ESG Uncertainty, Investor Attention and Stock Price Crash Risk in China: Evidence from PVAR Model Analysis

Can ESG uncertainty influence the risk of stock price crashes?

Danni Yu, Tiantian Meng, Minyu Zheng, and Rongyi Ma explore the relationship between ESG uncertainty, investor attention, and stock price crash risk in China in their paper ESG uncertainty, investor attention and stock price crash risk in China: evidence from PVAR model analysis.

They use a Panel Vector Autoregression (PVAR) model based on data from Chinese listed companies between 2011 and 2021 to conclude:

• ESG uncertainty does not directly cause stock price crashes but significantly increases investor attention, influencing market behaviours indirectly.
• Increased investor attention initially reduces stock price crash risk by improving transparency and reducing information asymmetry, but heightened attention over time can amplify market volatility and eventually raise crash risk.
• Investor attention and ESG uncertainty are mutually reinforcing: higher ESG uncertainty boosts investor scrutiny, and heightened scrutiny further uncovers ESG uncertainties, intensifying market dynamics.
• Stock price crash risk and investor attention share a bidirectional relationship: higher crash risks attract investor attention, which subsequently elevates crash risk through market overreactions.
• ESG uncertainty primarily influences market behaviour indirectly, mediated through heightened investor attention rather than directly causing stock price volatility.

This article suggests investors could monitor sustainability reporting clarity to assess ESG uncertainty, as an indicator of potential market volatility to optimise risk management.

The study's focus on Chinese markets is a clear limitation, as it may limit broader generalisability. Additionally, the PVAR model, while robust, might not fully capture all potential causal relationships.

Future research could broaden the geographic scope, refine ESG measurement methods, and deepen analysis of investor behaviours using their proprietary scores to better model the market's behaviour.