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Application of machine learning in quantitative investment strategies on global stock markets

Author

Listed:
  • Jan Grudniewicz

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

Abstract

The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500 and selected CEE indices in the period between 2002-01-01 to 2020-10-30. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis. The research results indicated that quantitative strategies generate better risk adjusted returns than passive strategies and that for the analysed indices predominantly Bayesian Generalized Linear Model and Naïve Bayes were the best performing models. More comprehensive rank approach based on the results for all analysed models and indices allowed to select Bayesian Generalized Linear Model as the model which on average generated the best results.

Suggested Citation

  • Jan Grudniewicz & Robert Ślepaczuk, 2021. "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers 2021-23, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2021-23
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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/6844/
    File Function: First version, 2021
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    Citations

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    Cited by:

    1. Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.

    More about this item

    Keywords

    quantitative investment strategies; machine learning; neural networks; regression trees; random forests; support vector machine; technical analysis; equity stock indices; developed and emerging markets; information ratio;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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