Climate policy uncertainty and the stock return predictability of the oil industry

Mengxi He and Yaojie Zhang

Journal of International Financial Markets, Institutions and Money, 2022, vol. 81, issue C

Abstract: This paper uses a news-based climate policy uncertainty (CPU) proposed by Gavriilidis (2021) to test the stock return predictability of the oil industry. Results show that CPU is a strong predictor of future oil industry stock returns both in- and out-of-sample. The predictive power of CPU is informationally complementary to existing uncertainty indicators and far greater than that of other uncertainty indicators, economic variables, new predictors, and oil industry-specific predictors. Furthermore, CPU can provide sizeable economic gains to mean–variance investors. The driving force of CPU’s predictive power appears to stem from its ability to predict future cash flows in the oil industry. We also explain the predictability of CPU from the perspective of oil-related fundamentals and investor attention.

Keywords: Climate policy uncertainty; Return predictability; Oil industry; Cash flow; Investor attention (search for similar items in EconPapers)
JEL-codes: C53 G11 G12 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1042443122001470
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:intfin:v:81:y:2022:i:c:s1042443122001470

DOI: 10.1016/j.intfin.2022.101675

Access Statistics for this article

Journal of International Financial Markets, Institutions and Money is currently edited by I. Mathur and C. J. Neely

More articles in Journal of International Financial Markets, Institutions and Money from Elsevier
Bibliographic data for series maintained by Catherine Liu ().