Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation

TL;DR

This paper introduces a reinforcement learning framework for asset allocation that leverages Monte Carlo simulation to enhance training data diversity and consider market statistical structures, improving performance over benchmarks.

Contribution

The paper proposes a novel reinforcement learning asset allocation method that incorporates Monte Carlo simulations considering market correlations to prevent overfitting and improve decision-making.

Findings

Outperforms benchmark methods in multiple test intervals.

Uses Monte Carlo simulation to increase data complexity and robustness.

Considers market correlation structures in training data.

Abstract

Asset allocation using reinforcement learning has advantages such as flexibility in goal setting and utilization of various information. However, existing asset allocation methods do not consider the following viewpoints in solving the asset allocation problem. First, State design without considering portfolio management and financial market characteristics. Second, Model Overfitting. Third, Model training design without considering the statistical structure of financial time series data. To solve the problem of the existing asset allocation method using reinforcement learning, we propose a new reinforcement learning asset allocation method. First, the state of the portfolio managed by the model is considered as the state of the reinforcement learning agent. Second, Monte Carlo simulation data are used to increase training data complexity to prevent model overfitting. These data can have different patterns, which can increase the complexity of the data. Third, Monte Carlo simulation data are created considering various statistical structures of financial markets. We define the statistical structure of the financial market as the correlation matrix of the assets constituting the financial market. We show experimentally that our method outperforms the benchmark at several test intervals.