DOI: https://doi.org/10.36719/2663-4619/123/69-75
Rafail Bayramli
Akdeniz University
PhD student
https://orcid.org/0000-0002-6186-8714
rafael.bayramli.98@gmail.com
Financial Failure Prediction Models
Abstract
The stable economic and social development of modern states hinges on uninterrupted financial inflows, operational profitability, and sustained investment. Within this framework, the timely analysis and prediction of corporate bankruptcy emerge as pivotal mechanisms for identifying internal vulnerabilities and enabling pre-emptive interventions. This study examines classical bankruptcy prediction models—ranging from Beaver’s univariate ratio tracking and Altman’s Z-Score discriminant analysis to Ohlson’s logistic regression, Springate’s simplified MDA, Zmijewski’s probit-based probability estimation, and the market-augmented CHS framework. By synthesizing their methodologies, strengths, and contextual limitations, the research underscores their collective role in transforming financial diagnostics into proactive economic safeguards. Bankruptcy is revealed not as an abrupt event but as a detectable, progressive decline—affording critical windows for recovery if early signals are heeded. The integration of accounting-based and market-driven indicators, tailored to local economic realities, is proposed as essential for enhancing predictive accuracy and supporting macroeconomic resilience.
Keywords: bankruptcy prediction, financial distress, Altman Z-Score, Beaver model, Ohlson O-Score, Springate model, Zmijewski model, CHS model, financial ratios, early warning systems, economic stability, corporate failure, discriminant analysis, probit regression, market-based indicators