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Forecasting stock market indices using machine learning algorithms

Author

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  • Berislav Žmuk

    (University of Zagreb - Faculty of Economics and Business, Zagreb, Croatia)

  • Hrvoje Jošiæ

    (University of Zagreb - Faculty of Economics and Business, Zagreb, Croatia)

Abstract

In recent years machine learning algorithms have become a very popular tool for analysing financial data and forecasting stock prices. The goal of this article is to forecast five major stock market indexes (DAX, Dow Jones, NASDAQ, Nikkei 225 and S&P 500) using machine learning algorithms (Linear regression, Gaussian Processes, SMOreg and neural network Multilayer Perceptron) on historical data covering the period February 1, 2010, to January 31, 2020. The forecasts were made by using historical data in different base period lengths and forecasting horizons. The precision of machine learning algorithms was evaluated with the help of error metrics. The results of the analysis have shown that machine learning algorithms achieved highly accurate forecasting performance. The overall precision of all algorithms was better for shorter base period lengths and forecast horizons. The results obtained from this analysis could help investors in determining their optimal investment strategy. Stock price prediction remains, however, one of the most complex issues in the field of finance.

Suggested Citation

  • Berislav Žmuk & Hrvoje Jošiæ, 2020. "Forecasting stock market indices using machine learning algorithms," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(4), pages 471-489.
    • Handle: RePEc:zna:indecs:v:18:y:2020:i:4:p:471-489
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    References listed on IDEAS

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    1. Arnerić Josip & Poklepović Tea & Teai Juin Wen, 2018. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data," Business Systems Research, Sciendo, vol. 9(2), pages 18-34, July.
    2. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    3. Gary Grudnitski & Larry Osburn, 1993. "Forecasting S&P and gold futures prices: An application of neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(6), pages 631-643, September.
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    Cited by:

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    More about this item

    Keywords

    machine learning; neural networks; stock market indices prediction;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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