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Identification of nonlinear determinants of stock indices derived by Random Forest algorithm

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  • Tratkowski Grzegorz

    (Wrocław University of Economics, Wrocław, Poland.)

Abstract

In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment firms and individual investors need comprehensive and insight information such as the drivers of stock price movements and relationships existing between the various factors of the stock market so that they can invest efficiently through better understanding. Our work focuses on determining the factors that drive the future price movements of Stoxx Europe 600, DAX, and WIG20 by using the importance of input variables in the Random Forest classifier. The main determinants were derived from a large dataset containing macroeconomic and market data, which were collected everyday through various ways.

Suggested Citation

  • Tratkowski Grzegorz, 2020. "Identification of nonlinear determinants of stock indices derived by Random Forest algorithm," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 56(3), pages 209-217, September.
    • Handle: RePEc:vrs:ijomae:v:56:y:2020:i:3:p:209-217:n:3
    DOI: 10.2478/ijme-2020-0017
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    References listed on IDEAS

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    More about this item

    Keywords

    determinants; Random Forest; stock index; machine learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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