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.

Corporate Bankruptcy Prediction with BERT Model



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W1: 3rd Workshop on Economics and Natural Language Processing (ECONLP)

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Alex Kim

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Corporate Bankruptcy Prediction with BERT Model

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