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.
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