Google trends and the predictability of precious metals

Afees Salisu, Ahamuefula Ogbonna and Adeolu Adewuyi

Resources Policy, 2020, vol. 65, issue C

Abstract: In this paper, we examine how Google trends can influence the predictability of the most traded precious metals, namely Gold, Palladium, Platinum and Silver. We construct a predictive model that simultaneously accounts for conditional heteroscedasticity, due to the use of high frequency data; endogeneity bias due to probable exclusion of important predictors and persistence due to the dynamic behaviour of economic agents. We also allow for asymmetry in Google trends (G-trends hereafter), to explain positive and negative worded news in the predictability of precious metals. The G-trends series appears to, positively and significantly, impact the returns accruable to the considered precious metals and its inclusion in the predictive model outperforms the random walk model. Also, additional information from other macroeconomic variables, as well as the negative and positive partial sums of G-trends, significantly improve the predictive performance of the single predictor G-trends-based model for precious metals. On robustness, while we find consistency of model forecast performance both in the in-sample and out-of-sample forecast periods, and across global factors employed, our results seem to be dependent on the choice of precious metal being predicted.

Keywords: Google trends; Precious metals; Predictability; Forecast evaluation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420719307408
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:65:y:2020:i:c:s0301420719307408

DOI: 10.1016/j.resourpol.2019.101542

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().