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Commodity factor investing via machine learning

Author

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  • Zhu, Shunwei
  • Zhou, Chunyang
  • Liu, Hailong
  • Ren, Yangyang

Abstract

We investigate the factor investing in Chinese commodities markets following two steps. The first step is to find profitable characteristics. We find that some technical characteristics can produce a comparable out-of-sample performance to the fundamental characteristics. The second step is to integrate various commodity characteristics to generate a composite signal. We apply the naïve equal-weighted model, three linear models and four tree-ensemble nonlinear models for style integration. The empirical results show that the four nonlinear machine learning integration models produce better out-of-sample performance than the linear models. Meanwhile, among the four tree-ensemble algorithms, the XGBoost algorithm performs best with control of the overfitting problem.

Suggested Citation

  • Zhu, Shunwei & Zhou, Chunyang & Liu, Hailong & Ren, Yangyang, 2024. "Commodity factor investing via machine learning," Pacific-Basin Finance Journal, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:pacfin:v:83:y:2024:i:c:s0927538x23003025
    DOI: 10.1016/j.pacfin.2023.102231
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