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- Title
Structural break-aware pairs trading strategy using deep reinforcement learning.
- Authors
Lu, Jing-You; Lai, Hsu-Chao; Shih, Wen-Yueh; Chen, Yi-Feng; Huang, Shen-Hang; Chang, Hao-Han; Wang, Jun-Zhe; Huang, Jiun-Long; Dai, Tian-Shyr
- 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.
- Subjects
TAIWAN; REINFORCEMENT learning; MACHINE learning; TRANSACTION costs; STOCK exchanges; DEEP learning; COINTEGRATION
- Publication
Journal of Supercomputing, 2022, Vol 78, Issue 3, p3843
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-021-04013-x