Deep learning models for bankruptcy prediction using textual disclosures

Feng Mai, Shaonan Tian, Chihoon Lee, Ling Ma

Research output: Contribution to journalArticlepeer-review

258 Scopus citations

Abstract

This study introduces deep learning models for corporate bankruptcy forecasting using textual disclosures. Although textual data are common, it is rarely considered in the financial decision support models. Deep learning uses layers of neural networks to extract features from textual data for prediction. We construct a comprehensive bankruptcy database of 11,827 U.S. public companies and show that deep learning models yield superior prediction performance in forecasting bankruptcy using textual disclosures. When textual data are used in conjunction with traditional accounting-based ratio and market-based variables, deep learning models can further improve the prediction accuracy. We also investigate the effectiveness of two deep learning architectures. Interestingly, our empirical results show that simpler models such as averaging embedding are more effective than convolutional neural networks. Our results provide the first large-sample evidence for the predictive power of textual disclosures.

Original languageEnglish
Pages (from-to)743-758
Number of pages16
JournalEuropean Journal of Operational Research
Volume274
Issue number2
DOIs
StatePublished - 16 Apr 2019

Keywords

  • Bankruptcy prediction
  • Decision support systems
  • Deep learning
  • Machine learning
  • Textual data

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