| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27843 |
| 来源ID | Working Paper 27843 |
| Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases | |
| Jules H. van Binsbergen; Xiao Han; Alejandro Lopez-Lira | |
| 发表日期 | 2020-09-21 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | We introduce a real-time measure of conditional biases in firms' earnings forecasts. The measure is defined as the difference between analysts' expectations and a statistically optimal unbiased machine-learning benchmark. Analysts' conditional expectations are, on average, biased upwards, and the bias increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly-used linear earnings models do not work out-of-sample and are inferior to those provided by analysts. |
| 主题 | Microeconomics ; Households and Firms ; Economics of Information ; Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Markets ; Corporate Finance ; Behavioral Finance |
| URL | https://www.nber.org/papers/w27843 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585514 |
| 推荐引用方式 GB/T 7714 | Jules H. van Binsbergen,Xiao Han,Alejandro Lopez-Lira. Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w27843.pdf（1057KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
