| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29723 |
| 来源ID | Working Paper 29723 |
| Machine-Learning the Skill of Mutual Fund Managers | |
| Ron Kaniel; Zihan Lin; Markus Pelger; Stijn Van Nieuwerburgh | |
| 发表日期 | 2022-02-07 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum. |
| 主题 | Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Institutions |
| URL | https://www.nber.org/papers/w29723 |
| 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/587397 |
| 推荐引用方式 GB/T 7714 | Ron Kaniel,Zihan Lin,Markus Pelger,et al. Machine-Learning the Skill of Mutual Fund Managers. 2022. |
| 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 | ||
| w29723.pdf（1688KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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