Greenwashing and analyst forecast accuracy
Prior studies reported that analysts use not only financial information but also nonfinancial information such as corporate social responsibility (CSR) information to make earnings forecasts. However, the regulation and assurance of CSR information disclosure are limited, raising concerns about the reliability of CSR information. This study examines the link between greenwashing and analyst forecast accuracy by using data from Chinese-listed firms from 2011 to 2019. The difference between a firm’s disclosure and performance of ESG is used to proxy a firm’s greenwashing behaviour. The results show a negative relationship between firms’ greenwashing and analyst forecast accuracy, suggesting the greater the firms’ greenwashing behaviour, the higher the analysts’ forecast errors. Accordingly, analysts fail to identify firms’ greenwashing behaviour.
To check the reliability of the baseline results, I conducted a series of robustness tests. The results of robustness tests support the baseline findings. To mitigate endogeneity issues, an instrumental variable (IV) analysis was conducted, and the baseline results remained unchanged.
Next, this study explores the underlying mechanisms linking greenwashing and analyst forecast accuracy. The results show that the relationship between greenwashing and analyst forecast accuracy is transmitted through the external monitoring and reputation channels. Further Cross-sectional analyses indicate that the relationship between greenwashing and analyst forecast accuracy is more pronounced for firms led by non-dual CEOs, with lower managerial ownership and less board gender diversity. In an additional analysis, I investigate the consequence of firms’ greenwashing behaviour on their performance. The results indicate that greenwashing will not affect the firm performance in the current year but adversely affects the firm performance in the following years.
This study makes the following contributions. First, this study extends the study of influencing factors that affect analyst forecast accuracy: firms’ greenwashing behaviour. Second, this study constructs metrics for measuring greenwashing, shedding light on identifying firms’ greenwashing behaviour. Third, this study has significant practical implications by showing that firms use greenwashing to manipulate investor perceptions. It is difficult for investors to identify firms’ greenwashing behaviour because of information asymmetry between investors and firms. Hence, firms can access capital at a lower cost by greenwashing. Finally, this study proposes practicable policy recommendations for preventing and monitoring the firms’ greenwashing behaviour and improving analyst forecast accuracy.
