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Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs

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  • Seung-Hyun Moon

    (School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Yourim Yoon

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Korea)

Abstract

Online portfolio selection (OLPS) is a procedure for allocating portfolio assets using only past information to maximize an expected return. There have been successful mean reversion strategies that have achieved large excess returns on the traditional OLPS benchmark datasets. We propose a genetic mean reversion strategy that evolves a population of portfolio vectors using a hybrid genetic algorithm. Each vector represents the proportion of the portfolio assets, and our strategy chooses the best vector in terms of the expected returns on every trading day. To test our strategy, we used the price information of the S&P 500 constituents from 2000 to 2017 and compared various strategies for online portfolio selection. Our hybrid genetic framework successfully evolved the portfolio vectors; therefore, our strategy outperformed the other strategies when explicit or implicit transaction costs were incurred.

Suggested Citation

  • Seung-Hyun Moon & Yourim Yoon, 2022. "Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs," Mathematics, MDPI, vol. 10(7), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1073-:d:780482
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    1. Myung Jig Kim & Charles R. Nelson & Richard Startz, 1991. "Mean Reversion in Stock Prices? A Reappraisal of the Empirical Evidence," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 515-528.
    2. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    3. Geoffrey Booth, G. & Fung, Hung-Gay & Leung, Wai Kin, 2016. "A risk-return explanation of the momentum-reversal “anomaly”," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 68-77.
    4. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    5. Fama, Eugene F. & French, Kenneth R., 2012. "Size, value, and momentum in international stock returns," Journal of Financial Economics, Elsevier, vol. 105(3), pages 457-472.
    6. László Györfi & Gábor Lugosi & Frederic Udina, 2006. "Nonparametric Kernel‐Based Sequential Investment Strategies," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 337-357, April.
    7. Seung-Hyun Moon & Yong-Hyuk Kim & Byung-Ro Moon, 2019. "Empirical investigation of state-of-the-art mean reversion strategies for equity markets," Papers 1909.04327, arXiv.org.
    8. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    9. Donald B. Keim & Ananth Madhavan, "undated". "The Cost of Institutional Equity Trades," Rodney L. White Center for Financial Research Working Papers 08-98, Wharton School Rodney L. White Center for Financial Research.
    10. Yoon, Yourim & Kim, Yong-Hyuk & Moon, Byung-Ro, 2012. "A theoretical and empirical investigation on the Lagrangian capacities of the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 218(2), pages 366-376.
    11. repec:fth:pennfi:68 is not listed on IDEAS
    12. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    13. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    14. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, February.
    15. Bin Li & Jialei Wang & Dingjiang Huang & Steven C. H. Hoi, 2018. "Transaction cost optimization for online portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1411-1424, August.
    16. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    17. Ronald Balvers & Yangru Wu & Erik Gilliland, 2000. "Mean Reversion across National Stock Markets and Parametric Contrarian Investment Strategies," Journal of Finance, American Finance Association, vol. 55(2), pages 745-772, April.
    18. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    19. Dong-Hee Cho & Seung-Hyun Moon & Yong-Hyuk Kim, 2021. "Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    20. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
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