 Forecasting crude oil market returns: Enhanced moving average technical indicatorsDanyan Wen, Li Liu, Yudong Wang and Yaojie Zhang Resources Policy, 2022, vol. 76, issue C Abstract: Technical indicators are widely employed by practitioners, but they receive less attention in the literature of energy market forecasting. In this paper, we propose two enhanced moving average (MA) technical indicators—one that incorporates daily trading information (MA-D) and one that is the normalized form in which the MA is divided by price (MA-P) to predict monthly crude oil futures returns. Using univariate and multiple predictive models to predict monthly crude oil futures returns, we find that the enhanced MAs consistently outperform the general MAs, as well as macroeconomic variables. Notably, the out-of-sample values across five multiple predictive models for MA-Ps are relatively higher indicating their strong forecasting performances. Further analysis reveals that the prior 20 trading days have the most valuable forecasting information, even over longer predictive horizons. Our findings hold after a large set of extension and robustness analyses. Keywords: Crude oil futures; Return forecasting; Moving average; Multiple predictive models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:76:y:2022:i:c:s0301420722000216 DOI: 10.1016/j.resourpol.2022.102570 Access Statistics for this article Resources Policy is currently edited by R. G. Eggert More articles in Resources Policy from Elsevier |
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