How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores

Christian Haas, Constantin Budin and d’Arcy, Anne

Energy Economics, 2024, vol. 133, issue C

Abstract: Predicting crude oil prices is an important yet challenging forecasting problem due to various influencing quantitative and qualitative factors. To address the growing number of potential prediction models and model parameters to consider during model selection, we highlight the need to systematically compare alternative prediction models and variables while taking the specific context of their application into account. Specifically, we provide a novel perspective on oil price prediction models by comparing a variety of different forecasting models and considering both their statistical and financial performance. In addition to common statistical measures, to assess the usefulness in a practical setting we evaluate the potential financial impact of the predictions in a simulation of a simple trading strategy. We show that the ranking of different approaches depends on the selected evaluation metric and that small differences between models in one evaluation metric can translate into large differences in another metric. For instance, forecasts that are not considered statistically different can lead to substantially different financial performance when the forecasts are used in a trading strategy. Finally, we show that including qualitative information in the prediction models through sentiment analysis can yield both statistical and financial performance improvements.

Keywords: Oil-price prediction; Machine learning; Sentiment analysis; Performance metrics; Model selection; Forecasting models (search for similar items in EconPapers)
JEL-codes: C45 C49 C51 C52 C53 G17 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:133:y:2024:i:c:s0140988324001749

DOI: 10.1016/j.eneco.2024.107466

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