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An Effective Correlation-Based Pair Trading Strategy Using Genetic Algorithms

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

In the stock market, trading strategies are commonly utilized to find trading signals to make a more profitable trading, and can be formed by various technical indicators. Based on the correlation of stocks, the pair trading strategy is then developed for trading. The process of a pair trading can be divided into two parts that are finding potential stock pairs and then deriving trading signals, including buying and selling signals. In the process, many parameters should be considered and it is not easy to obtain their appropriate setting. In this paper, we thus propose an approach for finding those parameters by the genetic algorithms. It first encodes the parameters of the correlation coefficient and Bollinger bands into a chromosome. The fitness value of every possible solution is evaluated by the return and number of trading. Experiments are also conducted on real datasets to show that the proposed method is better than the previous one in terms of return.

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References

  1. Krauss, C.: Statistical arbitrage pairs trading strategies: review and outlook. J. Econ. Surv. 31, 513–545 (2017)

    Article  Google Scholar 

  2. Ronnachai, J., Naragain, P.: Prediction of the profitability of pairs trading strategy using machine learning. In: IEEE International Conference on Industrial Engineering and Applications, pp. 1025–1030 (2020)

    Google Scholar 

  3. Gatev, E., Goetzmann, W.N., Rouwenhorst, K.G.: Pairs trading: performance of a relative-value arbitrage rule. Rev. Financ. Stud. 19(3), 797–827 (2006)

    Article  Google Scholar 

  4. Do, B., Faff, R.: Does simple pairs trading still work? Financ. Anal. J. 66(4), 83–95 (2010)

    Article  Google Scholar 

  5. Vidyamurthy, G.: Pairs trading: quantitative methods and analysis. John Wiley & Sons, Hoboken, NJ (2004)

    Google Scholar 

  6. Elliott, R.J., Van Der Hoek, J., Malcolm, W.P.: Pairs trading. Quantit. Financ. 5(3), 271–276 (2005)

    Article  MathSciNet  Google Scholar 

  7. Fil, M., Kristoufek, L.: Pairs trading in cryptocurrency markets. IEEE Access 8, 172644–172651 (2020)

    Article  Google Scholar 

  8. Shen, L., Shen, K., Yi, C., Chen, Y.: An evaluation of pairs trading in commodity futures markets. In: IEEE International Conference on Big Data, pp. 5457–5462 (2020)

    Google Scholar 

  9. Huang, C.C.: Correlation-based Pair Trading Optimization Techniques. Department of Computer Science and Information Engineering, Tamkang University (2020)

    Google Scholar 

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Acknowledgment

This research was supported by the Ministry of Science and Technology of the Republic of China under grants MOST 109-2221-E-390-015-MY3.

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Authors and Affiliations

  1. Department of Information and Finance Management, National Taipei University of Technology, Taipei, 106, Taiwan

    Chun-Hao Chen

  2. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, 804, Taiwan

    Wei-Hsun Lai & Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan

    Tzung-Pei Hong

Authors
  1. Chun-Hao Chen
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  2. Wei-Hsun Lai
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  3. Tzung-Pei Hong
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Corresponding author

Correspondence to Tzung-Pei Hong .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Chen, CH., Lai, WH., Hong, TP. (2021). An Effective Correlation-Based Pair Trading Strategy Using Genetic Algorithms. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_19

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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