| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16748 |
| DP16748 Ambiguity with Machine Learning: An Application to Portfolio Choice | |
| Eric Ghysels; Yan Qian; Steve Raymond | |
| 发表日期 | 2021-11-22 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | To characterize ambiguity we use machine learning to impose guidance and discipline on the formulation of expectations in a data-rich environment. In addition, we use the bootstrap to generate plausible synthetic samples of data not seen in historical real data to create statistics of interest pertaining to uncertainty. While our approach is generic we focus on robust portfolio allocation problems as an application and study the impact of risk versus uncertainty in a dynamic mean-variance setting. We show that a mean-variance optimizing investor achieves economically meaningful wealth gains (33%) across our sample from 1996-2019 by internalizing our uncertainty measure during portfolio formation. |
| 主题 | Financial Economics |
| URL | https://cepr.org/publications/dp16748 |
| 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/545682 |
| 推荐引用方式 GB/T 7714 | Eric Ghysels,Yan Qian,Steve Raymond. DP16748 Ambiguity with Machine Learning: An Application to Portfolio Choice. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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