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Identifying the influential factors of commodity futures prices through a new text mining approach

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Listed:
  • Jianping Li
  • Guowen Li
  • Xiaoqian Zhu
  • Yanzhen Yao

Abstract

How to determine the numerous and complicated factors that affect the dynamics of commodity futures prices is still a great challenge. The existing studies have mainly identified influential factors based on researchers’ judgements or the summarization of previous studies, which is relatively subjective and makes it difficult to attain comprehensive factors. This paper proposes a new text mining method named Dependency Parsing-Sentence-Latent Dirichlet Allocation (DP-Sent-LDA) to identify the influential factors of commodity futures prices objectively and comprehensively from a massive number of news headlines. In the empirical analysis, based on 49 501 news headlines about six Chinese commodity futures over the period of 2011–2018, a total of 104 specific influential factors are identified, and their relative importance is given. The identified influential factors not only contain almost all the widely studied influential factors but also include some factors rarely mentioned in the existing literature. Regression analysis is conducted to validate the effectiveness of these influential factors.

Suggested Citation

  • Jianping Li & Guowen Li & Xiaoqian Zhu & Yanzhen Yao, 2020. "Identifying the influential factors of commodity futures prices through a new text mining approach," Quantitative Finance, Taylor & Francis Journals, vol. 20(12), pages 1967-1981, December.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:12:p:1967-1981
    DOI: 10.1080/14697688.2020.1814008
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    Cited by:

    1. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. Li, Guowen & Jing, Zhongbo & Li, Jingyu & Feng, Yuyao, 2023. "Drivers of risk correlation among financial institutions: A study based on a textual risk disclosure perspective," Economic Modelling, Elsevier, vol. 128(C).
    3. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.

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