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

Date
2022-06
Advisor
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Source Title
Annals of Operations Research
Print ISSN
0254-5330
Electronic ISSN
Publisher
Springer
Volume
313
Issue
2
Pages
639 - 690
Language
English
Type
Article
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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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Keywords
Algorithmic trading, Forecasting, Machine learning, Stock market
Citation
Published Version (Please cite this version)