Corporate carbon information disclosure and asset mispricing
Description
Owing to the implementation of new accounting standards in China since 2007, we use Python software to capture the relevant disclosures in the social responsibility, sustainability, and environmental reports of China's A-share listed companies from 2007 to 2021 from the Shanghai and Shenzhen Stock Exchanges. We use text analysis technology and natural language processing (NLP) technology to assign scores and then use the weighted sum of indicator weights obtained by the analytic network process (ANP) to finally obtain the data of carbon information disclosure samples. Asset mispricing level data rely on the stock trading volume from the Wind database. Financial data are obtained from the CSMAR and CNRDS databases. Initial data are further excluded as follows: (1) samples from the financial industry, (2) special treatment samples, and (3) samples with missing data. In addition, to reduce the potential interference of outliers, all continuous variables are winsorized at the 1% level. Finally, this study obtained 6,048 firm-year observations.