Predicting financial distress using multimodal data: An attentive and regularized deep learning method

The proliferation of multimodal data provides a valuable repository of information for financial distress prediction. However, the use of multimodal data faces critical challenges, such as heterogeneity within and among modalities and difficulties in discriminating complementary and redundant information among modalities. To this end, we propose an attentive and regularized deep learning method for predicting financial distress using multimodal data, including financial indicators, current reports, and interfirm networks. Specifically, considering heterogeneity within and among modalities, we design three modality-specific attentions, i.e., ratio-aware, reportaware, and neighbor-aware attentions, for adaptively extracting key information from financial indicators, current reports, and interfirm networks, respectively. Considering difficulties in discriminating complementary and redundant information among modalities, we design a conditional entropy-based regularization to guide the method focusing on complementary information while discarding redundant information during modality fusion. We also propose the use of focal loss to address the class imbalance problem. Empirical evaluation shows that the proposed method significantly outperformed all benchmarked methods in terms of predictive and representation performance. We also provide key findings and implications for stakeholders.