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.
Acknowledgments
This work is supported by the financial support from the National Natural Science Foundation of China [71901122, 72001110, 71771124]; the Fundamental Research Funds for the Central Universities [30919013204, 30919013232].
Disclosure statement
No potential conflict of interest was reported by the author(s).
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
This work was supported by National Natural Science Foundation of China: [Grant Number 71901122,72001110, 71771124].
Supplemental data
Supplemental data for this article can be accessed at http://doi.org/10.1080/14697688.2022.2074305.
Notes
1 To save space, the detailed descriptions of the 90 technical indicators according added technical rules are available in the Internet Appendix.
2 Owing to the page limitation, the related results are available in the Internet Appendix.