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

71 Pages Posted: 24 Feb 2020 Last revised: 9 Nov 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, ABFER, CEPR, and ECGI

Date Written: September 1, 2019

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: Information Diversity, Learning, Herding, Analyst Forecasts

JEL Classification: G24, D83

Suggested Citation

Cen, Ling and Chang, Yuk Ying and Dasgupta, Sudipto, Do Analysts Learn from Each Other? Evidence from Analysts’ Location Diversity (September 1, 2019). Available at SSRN: https://ssrn.com/abstract=3526642 or http://dx.doi.org/10.2139/ssrn.3526642

Ling Cen

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 (Contact Author)

Chinese University of Hong Kong, ABFER, CEPR, and ECGI ( email )

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

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