Forecasting oil futures returns with news

Zhiyuan Pan, Hao Zhong, Yudong Wang and Juan Huang

Energy Economics, 2024, vol. 134, issue C

Abstract: This paper aims to explore the extent to which text data contains valuable information for predicting oil futures returns. A novel mixed-frequency data sampling random forest regression (MIDAS-RF) approach is proposed to construct a textual indicator. This approach can extract nonlinearity and interaction information from news and allows us to better handle the mixed-frequency and high-dimensional data. Comparing it with traditional sentiment variables and financial factors, our indicator demonstrates better forecasting performance both statistically and economically, with a monthly out-of-sample R2 of 5.26% and an annualized certainty equivalent return gain of 3.08%, respectively. Further evidence suggests that the predictability of the textual indicator is primarily driven by words related to capital markets and macroeconomic topics.

Keywords: Oil futures; Return predictability; Machine learning; Mixed-frequency data sampling; Textual analysis (search for similar items in EconPapers)
JEL-codes: G12 G13 G14 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324003141

DOI: 10.1016/j.eneco.2024.107606

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