نوع مقاله : مقاله پژوهشی

نویسندگان

گروه حسابداری و مالی، دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، یزد، ایران.

چکیده

هدف: تشکیل جفت سهام یک گام مهم در معاملات جفتی است که فقط به‌صورت دستی یا از طریق دستورالعمل‌های شمارشی موردبررسی قرار گرفته است. این روش‌ها در حالت چندمتغیره شکست خورده و اهداف متناقض را در ساختار مساله در نظر نمی‌گیرند. در این پژوهش روشی ارایه می‌شود که ترکیب‌های جفتی چندمتغیره را با در نظر گرفتن اهداف چندگانه متناقض در معاملات جفتی سهام ایجاد کند.
روش‌شناسی پژوهش: در این پژوهش نمونه آماری به‌واسطه نیاز به معاملات پربسامد به 30 شرکت برتر پذیرفته‌شده در بورس اوراق بهادار تهران محدود شده است. مساله در قالب یک مدل برنامه‌ریزی عدد صحیح مختلط (MIP) تدوین و به دلیل محدودیت‌های غیرمحدب و فضای حل نمایی از الگوریتم ژنتیک چندهدفه برای به‌دست آوردن ترکیب‌های جفتی چندمتغیره استفاده شده است. برای دستیابی به اهداف چندگانه، از نوع توسعه‌یافته الگوریتم ژنتیک، یعنی الگوریتم ژنتیک مرتب‌سازی نامغلوب آشوبناک (CNSGA-II) استفاده گردید. در این روش برای به‌دست آوردن راه‌حل‌های مناسب و با دقت بالا، از تئوری آشوب در ایجاد جمعیت اولیه الگوریتم ژنتیک استفاده شده است.
یافته‌ها: نتایج پژوهش نشان داد که استفاده از نظریه آشوب می‌تواند میزان همگرایی را در الگوریتم‌های تکاملی افزایش دهد. علاوه‌بر این نتایج بیان‌گر برتری استراتژی معاملات جفتی چندهدفه مبتنی بر رویکرد فاصله نسبت به مدل تک‌هدفه سنتی است.
اصالت/ارزش افزوده علمی: برای بهینه‌سازی معاملات جفتی از الگوریتم ژنتیک مرتب‌سازی نامغلوب استفاده گردید. هم‌چنین جمعیت اولیه افراد در الگوریتم ژنتیک چندهدفه بر اساس تئوری آشوب ایجاد شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Chaotic evolutionary multi-objective optimization for multivariate pair trading in tehran stock exchange: the distance approach

نویسندگان [English]

  • Hossein Nikoo
  • Jamal Barzgari khanagha
  • Hamid Reza Mirzaei

Department of Accounting and Finance, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.

چکیده [English]

Purpose: Pair formation is an important step in pair trading that has only been examined manually or through numerical instructions. These methods fail in the multivariate mode and do not consider conflicting goals in the problem structure. In this research, a method is presented to create multivariate pair combinations by considering contradictory multiple goals in stock pair trading.
Methodology: In this study, the statistical sample is limited to the top 30 companies listed on the Tehran Stock Exchange due to the need for high-frequency transactions. The problem is developed in the form of a Mixed Integer Programming (MIP) model, and due to non-convex constraints and exponential solution space, a multi-objective genetic algorithm is used to obtain multivariate pair combinations. To achieve multiple goals, the developed type of genetic algorithm, namely, The Chaotic Non-dominated Sorting Genetic Algorithm (CNSGA-II), was used. In this method, chaos theory is used to create the initial population of the genetic algorithm in order to obtain appropriate and high-precision solutions.
Findings: The results showed that the use of chaos theory could increase the degree of convergence in evolutionary algorithms. In addition, these results indicate the superiority of the multi-objective pair trading strategy based on the distance approach over the traditional single-objective model.
Originality/Value: In order to optimize pair trading, the Non-dominated Sorting Genetic Algorithm was used. Also, the initial population of individuals was created in a multi-objective genetic algorithm based on chaos theory.

کلیدواژه‌ها [English]

