Novel Deep Reinforcement Algorithm With Adaptive Sampling Strategy for Continuous Portfolio Optimization

Novel Deep Reinforcement Algorithm With Adaptive Sampling Strategy for Continuous Portfolio Optimization

Quantitative trading targets favorable returns by determining patterns in historical data through statistical or mathematical approaches. With advances in artificial intelligence, many studies have indicated that deep reinforcement learning (RL) can perform well in quantitative trading by predicting...

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Bibliographic Details
Main Authors: Szu-Hao Huang, Yu-Hsiang Miao, Yi-Ting Hsiao
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Portfolio management
reinforcement learning
adversarial learning
Online Access:https://ieeexplore.ieee.org/document/9437210/

Internet

https://ieeexplore.ieee.org/document/9437210/

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