IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i20p2574-d655750.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/20/2574/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/20/2574/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    2. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. repec:pri:cepsud:91malkiel is not listed on IDEAS
    4. Markowitz, Harry M, 1991. "Foundations of Portfolio Theory," Journal of Finance, American Finance Association, vol. 46(2), pages 469-477, June.
    5. 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).
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kofi A. Amoateng, 2019. "Did Tom Brady Save the US stock market? Market Anomaly or Market Efficiency?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(5), pages 128-128, May.
    2. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    3. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    4. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    5. Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
    6. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    7. Nathan Jensen, 2007. "International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 U.S. steel tariffs," The Review of International Organizations, Springer, vol. 2(3), pages 261-280, September.
    8. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    9. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
    10. John Sabelhaus, 2005. "Alternative Methods for Projecting Equity Returns: Implications for Evaluating Social Security Reform Proposals," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 8(1), pages 43-63, March.
    11. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    12. Stephen Bell & John Quiggin, 2006. "Asset Price Instability and Policy Responses: The Legacy of Liberalization," Journal of Economic Issues, Taylor & Francis Journals, vol. 40(3), pages 629-649, September.
    13. Paolo Cremonesi & Chiara Francalanci & Alessandro Poli & Roberto Pagano & Luca Mazzoni & Alberto Maggioni & Mehdi Elahi, 2018. "Social Network based Short-Term Stock Trading System," Papers 1801.05295, arXiv.org.
    14. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability Of Technical Trading Rules In Us Futures Markets: A Data Snooping Free Test," 2004 Conference, April 19-20, 2004, St. Louis, Missouri 19011, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    15. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    16. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    17. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.
    18. Abootaleb Shirvani & Svetlozar T. Rachev & Frank J. Fabozzi, 2019. "A Rational Finance Explanation of the Stock Predictability Puzzle," Papers 1911.02194, arXiv.org.
    19. Admin Starcevic & Timothy Rodgers, 2011. "Market Efficiency within the German Stock Market: A Comparative Study of the Relative Efficiencies of the DAX, MDAX, SDAX and ASE Indices," International Econometric Review (IER), Econometric Research Association, vol. 3(1), pages 25-37, April.
    20. Olayinka Oyekola & Sofia Johan & Rilwan Sakariyahu & Oluwatoyin Esther Dosumu & Shima Amini, 2023. "Political institutions, financial liberalisation, and access to finance: firm-level empirical evidence," Discussion Papers 2307, University of Exeter, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2574-:d:655750. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.