# Application of Deep Q-Network in Portfolio Management

arXiv: https://arxiv.org/abs/2003.06365 · HTML: https://mlsanity.com/paper/2003.06365/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization](https://mlsanity.com/paper/2211.07675/) (2022)
- [A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2501.06832/) (2025)
- [MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling](https://mlsanity.com/paper/2503.04143/) (2025)
- [TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution](https://mlsanity.com/paper/2104.00620/) (2021)
- [A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/2203.14159/) (2022)
- [Comparing Multiclass Classification Algorithms for Financial Distress Prediction](https://mlsanity.com/paper/2307.03908/) (2023)
- [Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price Levels-Based Trading Strategy](https://mlsanity.com/paper/2501.08528/) (2025)
- [MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management](https://mlsanity.com/paper/2502.07280/) (2025)
- [Dynamic resource matching in manufacturing using deep reinforcement learning](https://mlsanity.com/paper/2603.27066/) (2026)
- [Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman](https://mlsanity.com/paper/2606.09104/) (2026)
- [Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization](https://mlsanity.com/paper/2607.06610/) (2026)

## Abstract (arXiv)

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.