Portfolio management system in equity market neutral using reinforcement learning
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TLDR
The developed Portfolio Management System using reinforcement learning with two neural networks is profitable, effective, and offers lower investment risk among almost all datasets, and the novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.Abstract:
Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed PMS_CNN model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the PMS_RNN. Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.read more
Citations
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Journal ArticleDOI
Self-Management Portfolio System with Adaptive Association Mining: A Practical Application on Taiwan Stock Market
TL;DR: A self-management portfolio system with adaptive association mining for practical applications that allocates funds into independent units for risk management, and utilizes association mining and adaptive closing mechanism for resource allocation and sustainability, and adopts a self- management module for monitoring positions.
Proceedings ArticleDOI
Deep Reinforcement Learning for Trading—A Critical Survey
TL;DR: Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks as discussed by the authors and has been applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches.
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Multi-type data fusion framework based on deep reinforcement learning for algorithmic trading
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