Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

@article{Ye2020ReinforcementLearningBP,
  title={Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States},
  author={Yunan Ye and Hengzhi Pei and Boxin Wang and Pin-Yu Chen and Y. Zhu and Jun Xiao and Bo Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.05780}
}
Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in… Expand
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References

SHOWING 1-10 OF 42 REFERENCES
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Adversarial Deep Reinforcement Learning in Portfolio Management
Deep Hedging
On-Line Portfolio Selection with Moving Average Reversion
Deep Learning for Finance: Deep Portfolios
Weighted Moving Average Passive Aggressive Algorithm for Online Portfolio Selection
  • L. Gao, Weiguo Zhang
  • Computer Science
  • 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics
  • 2013
Universal Portfolios
...
1
2
3
4
5
...