Linguistic complexity consideration for advanced risk decision making and handling

Abstract

This study presents a fusion architecture to predict enterprises’ operating efficiency via linguistic cues from the accounting narrative disclosures. We specifically apply topic modelling to decompose annual reports into some topics, preserve those topics that relate to business operation risks (including financial, operational, strategic, and hazard), and then incorporate the preserved topics with a readability score (i.e., linguistic cues) to conjecture managers’ attitude toward their firm’s risks. After examining the association between those four risk types derived from annual reports and operating efficiency calculated by a two-step integration model (i.e., it combines balanced scorecards (BSCs) with data envelopment analysis (DEA)), the results show that the model with linguistic cues does improve forecasting quality. This finding provides deep insight for users to form better judgments, and directly supports the recent reporting regulation in Taiwan that encourages enterprises to add a new section in their annual reports to disclose any risk encountered.