Leveraging Multiview Data Through Discrete and Regularized Deep Learning for Dynamic Financial Risk Prediction

• 成果类型: Article; Early Access
• 署名作者: Wang, Zhao; Che, Wanliu; Jiang, Cuiqing; Zhao, Huimin
• 署名单位: Hefei University of Technology; University of Wisconsin System; University of Wisconsin Milwaukee
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2024.1417
• 发表日期: 2026-03-17
• 关键词: dynamic financial risk prediction; multiview data; Deep learning; discrete representation learning; MONOTONICITY; Conflict resolution; MODEL

摘要

The dramatic surge of demand for predictive insights into the dynamics of financial risk and the rich, yet entangled, information brought by proliferating multiview data have spawned a new research problem, multiview data-based dynamic financial risk prediction (DFRP). Although previous studies have provided diverse methods for financial risk prediction, how to effectively extract information from entangled multiview data and accurately predict the financial risk of a company over time remains challenging. To better leverage multiview data for DFRP, we take a general risk event-time modeling approach and propose a discrete and regularized deep learning (DRDL) method. We design a disentangled multiview learning module, inspired by the chunking theory, to learn a discrete and disentangled representation using a tailored discrete multiview recoder and extract complementary information using a tailored mask-based view fusion decoder. We also design a monotonicity-aware multiperiod risk prediction module to guarantee time-wise monotonicity using a tailored risk accumulation function, accommodate instance-wise monotonicity using a tailored focal survival loss, and avoid conflicts between these two objectives using a tailored adaptive gradient balancing method. We have evaluated DRDL on two types of companies across three markets in China and the United States, using yearly, quarterly, and monthly prediction windows, with three specific types of financial risk (i.e., fundamental distress, regulatory listing risk, and market tail risk). Evaluation at the model level, in terms of time-to-risk prediction performance and out-of-time prediction performance, and impact analysis at the application level, in terms of identification performance and profitability performance, demonstrate advantages of DRDL over benchmarked classic and state-of-the-art methods on all fronts. Mechanism-level analyses further reveal the core drivers underlying the utility of DRDL.