Deep Parametric Portfolio Policies
64 Pages Posted: 12 Jul 2022 Last revised: 10 Feb 2023
Date Written: June 30, 2022
We directly optimize portfolio weights as a function of firm characteristics via deep neural networks by generalizing the parametric portfolio policy framework. Our results show that network-based portfolio policies result in an increase of investor utility of between 30 and 100 percent over a comparable linear portfolio policy, depending on whether portfolio restrictions on individual stock weights, short-selling or transaction costs are imposed, and depending on an investor's utility function. We provide extensive model interpretation and show that network-based policies better capture the non-linear relationship between investor utility and firm characteristics. Improvements can be traced to both variable interactions and non-linearity in functional form. Both the linear and the network-based approach agree on the same dominant predictors, namely past return-based firm characteristics.
Keywords: Portfolio Choice, Machine Learning, Expected Utility
JEL Classification: G11, G12, C58, C45
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