We use three supervised machine learning methods, namely linear discriminant analysis,
quadratic discriminant analysis, and random forest, to predict corporate financial …

Predicting bank performance is important for investors and regulatory authorities. Previous
research on non-financial firms has shown that augmenting the numeric information in the …

The existing literature on corporate disclosures suggests that managerial sentiments are the
outcome of the firm's financial performance. We extend this line of research and examine …

This study reviews the research on the usefulness, determinants, and measurements of the
textual tone of corporate financial disclosures. This review suggests that various …

Purpose The purpose of this paper is to give a comprehensive review and synthesis of
automated textual analysis of corporate disclosure to show how the accuracy of disclosure …

We compare the ability of dictionary-based and machine-learning methods to capture
disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald …

Purpose This study aims to determine the association of Transparency and Disclosure (TD)
with financial distress (FID) while the competition (as Lerner Index) moderates the …

We investigate whether narrative disclosures in 10-K and 10K-405 filings contain value-
relevant information for predicting market performance. We apply text classification …

We argue that volatility in a manager's disclosure tone across time should be a function of
two components:(i) the firm's innate operating risk and (ii) the extent to which the manager's …

This study examines whether bank risk disclosures are associated with operating
performance and market valuation. Due to the inherent opaqueness of banking operations …