DYNAMIC LOGISTIC REGRESSION MODELING FOR BANKRUPTCY RISK PREDICTION IN UKRAINIAN BUILDING SECTOR
DOI:
https://doi.org/10.20535/kpisn.2026.1.350247Keywords:
bankruptcy prediction, logistic regression, dynamic modeling, financial ratios, L2 regularization, early-warning system, Ukrainian building sectorAbstract
Background. Financial distress and bankruptcy forecasting have become increasingly important in the context of post-war economic recovery and restructuring of Ukrainian industries. Firms in the building-and-construction materials sector operate under high uncertainty, where early detection of insolvency risk is crucial for maintaining financial stability. Logistic regression models, widely used in environmental and risk analytics, can be adapted to represent the nonlinear transition from solvency to bankruptcy as a probabilistic process.
Objective. The objective is to develop and evaluate both static and dynamic logistic regression models for predicting potential bankruptcy of a representative Ukrainian building-materials manufacturer. The dynamic extension is aimed at capturing temporal persistence in financial performance through lagged predictors.
Methods. A synthetic monthly dataset (5 years, 60 observations) is generated to simulate realistic financial ratios, including liquidity, leverage, profitability, efficiency, and interest coverage (solvency). The models are estimated in MATLAB using maximum-likelihood logistic regression with L2 regularization (ridge penalty) to retain correlated predictors. The dynamic model incorporated one-period lags of all financial ratios and the one-period-lagged response. Predictive performance is assessed by accuracy, precision, recall, F1-score, and the confusion matrix.
Results. The static logistic model achieved an average accuracy of approximately 89 %, but it missed two bankruptcy-risky months out of six ones. The dynamic model improved performance to 94 % accuracy, without missing a bankruptcy-risky month, but falsely labeling a non-risky month as bankruptcy-risky one. The signs of estimated coefficients are consistent with economic logic: higher leverage increases bankruptcy probability, whereas greater liquidity, profitability, efficiency, and solvency reduce it.
Conclusions. Dynamic L2-regularized logistic regression provides an interpretable and computationally efficient framework for early bankruptcy prediction in Ukrainian industrial firms. The inclusion of lagged financial indicators enhances predictive stability and timeliness, enabling practical early-warning applications.
References
P. du Jardin, “Bankruptcy prediction using terminal failure processes”, European Journal of Operational Research, 2015, vol. 242, iss. 1, pp. 286–303.
Available: https://doi.org/10.1016/j.ejor.2014.09.059
D. S. Nugroho and T. Dewayanto, “Application of statistics and artificial intelligence for corporate financial distress prediction models: a systematic literature review”, Journal of Modelling in Management, 2025, vol. 20, iss. 6, pp. 1999–2023.
Available: https://doi.org/10.1108/JM2-12-2024-0412
F. Fasano et al., “The dilemma of accuracy in bankruptcy prediction: a new approach using explainable AI techniques to predict corporate crises”, European Journal of Innovation Management, 2025, vol. 28, iss. 11, pp. 1–22.
Available: https://doi.org/10.1108/EJIM-06-2024-0633
E. Lyandres and A. Zhdanov, “Investment opportunities and bankruptcy prediction”, Journal of Financial Markets, 2013, vol. 16, iss. 3, pp. 439–476.
Available: https://doi.org/10.1016/j.finmar.2012.10.003
E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”. Journal of Finance, 1968, vol. 23, no. 4, pp. 589–609.
Available: https://doi.org/10.2307/2978933
J. A. Ohlson, “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research, 1980, vol. 18, no. 1, pp. 109–131.
Available: https://doi.org/10.2307/2490395
M. Hesse and T. Loy, “Unlocking bankruptcy clues: A novel sentence-based machine learning approach”, International Journal of Accounting Information Systems, 2025, vol. 56, Art. no. 100751. Available: https://doi.org/10.1016/j.accinf.2025.100751
X. Chen et al., “Multi-class financial distress prediction based on hybrid feature selection and improved stacking ensemble model”, Expert Systems with Applications, 2025, vol. 282, Art. no. 127832. Available: https://doi.org/10.1016/j.eswa.2025.127832
D. W. Hosmer Jr. et al., Applied Logistic Regression, 3rd Edition, Wiley, 2013.
Available: https://doi.org/10.1002/9781118548387
D. G. Kleinbaum and M. Klein, Logistic Regression. A Self-Learning Text, Springer New York, NY, 2010. Available: https://doi.org/10.1007/978-1-4419-1742-3
A. Magrini,“Bankruptcy risk prediction: A new approach based on compositional analysis of financial statements”, Big Data Research, 2025, vol. 41, Art. no. 100537.
Available: https://doi.org/10.1016/j.bdr.2025.100537
T. Hastie et al., The Elements of Statistical Learning, Springer Series in Statistics, Springer, New York, NY, 2009. Available: https://doi.org/10.1007/978-0-387-84858-7
C. M. Bishop, Pattern Recognition and Machine Learning, Springer New York, NY, 2006.
Available: https://link.springer.com/book/9780387310732
K. P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
Available: https://mitpress.mit.edu/9780262018029/machine-learning/
J. H. Friedman et al., “Regularization paths for generalized linear models via coordinate descent”, Journal of Statistical Software, 2010, vol. 33, iss. 1, pp. 1–22.
Available: https://doi.org/10.18637/jss.v033.i01
Tay et al., “Elastic net regularization paths for all generalized linear models”, Journal of Statistical Software, 2023, vol. 106, iss. 1, pp. 1–31.
Available: https://doi.org/10.18637/jss.v106.i01
M. Kuhn and K. Johnson, Applied Predictive Modeling, Springer New York, NY, 2013.
Available: https://doi.org/10.1007/978-1-4614-6849-3
G. James et al., An Introduction to Statistical Learning with Applications in Python, Springer Texts in Statistics, Springer Cham. Available: https://doi.org/10.1007/978-3-031-38747-0
J.-P. Danthine et al., “Chapter 22 – Financial Equilibrium with Differential Information”, In: J.-P. Danthine, J. B. Donaldson, and S. Danthine (Eds.), Intermediate Financial Theory (Fourth Edition), Academic Press, 2025, pp. 641–657.
Available: https://doi.org/10.1016/B978-0-443-28902-6.00022-1
S. V. Merinova and V. V. Romanuke, “Perspectives of blockchain technology in business and management: advantages and challenges”, Systems and Technologies, 2025, vol. 69, no. 1, pp. 138–144. Available: https://doi.org/10.32782/2521-6643-2025-1-69.17
A. Sutrisno and R. A. Hamka, “Development and stabilization in small open economies”, Journal of Enterprising Communities: People and Places in the Global Economy, 2025, vol. 19, iss. 3, pp. 700–701. Available: https://doi.org/10.1108/JEC-06-2025-272
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