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

Date

2022-06

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Source Title

Annals of Operations Research

Print ISSN

0254-5330

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Springer

Volume

313

Issue

2

Pages

639 - 690

Language

English

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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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Published Version (Please cite this version)