Corporate Bankruptcy Prediction with Domain-Adapted BERT

Abstract

This study performs BERT-based analysis, which is a representative contextualized language model, on corporate disclosure data to predict impending bankruptcies. Prior literature on bankruptcy prediction mainly focuses on developing more sophisticated prediction methodologies with financial variables. However, in our study, we focus on improving the quality of input dataset. Specifically, we employ BERT model to perform sentiment analysis on MD&A disclosures. We show that BERT outperforms dictionary-based predictions and Word2Vec-based predictions under time-discrete logistic hazard model, k-nearest neighbor (kNN-5), and linear kernel support vector machine (SVM). Further, instead of pretraining the BERT model from scratch, we apply self-learning with confidence-based filtering to corporate disclosure data. We achieve the accuracy rate of 91.56% and demonstrate that the domain adaptation procedure brings a significant improvement in prediction accuracy.

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