# High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2112.04755 · HTML: https://mlsanity.com/paper/2112.04755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [Horizontal and Vertical Ensemble with Deep Representation for Classification](https://mlsanity.com/paper/1306.2759/) (2013)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.02787/) (2018)
- [An intelligent financial portfolio trading strategy using deep Q-learning](https://mlsanity.com/paper/1907.03665/) (2019)
- [Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.14194/) (2020)
- [Reinforcement Learning with Expert Trajectory For Quantitative Trading](https://mlsanity.com/paper/2105.03844/) (2021)

## Built on by (most cited first, 2 of 2)

- [Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network](https://mlsanity.com/paper/2206.14267/) (2022)
- [Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management](https://mlsanity.com/paper/2306.10950/) (2023)

## Abstract (arXiv)

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.