| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17194 |
| DP17194 The Virtue of Complexity in Return Prediction | |
| Semyon Malamud; Bryan Kelly; Kangying Zhou | |
| 发表日期 | 2022-04-07 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We theoretically characterize the behavior of return prediction models in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. Contrary to conventional wisdom in finance, return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization, even when minimal regularization is used. Empirically, we document this "virtue of complexity" in US equity market prediction. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions. |
| 主题 | Financial Economics |
| 关键词 | Portfolio choice Machine learning Random matrix theory Benign overfit Overparameterization |
| URL | https://cepr.org/publications/dp17194 |
| 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/546190 |
| 推荐引用方式 GB/T 7714 | Semyon Malamud,Bryan Kelly,Kangying Zhou. DP17194 The Virtue of Complexity in Return Prediction. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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