Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction
Finance Research Letters, cilt.67, 2024 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 67
- Basım Tarihi: 2024
- Doi Numarası: 10.1016/j.frl.2024.105874
- Dergi Adı: Finance Research Letters
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
- Anahtar Kelimeler: Artificial neural networks, Support vector machines, Random forest, XGboost, Extreme trees classification, Convolutional neural networks (CNN)
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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.