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The performance of time series forecasting based on classical and machine learning methods for S&P 500 index

Author

Listed:
  • Maudud Hassan Uzzal

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

  • Robert Ślepaczuk

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

Abstract

Based on one step ahead forecasts, this study compares the forecasting abilities of the traditional technique (ARIMA) with recurrent neural network (LSTM). In order to check the possible use of these forecasts in different asset management methods, these forecasts are afterwards included into trading signals of investment strategies. As a benchmark method, the Random Walk model producing naive forecasts has been utilized. This research examines daily data from the S&P 500 index for 20 years, from 2000 to 2020, and it includes information on some significant market turbulence. The methods were tested in terms of robustness to changes in parameters and hyperparameters and evaluated based on various error metrics (MAE, MAPE, RMSE MSE). The results show that ARIMA outperforms LSTM in terms of one step ahead forecasts. Finally, LSTM model with a variety of hyperparameters - including a number of epochs, a loss function, an optimizer, activation functions, a number of units, a batch size, and a learning rate - was tested in order to check its robustness.

Suggested Citation

  • Maudud Hassan Uzzal & Robert Ślepaczuk, 2023. "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers 2023-05, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2023-05
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/2554/0
    File Function: First version, 2023
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    References listed on IDEAS

    as
    1. Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem," Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
    2. Sidra Mehtab & Jaydip Sen, 2019. "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers 1912.07700, arXiv.org.
    3. Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    deep learning; recurrent neural networks; ARIMA; algorithmic investment strategies; trading systems; LSTM; walk-forward process; optimization;
    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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