Predicting crude oil returns and trading position: evidence from news sentiment

Hail Jung and Daejin Kim

Journal of Derivatives and Quantitative Studies: ????????????, 2025, vol. 33, issue 2, 86-109

Abstract: We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly effective in explaining the crude oil futures returns, while existing textual analyses based on pre-defined dictionaries may mislead the contexts in the oil market. Consistent with previous findings that returns help explain the change in traders’ positions, the sentiment scores based on the machine learning method are also useful in explaining the behavior of different types of traders. Our empirical findings underscore the fact that accurately identifying textual information can increase the accuracy of oil price predictions and explain divergent behaviors of oil traders.

Keywords: Crude oil; Prediction; Textual analysis; Machine learning; Trading position (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/jdqs/article/33/2/86/12638 ... and-trading-position (text/html)
https://www.emerald.com/jdqs/article-pdf/33/2/86/9846695/jdqs-12-2024-0050.pdf (application/pdf)

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:eme:jdqspp:jdqs-12-2024-0050

DOI: 10.1108/JDQS-12-2024-0050

Access Statistics for this article

More articles in Journal of Derivatives and Quantitative Studies: ???????????? from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().