Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data

• Published in: Computational economics Vol. 59; no. 3; pp. 1231 - 1249
• Main Authors: Kim, Hyeongjun, Cho, Hoon, Ryu, Doojin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Bankruptcy; Behavioral/Experimental Economics; Classification; Computer Appl. in Social and Behavioral Sciences; Credit risk; Discriminant analysis; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Machine learning; Macroeconomics; Math Applications in Computer Science; Methods; Neural networks; Neurons; Operations Research/Decision Theory; Performance indicators; Recurrent; Recurrent neural networks; Research methodology; Short term memory; Statistical analysis; Support vector machines; Variables; Recurrent neural network; G33; Classification; Machine learning; Bankruptcy prediction; Long short-term memory; G12; G17

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 o...