  • Pair trading
  • Non-dominated sorting genetic algorithm
  • Chaos theory
  • Distance approach
[1]     Vidyamurthy, G. (2004). Pairs trading: quantitative methods and analysis (Vol. 217). John Wiley & Sons.
[2]     Caldeira, J., & Moura, G. V. (2013). Selection of a portfolio of pairs based on cointegration: a statistical arbitrage strategy. Brazilian review of finance, 11(1), 49–80. DOI:10.2139/ssrn.2196391
[3]     Sipilä, M. (2013). Algorithmic pairs trading: empirical investigation of exchange traded funds [Thesis]. http://urn.fi/URN:NBN:fi:aalto-201306267090.
[4]     Do, B., & Faff, R. (2010). Does simple pairs trading still work? Financial analysts journal, 66(4), 83–95.
[5]     Goldkamp, J., & Dehghanimohammadabadi, M. (2019). Evolutionary multi-objective optimization for multivariate pairs trading. Expert systems with applications, 135, 113–128. DOI:10.1016/j.eswa.2019.05.046
[6]     Bui, L. T., & Alam, S. (2008). An introduction to multi-objective optimization. In Multi-objective optimization in computational intelligence: theory and practice (pp. 1–19). IGI Global.
[7]     Ehrman, D. S. (2006). The handbook of pairs trading: strategies using equities, options, and futures. John Wiley & Sons.
[8]     Zarei, V., Parvin, H., & Shahriari, A. (2019). Evolutionary learning-based decision tree [Thesis]. (In Persian). https://ganj.irandoc.ac.ir/#/articles/5e96bf528c2ee80e48bcb8ae96bda3a0.
[9]     Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relative-value arbitrage rule. The review of financial studies, 19(3), 797–827.
[10]   Papadakis, G., & Wysocki, P. (2007). Pairs trading and accounting information. http://web.mit.edu/wysockip/www/papers/PapadakisWysocki2008.pdf
[11]   Engelberg, J., Gao, P., & Jagannathan, R. (2009). An anatomy of pairs trading: the role of idiosyncratic news, common information and liquidity [presentation]. Third singapore international conference on finance, Singapore. http://dx.doi.org/10.2139/ssrn.1330689
[12]   Ma, B., & Ślepaczuk, R. (2022). The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods. University of Warsaw, Faculty of Economic Sciences.
[13]   Chen, C. H., Lai, W. H., Hung, S. T., & Hong, T. P. (2022). An advanced optimization approach for long-short pairs trading strategy based on correlation coefficients and bollinger bands. Applied sciences (switzerland), 12(3), 1052. DOI:10.3390/app12031052
[14]   Lu, J. Y., Lai, H. C., Shih, W. Y., Chen, Y. F., Huang, S. H., Chang, H. H., … & Dai, T. S. (2022). Structural break-aware pairs trading strategy using deep reinforcement learning. Journal of supercomputing, 78(3), 3843–3882. DOI:10.1007/s11227-021-04013-x
[15]   Ramos-Requena, J. P., Trinidad-Segovia, J. E., & Sánchez-Granero, M. Á. (2020). Some notes on the formation of a pair in Pairs Trading. Mathematics, 8(3), 348. DOI:10.3390/math8030348
[16]   Bajalan, S., Eyvazlu, R., & Akbari, G. (1999). Pair trading in tehran stock exchange based on smooth transition GARCH Model. Iranian journal of finance, 2(2), 7–28. (In Persian). DOI: 10.22034/IJF.2018.88416
[17]   Huck, N. (2013). The high sensitivity of pairs trading returns. Applied economics letters, 20(14), 1301–1304.
[18]   Barahimipour, M. M., & Davoodi, S. M. R. (2021). The profitability of  pairs trading strategy based on linear state-space models and the Kalman filter  in Tehran Stock Exchange. Journal of investment knowledge, 10(37), 57–75. (In Persian). html%0Ahttps://jik.srbiau.ac.ir/article_17091_369098cf4c35a92d3e5fdaa3202623db.pdf
[19]   Moradpour, S., & Dastoori, M. (2021). Algorithm trading application and persistence in the cryptocurrency market. Financial engineering and portfolio management, 12(47), 435–449. (In Persian). http://fej.iauctb.ac.ir/article_682736.html?lang=en
[20]   Dastori, M., & Moradpour, S. (2021). Optimization of High-frequency pair trading algorithm using a combination of genetic algorithm and fuzzy statistical quality control. Journal of investment knowledge, 10(40), 471-484. (In Persian). https://jik.srbiau.ac.ir/article_18249.html?lang=en
[21]   Jaliliyan, J., & Taherkhani, N. (2019). A survey on the pairs transactions strategy of in stock market of Iran (case study of investment companies of the stock market). Commercial surveys, 17(96), 23-37. (In Persian). http://barresybazargani.itsr.ir/article_36586_59a5770a62e67b0c3e1c6d417ad206c4.pdf
[22]   Tadi, M., Abkar, M., & Motaharinia, V. (2018). Evaluation of pairs trading strategy using distance approach at tehran stock exchange. Journal of investment knowledge, 7(26), 99-112. (In Persian). https://jik.srbiau.ac.ir/article_12603
[23]   Rastegar, M. A., & Sedaghatipour, A. (2018). Algorithmic trading system for future contract of gold coin based on intra-day data. Journal of investment knowledge, 7(28), 49-68. (In Persian). https://jik.srbiau.ac.ir/article_13306.html?lang=en
[24]   Pakizeh, K., & Habibi, S. (2017). Comparing profitability of the pair trading strategy in different asset classes. Journal of asset management and financing, 5(4), 69–88. (In Persian). DOI: 10.22108/AMF.2017.21195
[25]   Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International conference on parallel problem solving from nature (pp. 849-858). Springer, Berlin, Heidelberg. DOI:10.1007/3-540-45356-3_83/COVER
[26]   Wu, H., Huang, Y., Chen, L., Zhu, Y., & Li, H. (2022). Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II). Environmental research, 204, 111999. DOI:10.1016/j.envres.2021.111999
[27]   Dao, S. D., Abhary, K., & Marian, R. (2017). An innovative framework for designing genetic algorithm structures. Expert systems with applications, 90, 196–208. DOI:10.1016/j.eswa.2017.08.018
[28]   Sonmez, R., & Bettemir, Ö. H. (2012). A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert systems with applications, 39(13), 11428–11434. DOI:10.1016/j.eswa.2012.04.019
[29]   Tahir, M. A., & Smith, J. E. (2007). Feature selection for heterogeneous ensembles of nearest-neighbour classifiers using hybrid tabu search. In Advances in metaheuristics for hard optimization (pp. 69–85). Springer. DOI: 10.1007/978-3-540-72960-0_4
[30]   Naser Sadrabadi, A., & Taghavi, N. (2017). An introduction to meta-heuristic algorithms. PendarePars. (In Persian). https://www.gisoom.com/book/11511135/