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Research on a stock-matching trading strategy based on bi-objective optimization

Author

Listed:
  • Haican Diao

    (Renmin University of China)

  • Guoshan Liu

    (Renmin University of China)

  • Zhuangming Zhu

    (Renmin University of China)

Abstract

In recent years, with strict domestic financial supervision and other policy-oriented factors, some products are becoming increasingly restricted, including nonstandard products, bank-guaranteed wealth management products, and other products that can provide investors with a more stable income. Pairs trading, a type of stable strategy that has proved efficient in many financial markets worldwide, has become the focus of investors. Based on the traditional Gatev–Goetzmann–Rouwenhorst (GGR, Gatev et al., 2006) strategy, this paper proposes a stock-matching strategy based on bi-objective quadratic programming with quadratic constraints (BQQ) model. Under the condition of ensuring a long-term equilibrium between paired-stock prices, the volatility of stock spreads is increased as much as possible, improving the profitability of the strategy. To verify the effectiveness of the strategy, we use the natural logs of the daily stock market indices in Shanghai. The GGR model and the BQQ model proposed in this paper are back-tested and compared. The results show that the BQQ model can achieve a higher rate of returns.

Suggested Citation

  • Haican Diao & Guoshan Liu & Zhuangming Zhu, 2020. "Research on a stock-matching trading strategy based on bi-objective optimization," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:spr:fobric:v:14:y:2020:i:1:d:10.1186_s11782-020-00076-4
    DOI: 10.1186/s11782-020-00076-4
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    References listed on IDEAS

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    1. Gareth W. Peters & Balakrishnan B. Kannan & Ben Lasscock & Chris Mellen & Simon Godsill, 2010. "Bayesian Cointegrated Vector Autoregression models incorporating Alpha-stable noise for inter-day price movements via Approximate Bayesian Computation," Papers 1008.0149, arXiv.org.
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