2023/08/21 by Kristoffer Andersson, Cornelis W. Oosterlee, Andersson, Kristoffer +1 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #91G10 #91G60 #Computational Finance (q-fin.CP) #FOS: Economics and business #Financial Markets and Investment Strategies #Portfolio Management (q-fin.PM) #Reservoir Engineering and Simulation Methods #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2308.10556
openalex publication_date 2023/08/21 · openalex created_date 2023/08/23 · openalex updated_date 2026/07/28
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.