Abstract
Machine learning has been gradually introduced into corporate financial distress prediction and several prediction models have been developed. Financial distress affects the sustainability of a company’s operations and undermines the rights and interests of its stakeholders, also harming the national economy and society. Therefore, we developed an accurate predictive model for financial distress. Using 17 financial attributes obtained from the financial statements of Indonesia’s consumer cyclical companies, we developed a machine learning model for predicting financial distress using decision tree, logistic regression, LightGBM, and the k-nearest neighbor algorithms. The overall accuracy of the proposed model ranged from 0.60 to 0.87, which improved on using the one-year prior growth data of financial attributes.
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Martono, N.P., Ohwada, H. (2023). Financial Distress Model Prediction Using Machine Learning: A Case Study on Indonesia’s Consumers Cyclical Companies. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. https://doi.org/10.1007/978-3-031-23633-4_5
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DOI: https://doi.org/10.1007/978-3-031-23633-4_5
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