| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24435 |
| 来源ID | Working Paper 24435 |
| Selecting Directors Using Machine Learning | |
| Isil Erel; Léa H. Stern; Chenhao Tan; Michael S. Weisbach | |
| 发表日期 | 2018-03-26 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Can algorithms assist firms in their decisions on nominating corporate directors? We construct algorithms to make out-of-sample predictions of director performance. Tests of the quality of these predictions show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably poor performing directors are more likely to be male, have more past and current directorships, fewer qualifications, and larger networks than the directors the algorithm would recommend in their place. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help real-world firms improve their governance. |
| 主题 | Financial Economics ; Corporate Finance ; Other ; Accounting, Marketing, and Personnel |
| URL | https://www.nber.org/papers/w24435 |
| 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/582108 |
| 推荐引用方式 GB/T 7714 | Isil Erel,Léa H. Stern,Chenhao Tan,et al. Selecting Directors Using Machine Learning. 2018. |
| 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 | ||
| w24435.pdf（532KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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