Deep Parametric Portfolio Policies

75 Pages Posted: 12 Jul 2022 Last revised: 21 Feb 2025

See all articles by Frederik Simon

Frederik Simon

University of Cologne

Sebastian Weibels

University of Cologne

Tom Zimmermann

University of Cologne

Date Written: June 30, 2022

Abstract

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP) generate 43-102 basis points higher monthly certainty equivalent returns compared to linear policies. Looking beyond expected returns, non-linear portfolio policies better capture the complex relationship between investor preferences and firm characteristics but the benefits of using complex models vary with investor preferences. Results hold across different utility functions and remain robust to transaction costs and short-selling restrictions.

Keywords: Portfolio Choice, Machine Learning, Expected Utility

JEL Classification: G11, G12, C58, C45

Suggested Citation

Simon, Frederik and Weibels, Sebastian and Zimmermann, Tom, Deep Parametric Portfolio Policies (June 30, 2022). Available at SSRN: https://ssrn.com/abstract=4150292 or http://dx.doi.org/10.2139/ssrn.4150292

Frederik Simon

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Sebastian Weibels (Contact Author)

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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