Forecasting Spanish Olive Oil Prices Using Machine Learning: A Comprehensive Analysis of Contributing Factors and Model Performance

Keffala Mohamed Rochdi () and Mahjoub Safoua ()
Additional contact information
Keffala Mohamed Rochdi: Assistant Professor in Finance at High Institute of Accountancy and Business Administration, and Member of Ligue Laboratory LR99ES24, University of Manouba, Manouba, Tunisia
Mahjoub Safoua: Finance Graduate from Tunis Business School, University of Tunis, Tunis, Tunisia

Journal of Agricultural & Food Industrial Organization, 2025, vol. 23, issue 2, 107-121

Abstract: The main goal of this study was to create a strong predictive model for forecasting olive oil prices. To do this, we applied four machine learning models in Python: Random Forest, Gradient Boosting, Decision Tree, and Support Vector Regression, using 164 monthly price observations along with factors like temperature, precipitation, consumer price index, IBEX35 stock market prices, EUR/USD exchange rate, and import/export quantities. The results showed that Random Forest and Gradient Boosting models performed the best. The Spearman correlation analysis revealed that the exchange rate had a strong negative correlation with prices, while the consumer price index and import quantity had moderate positive correlations. Random Forest highlighted the consumer price index as the most important factor in predicting olive oil prices. This study fills a gap in existing research and provides practical insights for companies in the olive oil industry to better monitor and forecast prices, helping with profitability, risk management, stock optimization, and investment decisions.

Keywords: Spanish olive oil; price forecasting; machine learning models (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jafio-2024-0067 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:bjafio:v:23:y:2025:i:2:p:107-121:n:1006

Ordering information: This journal article can be ordered from
https://www.degruyte ... urnal/key/jafio/html

DOI: 10.1515/jafio-2024-0067

Access Statistics for this article

Journal of Agricultural & Food Industrial Organization is currently edited by Azzeddine Azzam

More articles in Journal of Agricultural & Food Industrial Organization from De Gruyter
Bibliographic data for series maintained by Peter Golla ().