 Do Analysts Learn from Each Other? Evidence from Analysts’ Location DiversityLing Cen, Yuk Ying Chang and Sudipto Dasgupta No 15057, CEPR Discussion Papers from Centre for Economic Policy Research 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: Information diversity; Learning; Herding; Analyst forecasts (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:15057 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK. |
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