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

55 Pages Posted: 28 Jul 2020 Last revised: 29 Mar 2022

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: July 2020

Abstract

We show that when the locations of analysts covering a firm are geographically more diverse, the individual forecasts of the analysts for that firm are less correlated. More geographical diversity of co-analyst locations leads to more accurate individual analyst forecasts. This suggests that analysts assign weights to co-analysts' forecasts when making their own forecasts, and the individual forecasts become more accurate due to a diversification effect. Moreover, in line with efficient weighted average forecasting, our results indicate that the weights assigned to peer forecasts vary with measures of the precision of the analyst's signal and those of the peers. Overall, our evidence suggests observational learning in the analyst setting. Our empirical design avoids typical pitfalls of outcome-on-outcome peer effects (Angrist, 2014) by showing that an analyst's expected absolute forecast error (proportional to standard deviation) is affected by the covariance of co-analyst's forecast errors (as captured by their locational diversity).

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, ABFER, CEPR, and ECGI ( email )

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

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