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Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?

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Listed:
  • Tian, Guangning
  • Peng, Yuchao
  • Du, Huancheng
  • Meng, Yuhao

Abstract

Numerous studies have demonstrated the strong out-of-sample predictive ability of machine learning models, particularly in variable selection and dimension reduction, on crude oil price returns. We find significant disparities in out-of-sample predictive performance between these two methods under varying degrees of ambiguity, a fuzziness measure proposed by Izhakian (2020), independent of outcomes, risks, and attitudes. Variable selection methods exhibit strong out-of-sample predictive power in low ambiguity environments, but weaker performance in high ambiguity environments, whereas dimension reduction methods show the opposite pattern. Furthermore, the optimal penalty coefficient selected by variable selection methods during in-sample model fitting is highly correlated with ambiguity, indicating that the predictive ability of variable selection stems from its ability to accurately identify the correct predictors.

Suggested Citation

  • Tian, Guangning & Peng, Yuchao & Du, Huancheng & Meng, Yuhao, 2024. "Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?," Energy Economics, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:eneeco:v:139:y:2024:i:c:s0140988324005759
    DOI: 10.1016/j.eneco.2024.107867
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    More about this item

    Keywords

    Oil return forecast; Ambiguity; Machine learning; Variable selection; Dimension reduction;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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