Corporate Risk Disclosure Practices and Their Impact on Investor Decision Making (gjstudies, 2025)
This research introduces a novel computational framework for analyzing corporate risk disclosure practices and their impact on investor decision-making, movingbeyond traditional content analysis by integrating natural language processing, network theory, and behavioral finance simulations. While existing literature examinesdisclosure content and market reactions, our approach uniquely models the structural and semantic properties of risk disclosures as complex information networks,where individual risk factors are nodes and their co-occurrence patterns create edgeswith varying weights. We develop a proprietary corpus of 10-K filings from SP 500companies (2018-2023) and apply a hybrid methodology combining transformerbased semantic embedding (BERT) with graph convolutional networks to extractlatent risk interdependencies that are not apparent through manual reading orkeyword counting. Our findings reveal that the network topology of risk disclosures—specifically, measures of centrality, clustering, and path length between riskconcepts—significantly predicts investor attention allocation, as measured by eyetracking experiments with professional investors, and subsequent trading behaviorin simulated markets. We identify a ’disclosure complexity paradox’: firms withmore interconnected and densely clustered risk narratives experience lower investorcomprehension but higher perceived managerial competence, leading to asymmetric market reactions. Furthermore, we demonstrate that machine learning models trained on network features outperform traditional sentiment and readabilitymetrics in forecasting abnormal returns following disclosure events. This researchcontributes to the accounting, finance, and information science literatures by providing a new theoretical lens—the network theory of risk communication—and anoriginal analytical toolkit for assessing the informational quality and economic consequences of corporate transparency. The implications extend to regulatory policy,suggesting that standard setters should consider mandating not just the presence ofrisk factors, but also guidelines for their structural presentation to optimize investordecision-making