Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11227-021-04013-x | The Journal of Supercomputing |
Keywords | DocType | Volume |
Pairs trading strategy, Structural break detection, Deep reinforcement learning, Continuous wavelet CNN | Journal | 78 |
Issue | ISSN | Citations |
3 | 0920-8542 | 0 |
PageRank | References | Authors |
0.34 | 1 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-You Lu | 1 | 0 | 0.34 |
| Hsu-chao Lai | 2 | 2 | 3.74 |
| Wen-yueh Shih | 3 | 4 | 2.12 |
| Yi-Feng Chen | 4 | 0 | 0.34 |
| Shen-Hang Huang | 5 | 0 | 0.34 |
| Hao-Han Chang | 6 | 0 | 0.34 |
| Jun-Zhe Wang | 7 | 0 | 0.34 |
| Jiun-Long Huang | 8 | 0 | 0.68 |
| Tian-Shyr Dai | 9 | 0 | 0.34 |