Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques

Mimoun Benali and Lahboub Karima
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Mimoun Benali: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), National School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco.
Lahboub Karima: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), National School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco.

International Journal of Energy Economics and Policy, 2024, vol. 14, issue 2, 594-602

Abstract: This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating 1 day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-1-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.

Keywords: Machine Learning; Stock Price; Energy Sector; Regression; Price Prediction (search for similar items in EconPapers)
JEL-codes: C54 Q4 (search for similar items in EconPapers)
Date: 2024
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