Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

Analyst Coverage,Forecasting Bias,and Corporate Innovation:Evidence from China

Due to information asymmetry and strategic innovation,firms often encounter challenges related to insufficient driving forces and low-quality innovation outcomes.Analysts always act as information intermediaries who help foster the advancement of corporate innovation activities and the conversion of innovation output.This study examines the impact of analyst coverage and forecasting bias on corporate innovation,employing data from China A-shared listed firms spanning the period 2007 to 2019.We measure corporate innovation from two perspectives:Input and output.Specifically,we use the ratio of research and development(R&D)expenditure to sales as a proxy for the innovation input and the number of patent citations excluding self-citations to measure innovation output.We find that analyst coverage promotes corporate innovation,which is consistent with the“bright”side of analyst coverage.However,the positive effect of analyst coverage hinges on effectively transmitting and disclosing accurate information to investors in the capital market.Based on this,analysts'forecasting bias includes forecasting dispersion and optimism bias.We find evidence that an increase in analysts'forecast dispersion leads to a decrease in corporate innovation quality.Moreover,this paper presents a novel approach by employing the regression discontinuity method to examine the effect of analyst optimistic bias on firm innovation.The empirical findings reveal that overly optimistic forecasts by analysts exacerbate innovation quality.These analyses enrich the research on analyst coverage and corporate innovation,providing an empirical basis for improving the capital market with the help of analysts.