Working Paper: CEPR ID: DP16748
Authors: Eric Ghysels; Yan Qian; Steve Raymond
Abstract: 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.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| uncertainty measure (D81) | wealth of the investor (G11) |
| uncertainty measure (D81) | portfolio allocation decisions (G11) |
| risk (D81) | portfolio allocation decisions (G11) |
| uncertainty measure (D81) | better portfolio outcomes (G11) |