Sequential Modeling of Corporate Bankruptcy Risk Using Hidden Markov Model Augmented Transformers.

This study contributes a hybrid framework for bankruptcy risk prediction that integrates Hidden Markov Models to capture latent temporal financial dynamics with Transformer-based models for multivariate financial time-series classification. The study also provides a systematic comparison with classical machine learning baselines, demonstrating the potential of temporal representations for identifying bankruptcy risk under severe class imbalance.