D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options

TL;DR

This paper introduces a neural network-based method for time-inconsistent portfolio optimization that incorporates options and realistic market models, improving asset allocation stability and aligning with investor preferences.

Contribution

It extends neural portfolio optimization by integrating options, realistic market dynamics, and complex objectives, demonstrating improved stability and investor alignment.

Findings

Adding options enhances portfolio performance in incomplete markets.

The method achieves more stable stock allocations with fewer re-allocations.

Incorporating options aligns better with investor preferences.

Abstract

In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization. The proposed algorithm builds upon neural network based trading schemes, in which the asset allocation at each time point is determined by a a neural network. The loss function is given by an empirical version of the objective function of the portfolio optimization problem. Moreover, various trading constraints are naturally fulfilled by choosing appropriate activation functions in the output layers of the neural networks. Besides this, our main contribution is to add options to the portfolio of risky assets and a risk-free bond and using additional neural networks to determine the amount allocated into the options as well as their strike prices. We consider objective functions more in line with the rational preference of an investor than the classical mean-variance, apply realistic trading constraints and model the assets with a correlated jump-diffusion SDE. With an incomplete market and a more involved objective function, we show that it is beneficial to add options to the portfolio. Moreover, it is shown that adding options leads to a more constant stock allocation with less demand for drastic re-allocations.