# Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.01758 · HTML: https://mlsanity.com/paper/2108.01758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Deep Generative Video Compression](https://mlsanity.com/paper/1810.02845/) (2018)
- [Disentangled Recurrent Wasserstein Autoencoder](https://mlsanity.com/paper/2101.07496/) (2021)
- [Stein Variational Inference for Discrete Distributions](https://mlsanity.com/paper/2003.00605/) (2020)
- [Stein Variational Gradient Descent Without Gradient](https://mlsanity.com/paper/1806.02775/) (2018)
- [Stein Variational Adaptive Importance Sampling](https://mlsanity.com/paper/1704.05201/) (2017)
- [Bootstrap Model Aggregation for Distributed Statistical Learning](https://mlsanity.com/paper/1607.01036/) (2016)

## Abstract (arXiv)

Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward. Deep reinforcement learning provides good opportunity to model the complexity of portfolio choice in high-dimensional and data-driven environment by leveraging the powerful representation of deep neural networks. In this paper, we build a portfolio management system using direct deep reinforcement learning to make optimal portfolio choice periodically among S&P500 underlying stocks by learning a good factor representation (as input). The result shows that an effective learning of market conditions and optimal portfolio allocations can significantly outperform the average market.