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Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

Author

Listed:
  • Dong-Hee Cho

    (Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Seung-Hyun Moon

    (Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Yong-Hyuk Kim

    (Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

Abstract

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2574-:d:655750
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    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    3. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    5. repec:pri:cepsud:91malkiel is not listed on IDEAS
    6. Markowitz, Harry M, 1991. "Foundations of Portfolio Theory," Journal of Finance, American Finance Association, vol. 46(2), pages 469-477, June.
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    Cited by:

    1. Yong-Hyuk Kim & Fabio Caraffini, 2023. "Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”," Mathematics, MDPI, vol. 11(5), pages 1-3, March.
    2. 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.
    3. Pawnrat Thumrongvut & Kanchana Sethanan & Thitipong Jamrus & Chuleeporn Wongloucha & Rapeepan Pitakaso & Paulina Golinska-Dawson, 2022. "Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    4. Seung-Soo Shin & Yong-Hyuk Kim, 2023. "Optimal Agent Search Using Surrogate-Assisted Genetic Algorithms," Mathematics, MDPI, vol. 11(1), pages 1-16, January.
    5. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.

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