1. Structural break-aware pairs trading strategy using deep reinforcement learning.
- Author
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Lu, Jing-You, Lai, Hsu-Chao, Shih, Wen-Yueh, Chen, Yi-Feng, Huang, Shen-Hang, Chang, Hao-Han, Wang, Jun-Zhe, Huang, Jiun-Long, and Dai, Tian-Shyr
- Subjects
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REINFORCEMENT learning , *MACHINE learning , *TRANSACTION costs , *STOCK exchanges , *DEEP learning , *COINTEGRATION - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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