Early warning systems for financial distress: A machine learning approach to corporate risk mitigation
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Abstract
This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.
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How to Cite
Judijanto, L., Sihotang, J., & Simbolon, A. P. H. (2024). Early warning systems for financial distress: A machine learning approach to corporate risk mitigation. International Journal of Basic and Applied Science, 13(1), 14–27. https://doi.org/10.35335/ijobas.v13i1.470
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X. Li et al., “Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond,” Knowl. Inf. Syst., vol. 64, no. 12, pp. 3197–3234, 2022, doi: https://doi.org/10.1007/s10115-022-01756-8.
H. Aljawazneh, A. M. Mora, P. García-Sánchez, and P. A. Castillo-Valdivieso, “Comparing the performance of deep learning methods to predict companies’ financial failure,” in IEEE Access, IEEE, 2021, pp. 97010–97038. doi: https://doi.org/10.1109/ACCESS.2021.3093461.
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R. Hammad, Z. Khan, F. Safieddine, and A. Ahmed, “A review of learning theories and models underpinning technology-enhanced learning artefacts,” World J. Sci. Technol. Sustain. Dev., vol. 17, no. 4, pp. 341–354, 2020, doi: https://doi.org/10.1108/WJSTSD-06-2020-0062.
T. Iyamu, “Underpinning theories: Order‐of‐use in information systems research,” J. Syst. Inf. Technol., vol. 15, no. 3, pp. 224–238, 2013, doi: https://doi.org/10.1108/JSIT-11-2012-0064.
H. Kallio, A. Pietilä, M. Johnson, and M. Kangasniemi, “Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide,” J. Adv. Nurs., vol. 72, no. 12, pp. 2954–2965, 2016, doi: https://doi.org/10.1111/jan.13031.
A. Van den Berg and M. Struwig, “Guidelines for researchers using an adapted consensual qualitative research approach in management research,” Electron. J. Bus. Res. Methods, vol. 15, no. 2, pp. pp109-119, 2017, [Online]. Available: https://academic-publishing.org/index.php/ejbrm/article/view/1361
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F. Barboza, H. Kimura, and E. Altman, “Machine learning models and bankruptcy prediction,” Expert Syst. Appl., vol. 83, no. 15, pp. 405–417, 2017, doi: https://doi.org/10.1016/j.eswa.2017.04.006.
E. I. Altman, M. Iwanicz‐Drozdowska, E. K. Laitinen, and A. Suvas, “Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z‐score model,” J. Int. Financ. Manag. Account., vol. 28, no. 2, pp. 131–171, 2017, doi: https://doi.org/10.1111/jifm.12053.
M. Boďa and V. Úradníček, “The portability of Altman’s Z-score model to predicting corporate financial distress of Slovak companies,” Technol. Econ. Dev. Econ., vol. 22, no. 4, pp. 532–553, 2016, doi: https://doi.org/10.3846/20294913.2016.1197165.
A. Pramudita, “The Application of Altman Revised Z-Score Four Variables and Ohlson O-Score as A Bankruptcy Prediction Tool in Small and Medium Enterprise Segments in Indonesia,” in 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020), Atlantis Press, 2021, pp. 132–135. doi: https://doi.org/10.2991/aebmr.k.210831.027.
P. Morales-Bañuelos, N. Muriel, and G. Fernández-Anaya, “A modified Black-Scholes-Merton model for option pricing,” Mathematics, vol. 10, no. 9, p. 1492, 2022, doi: https://doi.org/10.3390/math10091492.
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A. K. Karagozoglu, “Option Pricing Models: From Black-Scholes-Merton to Present.,” J. Deriv., vol. 29, no. 4, pp. 61–73, 2022, doi: 10.3905/jod.2022.1.158.
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