Learning from Local Analysts

Bartram, Söhnke, Vidhi Chhaochharia, Alok Kumar and Hongwei Mo

No 19670, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We examine whether non-local analysts are able to improve the accuracy of their earnings forecasts by observing the forecasts of better-informed local analysts. We find that analysts issue more accurate forecasts for non-local firms in their coverage portfolios by observing the forecasts of local analysts. The improvement in accuracy is larger for non-local analysts at small brokerages and those with more complex portfolios. Further, analysts with superior local information learn more effectively from the locals in their non-local positions. Local analyst forecasts are particularly useful when (i) a firm is far away from the non-local analyst, (ii) the firm’s earnings are harder to predict, (iii) local analysts are more accurate, and (iv) nonlocal analyst forecasts are less accurate. At the aggregate level, learning from the locals improves consensus forecast accuracy and increases favorable career outcomes for non-local analysts. Collectively, these findings suggest that geography-based observational learning reduces informational asymmetry among analysts.

Keywords: Forecast; accuracy (search for similar items in EconPapers)
JEL-codes: D83 G14 G24 M41 (search for similar items in EconPapers)
Date: 2024-11
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