Unraveling the Relationship between ESG and Corporate Financial Performance - Logistic Regression Model with Evidence from China

16 Pages Posted: 4 Aug 2021

Date Written: August 1, 2021

Abstract

With growing awareness of sustainability, the field of Environmental, Social and Governance (ESG), has been attracting mainstream investors and researchers. Many previous studies have found inconclusive or mixed results on the relationship between ESG ratings and firms’ financial performance, which are mainly attributed to their varied markets, time horizons, and sources of ESG rating. Based on evidence from an emerging market, namely China, this paper examines whether ESG is an adequate indicator for firms’ future financial performance. Given the divergence in ESG rating methodologies, we use ESG data from two ESG rating agencies, one based in China (SynTao) and the other based in Switzerland (RepRisk), for robustness. Specifically, we investigate 377 China A-share companies covered by both agencies and find that ESG rating, albeit divergent due to disparate methodologies, is instrumental in predicting the trend of corporate financial performance (CFP). This work verifies that the forward-looking nature of ESG makes it crucial for firms’ long-term valuation and financial performance in emerging markets. Throughout the research, we observe four issues in the current ESG rating process: the opacity and inaccessibility of source data, the obscurity of ESG rating methodologies adopted by rating agencies, the lack of automated pipeline, and the unannounced historical data rewriting. We believe that the public blockchain ecosystem is promising to address these issues, and we propose future research on the ESG framework for blockchain to call for sustainability focus on this emerging technology.

Keywords: ESG,ESG ratings,China,Logistic Regression,Corporate Financial Performance,Blockchain

JEL Classification: G24,G30,M14,Q56

Suggested Citation

Tian, Lewis, Unraveling the Relationship between ESG and Corporate Financial Performance - Logistic Regression Model with Evidence from China (August 1, 2021). Available at SSRN: https://ssrn.com/abstract=3897207 or http://dx.doi.org/10.2139/ssrn.3897207

Lewis Tian (Contact Author)

Duke Kunshan University ( email )

No. 8 Duke Avenue
Kunshan, Jiangsu 215316
China

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