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Textual analysis and gold futures price forecasting: Evidence from the Chinese market

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  • Liu, Yanchu
  • Zhang, Yu
  • Peng, Xinyi

Abstract

This paper examines the predictive capacity of online news on the gold futures prices. The empirical results derived from the Chinese market demonstrate that the textual features extracted through natural language processing techniques contain complementary predictive content for gold futures prices, which enhance the 1-day ahead prediction accuracy across different machine learning methods and train-test sets.

Suggested Citation

  • Liu, Yanchu & Zhang, Yu & Peng, Xinyi, 2024. "Textual analysis and gold futures price forecasting: Evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 69(PA).
    • Handle: RePEc:eee:finlet:v:69:y:2024:i:pa:s1544612324011450
    DOI: 10.1016/j.frl.2024.106116
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    References listed on IDEAS

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    1. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
    2. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Liu, Sijie & Geng, Yong & Gao, Ziyan & Li, Jinze & Xiao, Shijiang, 2023. "Uncovering the key features of gold flows and stocks in China," Resources Policy, Elsevier, vol. 82(C).
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