This research introduces for the first time, to the best of our knowledge, the concept of multimodal learning in bankruptcy prediction models. We use the Conditional Multimodal Discriminative (CMMD) model to learn multimodal representations that embed information from accounting, market, and textual modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions. This fact makes the use of bankruptcy prediction models using textual data realistic and possible, since accounting and market data are available for all companies unlike textual data. The empirical results in this research show that the classification performance of our proposed methodology is superior compared to that of a large number of traditional classifier models. We also show that our proposed methodology solves the limitation of previous bankruptcy models using textual data, as they can only make predictions for a small proportion of companies. Finally, based on multimodal representations, we introduce an index that is able to capture the uncertainty of the financial situation of companies during periods of financial distress.

翻译：本研究首次（据我们所知）将多模态学习概念引入破产预测模型。我们采用条件多模态判别（CMMD）模型学习多模态表征，该表征融合了会计、市场及文本模态的信息。CMMD模型需要包含所有数据模态的样本进行模型训练。在测试阶段，该模型仅需获取会计和市场模态数据即可生成多模态表征，并进一步用于破产预测。这一特性使得基于文本数据的破产预测模型在实际中成为可能——因为与文本数据不同，所有企业均可获取会计和市场数据。实证结果表明，与大量传统分类模型相比，本文提出的方法在分类性能上具有显著优势。同时，我们证实该方法解决了此前基于文本数据的破产模型仅能对少数企业进行预测的局限性。最后，基于多模态表征，我们构建了一个能够在财务困境期间捕捉企业财务状况不确定性的指数。