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Structural break-aware pairs trading strategy using deep reinforcement learning.

Jing-You Lu1, Hsu-Chao Lai2, Wen-Yueh Shih2

  • 1Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.

The Journal of Supercomputing
|August 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a structural break-aware pairs trading strategy (SAPT) using machine learning to detect market changes. SAPT significantly enhances profitability and risk management in statistical arbitrage trading.

Keywords:
Continuous wavelet CNNDeep reinforcement learningPairs trading strategyStructural break detection

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Area of Science:

  • Quantitative Finance
  • Machine Learning in Finance
  • Algorithmic Trading

Background:

  • Pairs trading is a statistical arbitrage strategy relying on stable cointegration relationships between stock pairs.
  • Market volatility and structural breaks can disrupt these relationships, leading to significant trading losses.
  • Existing strategies often fail to adequately address dynamic market changes and associated risks.

Purpose of the Study:

  • To develop an optimized pairs trading strategy that accounts for structural breaks and other market risks.
  • To enhance the robustness and profitability of statistical arbitrage in dynamic financial markets.
  • To introduce a machine learning-based framework for adaptive trading strategy optimization.

Main Methods:

  • A two-phase framework, Structural Break-Aware Pairs Trading strategy (SAPT), was designed.
  • Phase one utilizes a hybrid model for frequency- and time-domain feature extraction to detect structural breaks.
  • Phase two employs a novel reinforcement learning model to optimize trading decisions, considering risks like structural breaks and market-closing, and incorporates transaction costs.

Main Results:

  • SAPT demonstrated superior performance compared to state-of-the-art strategies in real Taiwan stock market data.
  • The strategy achieved at least a 456% increase in profit.
  • A 934% improvement in the Sortino ratio was observed, indicating better risk-adjusted returns.

Conclusions:

  • The proposed SAPT framework effectively mitigates losses caused by structural breaks in pairs trading.
  • Machine learning, particularly reinforcement learning, offers a powerful approach for optimizing trading strategies in volatile markets.
  • SAPT provides a robust and highly profitable solution for statistical arbitrage, outperforming existing methods.