Climate Risk and Stock Markets: Implications for Market Efficiency and Return Predictability

ABSTRACT

In this study, we set out to construct a daily frequency climate risk index with the aim to explore the impacts of climate risk on the stock markets of advanced and emerging countries and green stocks. We adopt an approach similar to the Sharpe ratio to demonstrate the pricing of climate risk into stocks and therefore reexamine the (in)efficiency of these markets. We specify an econometric model to evaluate the predictive content of the climate risk index for stock returns. We find compelling evidence of market efficiency when climate risk is priced into the stocks and market inefficiency otherwise. Results from the predictability analyses indicate that about 85% of advanced stock markets, 17% of emerging stock markets, and 100% of green stocks have the capacity to protect investors against climate risk. Forecast evaluations reveal that the climate risk index is a good predictor of the stock returns only after controlling for the macroeconomic environment. We explore several robustness checks and highlight both investment and policy implications.

KEYWORDS:

• Climate risk
• emerging markets
• green stocks
• market efficiency
• predictability

JEL:

• C51
• G14
• G15
• Q54

Acknowledgments

Balikis A. Kabir and Ayatullahi Abdulrahman provided excellent research assistance.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Disclaimer

The views expressed in this paper are solely the authors’ and do not reflect the opinions and beliefs of the affiliated institutions.

Notes

1. Venturini (2021) has already established climate risk as an important financial market risk, hence, it deserves formal investigation in this study.

2. The process adopted for constructing the daily index is extensively described in the second section of this article.

3. The method has been employed by Adediran and Swaray (2023) to examine the nexus between uncertainty (economic policy uncertainty & geopolitical uncertainty) and carbon trading risk. Salisu, Ndako, and Vo (2023) adopt the technique to show that climate risk (divided into physical & transition risks) increases the volatility of energy commodities.

4. Russia is excluded due to data unavailability.

5. A limitation of this study acknowledged here is the unavoidable special representation of China in the construction of the index due to the inability to include word counts from Weibo or other Chinese dailies caused by strong barriers against data scraping from those sources.

6. The second approach, which we deal with in Section 4 is to use the constructed index as a predictor for stock returns to examine the former’s predictability of stock returns in emerging and developed markets since market efficiency implies predictability of financial series.

7. The out-of-sample R² is constructed to lie as follows: $0<R^2\le 1$ such that $R^2=0$ indicates equal predictability of the two competing models, $R^2>0$ better predictability of the climate risk-based model, and $R^2<0$ inferior predictability of the climate risk-based model.