| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w30217 |
| 来源ID | Working Paper 30217 |
| The Virtue of Complexity in Return Prediction | |
| Bryan T. Kelly; Semyon Malamud; Kangying Zhou | |
| 发表日期 | 2022-07-04 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | The extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Financial Markets |
| URL | https://www.nber.org/papers/w30217 |
| 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/587889 |
| 推荐引用方式 GB/T 7714 | Bryan T. Kelly,Semyon Malamud,Kangying Zhou. The Virtue of Complexity in Return Prediction. 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 | ||
| w30217.pdf（1807KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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