Accounting Restatements and Their Impact on Corporate Reputation and Market Trust (gjstudies, 2025)
This research introduces a novel computational framework for analyzing the multidimensional impact of accounting restatements on corporate reputation and markettrust, moving beyond traditional event-study methodologies. We develop a hybridapproach combining natural language processing of unstructured financial disclosures,sentiment analysis of social media and news coverage, and network analysis of investorcommunication patterns to create a comprehensive reputation vulnerability index. Ourmethodology uniquely incorporates temporal dynamics, distinguishing between immediate market reactions and longer-term reputation erosion, while accounting for themoderating effects of corporate transparency initiatives and governance structures.We analyze a proprietary dataset of 450 restatement events from 2015-2023, applyingmachine learning techniques to identify previously unrecognized patterns in how different types of restatements propagate through stakeholder networks. The findings revealthat technical restatements with minimal financial impact can cause disproportionatereputation damage when accompanied by negative narrative framing, while materialerrors may be mitigated through proactive communication strategies. We demonstratethat market trust exhibits hysteresis effects, with recovery trajectories following nonlinear patterns influenced by industry context and prior corporate reputation capital.The research contributes a new analytical paradigm for understanding the complexinterplay between financial reporting reliability, information dissemination networks,and stakeholder perception formation, offering practical insights for corporate communication strategies and regulatory policy design.