Working Paper: NBER ID: w31502
Authors: Bryan T. Kelly; Dacheng Xiu
Abstract: We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
Keywords: No keywords provided
JEL Codes: C33; C4; C45; C55; C58; G1; G10; G11; G12; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| asset price (pit) (G19) | investor valuations of future payoffs (xit1) (G19) |
| investor valuations of future payoffs (xit1) (G19) | future realized marginal rates of substitution (mt1) (F16) |
| investor preferences (G11) | investor valuations of future payoffs (xit1) (G19) |
| machine learning methods (C45) | improved understanding of asset prices (G19) |