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Forecasting crude oil prices: do technical indicators need economic constraints?

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Listed:
  • Danyan Wen
  • Mengxi He
  • Li Liu
  • Yaojie Zhang

Abstract

This study aims to improve the forecasting performance of technical indicators for crude oil prices by imposing economic constraints on the sign of the shrinkage estimators. The out-of-sample results indicate that our constrained methods deliver significantly stronger forecasts than their standard forms and prevalent predictive models. Moreover, the advantages of the constrained methods are stronger during recessions and weaker during expansions. The superior forecasting performance of the constrained methods is not affected by the consideration of long-horizon forecasts and is robust to a large body of alternative specifications. In addition to the statistical tests, we provide evidence that investors who use the new predictive framework can realize sizable economic gains through asset allocations and market timing.

Suggested Citation

  • Danyan Wen & Mengxi He & Li Liu & Yaojie Zhang, 2022. "Forecasting crude oil prices: do technical indicators need economic constraints?," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1545-1559, August.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:8:p:1545-1559
    DOI: 10.1080/14697688.2022.2074305
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    Cited by:

    1. Qingxiang Han & Mengxi He & Yaojie Zhang & Muhammad Umar, 2023. "Default return spread: A powerful predictor of crude oil price returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1786-1804, November.
    2. Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023. "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, vol. 125(C).
    3. Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "The predictability of iron ore futures prices: A product‐material lead–lag effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1289-1304, September.
    4. He, Mengxi & Shen, Lihua & Zhang, Yaojie & Zhang, Yi, 2023. "Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor," Finance Research Letters, Elsevier, vol. 58(PA).

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