Non-financial information, especially information carried in disclosure reports, plays an important role in conveying financial distress signals. Considering the rise of generative AI (GenAI) and its potential in capturing both surface and latent meanings of disclosure reports, we initiate a new research avenue, GenAI-enhanced financial distress prediction. We position GenAI as an information intermediary and propose a functional analogy framework to conceptualize the process of leveraging disclosure reports with four functions: perception, extraction, reasoning, and evaluation. We then provide a guideline with three GenAI use strategies (i.e., prompt engineering, knowledge injection, and fine-tuning) and design a deep learning method featuring a function-based bidirectional representation module, which explicitly and separately extracts representations for the emphasis information produced by the extraction function and insight information produced by the reasoning function, guided by tailored convergent and divergent mutual information criteria, respectively. Empirical evaluation at the model level and impact analysis at the application level demonstrate advantages of the proposed method over benchmarked state-of-the-art methods on all fronts. Mechanism-level analyses further reveal the core drivers underlying the utility of the proposed method.
Wang et al. (Sat,) studied this question.