Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction

Ahmet Göncü, Tolga U. Kuzubaş and Burak Saltoğlu

Finance Research Letters, 2024, vol. 67, issue PB

Abstract: This paper investigates the effectiveness of machine learning algorithms, including logistic regression, artificial neural networks, support vector machines, gradient boosting algorithms (XGBoost, ExtraTrees), random forests, and convolutional neural network (CNN) for trend prediction of daily spot oil prices across horizons of 1 to 8 days. We utilize a comprehensive set of features, including technical indicators, financial data, and volatility measures, to predict trends in closing prices. Our results reveal that the CNN model significantly outperforms other algorithms. This superior performance likely stems from CNN’s ability to capture visual patterns in price movements, potentially mimicking how traders identify trends.

Keywords: Artificial neural networks; Support vector machines; Random forest; XGboost; Extreme trees classification; Convolutional neural networks (CNN) (search for similar items in EconPapers)
JEL-codes: C45 C53 G17 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324009048

DOI: 10.1016/j.frl.2024.105874

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