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Structural Break-aware Pairs Trading Strategy Using Deep Reinforcement Learning by Jing-You Lu; Hsu-Chao Lai; Wen-Yueh Shih; Yi-Feng Chen; Shen-Hang Huang; Hao-Han Chang; Jun-Zhe Wang; Jiun-Long Huang; Tian-Shyr Dai is a Computer Science article available to read on EtoBox.
What is Structural Break-aware Pairs Trading Strategy Using Deep Reinforcement Learning about?
Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy (SAPT), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency-and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.
Who reads Structural Break-aware Pairs Trading Strategy Using Deep Reinforcement Learning?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jing-You Lu; Hsu-Chao Lai; Wen-Yueh Shih; Yi-Feng Chen; Shen-Hang Huang; Hao-Han Chang; Jun-Zhe Wang; Jiun-Long Huang; Tian-Shyr Dai
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)