{"ID":2871513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10802","arxiv_id":"2509.10802","title":"Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction","abstract":"In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component's value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling.","short_abstract":"In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-m...","url_abs":"https://arxiv.org/abs/2509.10802","url_pdf":"https://arxiv.org/pdf/2509.10802v1","authors":"[\"Yi Lu\",\"Aifan Ling\",\"Chaoqun Wang\",\"Yaxin Xu\"]","published":"2025-09-13T03:42:34Z","proceeding":"q-fin.RM","tasks":"[\"q-fin.RM\",\"cs.CL\",\"cs.LG\",\"q-fin.CP\"]","methods":"[]","has_code":false}