High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning
This addresses portfolio optimization for financial traders by offering a scalable method that outperforms benchmarks, though it is incremental as it builds on existing deep reinforcement learning techniques.
The paper tackles high-dimensional stock portfolio trading by proposing a Deep Q-learning algorithm that strategically allocates capital to assets predicted to outperform the average, achieving superior performance across 48 US stock portfolios with varying sizes and conditions.
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.