Deep parametric portfolio policies

Frederik Simon, Sebastian Weibels and Tom Zimmermann

No 23-01, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C45 C58 G11 G12 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:2301

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