Decoding Digital Risk From Corporate Disclosure: A Neural Network Approach

Digital risk —or the likelihood of losses from key digital activities (i.e., information system IS sourcing, digital infrastructure, data management, IS applications, IS use, and digital product offerings)—constitutes a key consideration in firm valuation. Firms’ public disclosures (e.g., 10-K reports, earnings conference calls) are a key source of data to learn about digital risks. Although text analytics approaches (e.g., word frequency, topic modeling, and sentiment analysis) have been applied to a firm's public disclosures to assess various types of risk (e.g., political risk, tax risk, cybersecurity), they do not consider the structural linguistic relations embedded in the text that are potentially relevant in measuring risk. We apply a neural network approach to address this gap and extract linguistic relations from a firm's 10-K disclosure (Section “Item 1A”). We develop novel firm-level digital risk measures based on these linguistic relations. Specifically, we measure firm-level digital risk from three perspectives: (1) presence (whether digital risk is mentioned or not), (2) intensity (text coverage of digital risk relative to other issues), and (3) diversity (the types of digital risk mentioned). We validate our digital risk measures by demonstrating their significant correlation with firm risk, proxied by stock market volatility. Our research reveals that investors’ perceptions of digital risk diversity and digital risk intensity differ between IT and non-IT companies. First, across all firms, digital risk intensity is negatively associated with firm risk, indicating that investors do not incorporate intensity of digital risk when assessing firm risk. Second, in non-IT firms, digital risk diversity is positively associated with firm risk, suggesting that managers in these firms may influence investor perceptions through strategic disclosure of digital risk types. Overall, our findings suggest that text-based digital risk measurement is practically feasible, scalable, and economically meaningful.