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


Akyildirim, Erdinc; Bariviera, Aurelio F; Nguyen, Duc Khuong; Sensoy, Ahmet (2022). Forecasting high-frequency stock returns: a comparison of alternative methods. Annals of Operations Research, 313:639-690.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > General Decision Sciences
Social Sciences & Humanities > Management Science and Operations Research
Scope:Discipline-based scholarship (basic research)
Language:English
Date:June 2022
Deposited On:28 Jun 2022 06:37
Last Modified:27 Jun 2024 01:38
Publisher:Springer
ISSN:0254-5330
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/s10479-021-04464-8
Official URL:https://doi.org/10.1007/s10479-021-04464-8
Other Identification Number:merlin-id:21947
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