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A random walk through the trees: Forecasting copper prices using decision learning methods

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  • Díaz, Juan D.
  • Hansen, Erwin
  • Cabrera, Gabriel

Abstract

We investigate the accuracy of copper price forecasts produced by three decision learning methods. Prior evidence (Liu et al. Resources Policy, 2017) shows that a regression tree, a simple decision learning model, can be used to predict copper prices for both short-term and long-term horizons (several days and several years, respectively). We contribute to this literature by evaluating more sophisticated decision learning methods based on trees: random forests and gradient boosting regression trees. Our results indicate that random forests and gradient boosting regression trees significantly outperform regression trees at forecasting copper prices. Our analysis also reveals that a random walk process, recognized in the literature as one of the most useful models for forecasting copper prices, yields competitive out-of-sample forecasts as compared to these decision learning methods.

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  • Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    • Handle: RePEc:eee:jrpoli:v:69:y:2020:i:c:s0301420720308904
    DOI: 10.1016/j.resourpol.2020.101859
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    3. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
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    5. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
    6. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    7. László Vancsura & Tibor Tatay & Tibor Bareith, 2023. "Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times," Risks, MDPI, vol. 11(2), pages 1-16, January.
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