| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15057 |
| DP15057 Do Analysts Learn from Each Other? Evidence from Analysts’ Location Diversity | |
| Ling Cen; Yuk Ying Chang; Sudipto Dasgupta | |
| 发表日期 | 2022-02-12 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | 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). |
| 主题 | Financial Economics |
| 关键词 | Information diversity Learning Herding Analyst forecasts |
| URL | https://cepr.org/publications/dp15057-0 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545977 |
| 推荐引用方式 GB/T 7714 | Ling Cen,Yuk Ying Chang,Sudipto Dasgupta. DP15057 Do Analysts Learn from Each Other? Evidence from Analysts’ Location Diversity. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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