We examine whether corporate bankruptcy predictions can be improved by utilizing the recurrent neural network (RNN) and long short-term memory (LSTM) algorithms, which can process sequential data. Employing the RNN and LSTM methodologies improves bankruptcy prediction performance relative to using other classification techniques, such as logistic regression, support vector machine, and random forest methods. Because performance indicators, such as sensitivity and specificity, differ depending on the methodology, selecting a model that suits the purpose of the bankruptcy predictions is necessary. Our ensemble model, a synthesis of all methodologies, exhibits the best forecasting performance. In the test sample for the ensemble model, none of the observations with a default probability of less than 10% defaults within one year.

我们研究了利用循环神经网络（RNN）和长短期记忆（LSTM）算法（可以处理顺序数据）是否可以改善公司破产的预测。与使用其他分类技术（例如逻辑回归，支持向量机和随机森林方法）相比，采用RNN和LSTM方法可提高破产预测的性能。由于性能指标（例如敏感性和特异性）根据方法的不同而不同，因此有必要选择适合破产预测目的的模型。我们的综合模型是所有方法的综合，具有最佳的预测性能。在集成模型的测试样本中，一年内没有违约概率小于10％的观察值均不违约。