High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning algorithm based on Deep Q-learning for high-dimensional stock portfolio trading, capable of handling large, complex datasets with data gaps and varying asset histories, and demonstrates superior out-of-sample performance.

Contribution

The paper presents a novel DRL approach for high-dimensional portfolio trading that works across diverse datasets with minimal hyperparameter tuning.

Findings

Outperforms passive and active benchmarks significantly

Effective in portfolios ranging from 10 to 500 stocks

Robust across different selection criteria and transaction costs

Abstract

This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning. The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any size which may include data gaps and non-unique history lengths in the assets. We sequentially set up environments by sampling one asset for each environment while rewarding investments with the resulting asset's return and cash reservation with the average return of the set of assets. This enforces the agent to strategically assign capital to assets that it predicts to perform above-average. We apply our methodology in an out-of-sample analysis to 48 US stock portfolio setups, varying in the number of stocks from ten up to 500 stocks, in the selection criteria and in the level of transaction costs. The algorithm on average outperforms all considered passive and active benchmark investment strategies by a large margin using only one hyperparameter setup for all portfolios.