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Forecasting high-frequency stock returns: a comparison of alternative methods

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  • Erdinc Akyildirim

    (Burdur Mehmet Akif Ersoy University
    University of Zurich)

  • Aurelio F. Bariviera

    (Universitat Rovira i Virgili)

  • Duc Khuong Nguyen

    (IPAG Business School
    Vietnam National University)

  • Ahmet Sensoy

    (Bilkent University)

Abstract

We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naïve Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.

Suggested Citation

  • Erdinc Akyildirim & Aurelio F. Bariviera & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "Forecasting high-frequency stock returns: a comparison of alternative methods," Annals of Operations Research, Springer, vol. 313(2), pages 639-690, June.
  • Handle: RePEc:spr:annopr:v:313:y:2022:i:2:d:10.1007_s10479-021-04464-8
    DOI: 10.1007/s10479-021-04464-8
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    References listed on IDEAS

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    Cited by:

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