Forecasting crude oil price with multilingual search engine data

Jingjing Li, Ling Tang and Shouyang Wang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 551, issue C

Abstract: In the big data era, search engine data (SED) have presented new opportunities for improving crude oil price prediction; however, the existing research were confined to single-language (mostly English) search keywords in SED collection. To address such a language bias and grasp worldwide investor attention, this study proposes a novel multilingual SED-driven forecasting methodology from a global perspective. The proposed methodology includes three main steps: (1) multilingual index construction, based on multilingual SED; (2) relationship investigation, between the multilingual index and crude oil price; and (3) oil price prediction, with the multilingual index as an informative predictor. With WTI spot price as studying samples, the empirical results indicate that SED have a powerful predictive power for crude oil price; nevertheless, multilingual SED statistically demonstrate better performance than single-language SED, in terms of enhancing prediction accuracy and model robustness.

Keywords: Big data; Multilingual search engine index; Crude oil price forecasting; Google Trends; Artificial intelligence (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:551:y:2020:i:c:s037843712030025x

DOI: 10.1016/j.physa.2020.124178

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