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Forecasting crude oil price: A deep forest ensemble approach

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  • Liu, Wei-han
  • Xu, Xingfu

Abstract

We made an application of a cutting-edge machine learning method, the deep forest ensemble approach (DFEA, Zhou and Feng (2017, 2019)), to empirically predict crude oil prices. We used a large data set with 36 explanatory variables to compare the predictability of the DFEA with seven popular machine learning models and their mean combination method. The out-of-sample forecasting results showed that the DFEA statistically and economically outperforms all the competing models in terms of out-of-sample R square and success ratio. The DFEA also displayed sizable certainty equivalent return (CER) gains for a mean-variance investor in practice from an asset allocation perspective. Furthermore, we found that the predictive power of the DFEA stems from technical indicators, especially momentum predictors. Our results survived in various robustness checks.

Suggested Citation

  • Liu, Wei-han & Xu, Xingfu, 2024. "Forecasting crude oil price: A deep forest ensemble approach," Finance Research Letters, Elsevier, vol. 69(PB).
    • Handle: RePEc:eee:finlet:v:69:y:2024:i:pb:s1544612324011826
    DOI: 10.1016/j.frl.2024.106153
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    References listed on IDEAS

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    More about this item

    Keywords

    Machine learning methods; Deep forest ensemble approach; Support vector machine; LASSO;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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