2018
DOI: 10.1002/mde.2916
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The usefulness of oil price forecasts—Evidence from survey predictions

Abstract: JEL Classification: D81; F37; F47This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous r… Show more

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Cited by 13 publications
(6 citation statements)
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“…Many studies have proven that nonlinearities arise in financial markets and that statistical models cannot effectively control them. With the rapid rise of AI and machine learning over the last decade, an increasing number of financial professionals have begun to analyse the index value of gaugeable models, have different requirements, and experiment with diverse methodologies [ 8 ]. K-NNs [ 9 ], Bayes classifiers [ 10 ], decision trees [ 11 ], and SVMs [ 12 ] are presently widely used for classification tasks [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…Many studies have proven that nonlinearities arise in financial markets and that statistical models cannot effectively control them. With the rapid rise of AI and machine learning over the last decade, an increasing number of financial professionals have begun to analyse the index value of gaugeable models, have different requirements, and experiment with diverse methodologies [ 8 ]. K-NNs [ 9 ], Bayes classifiers [ 10 ], decision trees [ 11 ], and SVMs [ 12 ] are presently widely used for classification tasks [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…The present study follows the second approach as we aim to examine the expectations formation and information processing of forecasters. Studies assessing survey-based crude oil price forecasts relying on different data sources include Prat and Uctum (2011), Reitz et al (2012), Alquist et al (2013), Leppin (2016), Kunze et al (2018) and Moghaddam et al (2019). Overall, these studies show that the concepts of rational expectations and unbiasedness are rejected for survey forecasts and therefore also highlight the need to study the expectations formation mechanism of professionals involved in the crude oil market.…”
Section: Introductionmentioning
confidence: 99%
“…As of now, with the fast improvement of AI and manufactured reasoning in the previous 10 years, an ever-increasing number of market analysts have begun to execute the index value estimating of gaugeable models, have exclusive requirements, and have attempted different strategies [2]. The best standard for deciding on the presentation of the model is to look at the anticipated effects of the model with genuine information.…”
Section: Introductionmentioning
confidence: 99%