We develop an empirical proxy for companies’ differential communication to local and foreign investors using translation differences in public disclosure. We validate our proxy using a field experiment and then use this proxy to document that differential communication is associated with increases in information asymmetry between local and foreign investors. It is also linked to decreases in the relative information quality of foreign analysts, even when foreign demand for information is high and communication costs are low. These and a variety of supporting tests, including those using an alternative artificial intelligence (AI)-based measure of translation differences, suggest that firms engage in differential communication because of a preference for local investors and when responding to incentives to maximize stock price. This study highlights the role of differential communication as one driver of local information advantage in our setting.
This study examines college students’ learning modality preferences during and after the pandemic. Results from a survey of 472 accounting students indicate a preference of a balanced mix of learning modalities: 35% face-to-face, 30% synchronous online, and 35% asynchronous online, on average. Preferences varied by student type, with freshmen and financial reporting students favoring more face-to-face instruction, while working students preferred more online instruction. Additionally, there was a significant post-pandemic shift, with students increasingly gravitating toward online learning and showing a decreased interest in synchronous online formats. These findings suggest that a flexible, multi-modal approach may be more effective for delivering accounting courses in the post-pandemic future.
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.