Deep reinforcement learning for automated stock trading: an ensemble strategy

By Hongyang Yang, Xiao-Yang Liu, Shanli Zhong, A. Walid. International Conference on AI in Finance, 2020

317 citations recorded by the source corpus.

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b8e1ab5f279018a2d6f677d55aa306ac6703fa47

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. M. A. Ganaie
2. Mohammad Tanveer
3. Minghui Hu
4. L. Di Nunzio
5. Ponnuthurai Nagaratnam Suganthan
6. Rocco Fazzolari
7. Gian Carlo Cardarilli
8. Marco Re

In Management Science and Operations Research

1. M. A. Ganaie
2. Ponnuthurai Nagaratnam Suganthan
3. Mohammad Tanveer
4. Minghui Hu

Citations in the corpus

• Multi-Agent Reinforcement Learning: A Review of Challenges and Applications (2021, background): “RL has found uses in many applications, from finance [1] and robotics [2–4], to natural language processing [5] and telecommunications [6].”
• Ensemble deep learning: A review (2021, methodology): “2020 Deep reinforcement learning for automated stock trading: An ensemble strategy[171] Reinforcement learning Stock trading agency”
• FinGPT: Open-Source Financial Large Language Models (2023, unknown): “Lastly, financial data is often characterized by a low signal-to-noise ratio (SNR) [ Yang et al. , 2020 ] .”