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Local predictive ability of analyst recommendations
Author(s) -
Karadas Serkan,
Papakroni Jorida
Publication year - 2019
Publication title -
review of financial economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.347
H-Index - 41
eISSN - 1873-5924
pISSN - 1058-3300
DOI - 10.1002/rfe.1055
Subject(s) - metropolitan area , state (computer science) , econometrics , business , economics , financial economics , geography , computer science , archaeology , algorithm
This article shows that locally aggregated analyst recommendations at the Metropolitan Statistical Area (MSA‐) or state‐level predict future locally aggregated excess returns. The results hold even after controlling for macroeconomic variables, industry and market returns, as well as investor sentiment. We also show that the local predictive ability of analyst recommendations is stronger for geographically concentrated firms. Additional analysis at the state‐level for the geographically concentrated firms reveals that locally aggregated analyst recommendations predict future local economic fundamentals. Overall, our findings suggest that analyst recommendations contain information at the MSA‐ and state‐level, and that local information content is richer for geographically concentrated firms.