Identifying Financially Material Content in Corporate Social Responsibility Reports

Abstract

This study examines the relationship between financially material content in corporate social responsibility (CSR) reports and the decision usefulness of these reports. Utilizing sustainability disclosure standards and a machine learning topic modeling algorithm, a firm-specific quantitative measure of financially material content in CSR reports is developed. It is hypothesized that firms providing greater amounts of financially material CSR content enhance their information environment, which enables analysts to make more accurate earnings predictions. The findings confirm a positive relationship between the extent of financially material CSR content disclosed and analyst forecast accuracy. This research demonstrates the effectiveness of using machine learning to identify financially material content within unstructured voluntary disclosures and contributes to the literature on the financial materiality of CSR activities and their related disclosures.