| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25398 |
| 来源ID | Working Paper 25398 |
| Empirical Asset Pricing via Machine Learning | |
| Shihao Gu; Bryan Kelly; Dacheng Xiu | |
| 发表日期 | 2018-12-24 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing |
| URL | https://www.nber.org/papers/w25398 |
| 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/583072 |
| 推荐引用方式 GB/T 7714 | Shihao Gu,Bryan Kelly,Dacheng Xiu. Empirical Asset Pricing via 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 | ||
| w25398.pdf（2331KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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