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

Hyeongjun Kim, Hoon Cho and Doojin Ryu ()
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Hyeongjun Kim: Yeungnam University
Hoon Cho: Korea Advanced Institute of Science and Technology
Doojin Ryu: Sungkyunkwan University

Computational Economics, 2022, vol. 59, issue 3, No 14, 1249 pages

Abstract: Abstract 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.

Keywords: Bankruptcy prediction; Classification; Long short-term memory; Machine learning; Recurrent neural network; G12; G17; G33 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10614-021-10126-5

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