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        <title>Corporate Bankruptcy Prediction with Domain-Adapted BERT</title>
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    <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&amp;A disclosures. We show that BERT outperforms dictionary-based predictions and Word2Vec-based predictions in terms of adjusted R-square in logistic regression, k-nearest neighbor (kNN-5), and linear kernel support vector machine (SVM). Further, instead of pre-training the BERT model from scratch, we apply self-learning with confidence-based filtering to corporate disclosure data (10-K). We achieve the accuracy rate of 91.56% and demonstrate that the domain adaptation procedure brings a significant improvement in prediction accuracy.</abstract>
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    <identifier type="doi">10.18653/v1/2021.econlp-1.4</identifier>
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%0 Conference Proceedings
%T Corporate Bankruptcy Prediction with Domain-Adapted BERT
%A Kim, Alex Gunwoo
%A Yoon, Sangwon
%Y Hahn, Udo
%Y Hoste, Veronique
%Y Stent, Amanda
%S Proceedings of the Third Workshop on Economics and Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kim-yoon-2021-corporate
%X 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 in terms of adjusted R-square in logistic regression, k-nearest neighbor (kNN-5), and linear kernel support vector machine (SVM). Further, instead of pre-training the BERT model from scratch, we apply self-learning with confidence-based filtering to corporate disclosure data (10-K). We achieve the accuracy rate of 91.56% and demonstrate that the domain adaptation procedure brings a significant improvement in prediction accuracy.
%R 10.18653/v1/2021.econlp-1.4
%U https://aclanthology.org/2021.econlp-1.4
%U https://doi.org/10.18653/v1/2021.econlp-1.4
%P 26-36

Markdown (Informal)

[Corporate Bankruptcy Prediction with Domain-Adapted BERT](https://aclanthology.org/2021.econlp-1.4) (Kim & Yoon, ECONLP 2021)

• Corporate Bankruptcy Prediction with Domain-Adapted BERT (Kim & Yoon, ECONLP 2021)

ACL

• Alex Gunwoo Kim and Sangwon Yoon. 2021. Corporate Bankruptcy Prediction with Domain-Adapted BERT. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 26–36, Punta Cana, Dominican Republic. Association for Computational Linguistics.