Work

The role of textual analysis in oil futures price forecasting based on machine learning approach

Year: 2022

Type: article

Abstract: This paper offers an innovative approach to capture the trend of oil futures prices based on the text‐based news. By adopting natural language processing techniques, the text features obtained from online oil news catch more hidden information, improving the forecasting accuracy of oil futures prices. We find that the textual features are complementary in improving forecasting performance, both for LightGBM and benchmark models. Besides, event studies verify the asymmetric impact of positive and negative emotional shocks on oil futures prices. The generated text‐based news features robustly reduce forecasting errors, and the reduction can be maximized by incorporating all features.

Source: Journal of Futures Markets

DOI: 10.1002/fut.22367

Authors: Xu Gong, Keqin Guan, Qiyang Chen

Institutions: Xiamen University, Carnegie Mellon University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Award: 基于混频数据的大宗商品价格波动建模及其应用研究