Citation

Cen, Ling and Chang, Yuk Ying and Dasgupta, Sudipto, Do Analysts Learn from Each Other? Evidence from Analysts' Location Diversity (July 2020). CEPR Discussion Paper No. DP15057, Available at SSRN: https://ssrn.com/abstract=3661400

Ling Cen (Contact Author)

The Chinese University of Hong Kong ( email )

CYT Building
Sha Tin
Hong Kong, Hong Kong
Hong Kong

HOME PAGE: http:///sites.google.com/site/cenling/

Yuk Ying Chang

Massey University ( email )

Palmerston North
New Zealand

Sudipto Dasgupta

Chinese University of Hong Kong and CEPR ( email )

CUHK, Cheng Yu Tung Building, Room 1224
Shatin, NT
Hong Kong
Hong Kong

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