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The dataset used in Prediction of Financial Restatement by Managerial Tone: Using Machine Learning Approach. A total of 1,893 financial restatement observations were identified from the Audit Analytics database and matched with firm-level data from Compustat for the period 2010 to 2022. The earning conference call transcripts are obtained from StreetEvent. Consistent with Zhang et al. (2025), only the most recent restatement year for each firm was retained, resulting in a sample of 1,025 distinct restating firms. To examine the association between managerial tone and the likelihood of restatements, a matched sample of non-restating firms from the same time period was randomly selected. This process yielded a balanced panel comprising 2,050 unique firms. All continuous variables were subsequently winsorized at the 1st and 99th percentiles to mitigate the influence of extreme values.

This table reports the summary statistics of the key variables in the quasi-natural experiment based on mergers between financial institutional blockholders during 1995–2012. The sample comes from multiple sources. Firm-level financial data come from COMPUSTAT database. Corporate social responsibility data come from MSCI ESG KLD database. Institutional investor holdings data come from Thomson Reuters Institutional (13F) Holdings database. Analyst coverage data come from Institutional Brokers Estimate System (I\B\E\S). We require observations to satisfy the following criteria: (1) Book equity is positive; (2) Each firm should at least have 2-year consecutive observations; (3) Variables are available in all observations; (4) Firms are not in financial (SIC code 6000–6999) or utility (SIC codes 4900–4999) industries. Our sample includes 3,778 firm-years that meet these criteria during 1995–2012 when Thomson Reuters Institutional (13F) Holdings and KLD are available and firms can be matched to blockholder mergers. All continuous variables are winsorized at 1st and 99th percentiles to alleviate the potential disturbance from outliers.