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Forecasting a Stock Trend Using Genetic Algorithm and Random Forest

Author

Listed:
  • Rebecca Abraham

    (Huizenga College of Business, Nova Southeastern University-SBE, 3301 College Avenue, Fort Lauderdale, FL 33319, USA)

  • Mahmoud El Samad

    (School of Arts and Sciences, Lebanese International University, Mouseitbah, Mazara P.O. Box 146404, Lebanon)

  • Amer M. Bakhach

    (School of Arts and Sciences, Lebanese International University, Mouseitbah, Mazara P.O. Box 146404, Lebanon)

  • Hani El-Chaarani

    (College of Business Administration, Tripoli Campus, Beirut Arab University, Beirut P.O. Box 11-50-20, Lebanon)

  • Ahmad Sardouk

    (Faculty of Economics and Business Administration, Tripoli Campus, Lebanese University (UL), Beirut P.O. Box 6573/14, Lebanon)

  • Sam El Nemar

    (Faculty of Business Administration, AZM University, Tripoli P.O. Box 1010, Lebanon)

  • Dalia Jaber

    (School of Arts and Sciences, Lebanese International University, Mouseitbah, Mazara P.O. Box 146404, Lebanon)

Abstract

This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.

Suggested Citation

  • Rebecca Abraham & Mahmoud El Samad & Amer M. Bakhach & Hani El-Chaarani & Ahmad Sardouk & Sam El Nemar & Dalia Jaber, 2022. "Forecasting a Stock Trend Using Genetic Algorithm and Random Forest," JRFM, MDPI, vol. 15(5), pages 1-18, April.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:5:p:188-:d:796978
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    References listed on IDEAS

    as
    1. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    2. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    3. Hani El-Chaarani, 2019. "The Impact of Oil Prices on Stocks Markets: New Evidence During and After the Arab Spring in Gulf Cooperation Council Economies," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 214-223.
    4. B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    Full references (including those not matched with items on IDEAS)

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