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All Journal International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)
Neng dilla Aprilianto
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Humanities Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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ENHANCING CORPORATE BANKRUPTCY PREDICTION MODELS: A COMPREHENSIVE ANALYSIS WITH EVIDENCE FROM THE EGYPTIAN STOCK MARKET Mahmoud Elsayed Mahmoud; Taufiq Arifin; Neng dilla Aprilianto
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 3 No. 3 (2025): June
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v3i3.357

Abstract

Accurately predicting corporate bankruptcy and financial failure is crucial for financial institutions, creditors, and other stakeholders engaged in credit transactions. This study investigates the effectiveness of accrual-based, cash flow-based, and hybrid models in predicting financial distress among companies listed on the Egyptian stock market. Utilizing Multiple Discriminant Analysis (MDA), the research develops three predictive models, each based on different sets of financial ratios. The cash flow-based model correctly classified 90.0% of original cases, while the accrual-based model demonstrated higher accuracy with a 96.7% classification rate. However, the hybrid model, which integrates both accrual and cash flow ratios, outperformed both, achieving a perfect 100% classification accuracy. These findings suggest that hybrid models provide superior predictive accuracy, offering a more comprehensive early warning system for bankruptcy. The study’s primary limitation is its small sample size, which may affect the generalizability of the results. Future research should consider expanding the dataset and including a more diverse range of companies to enhance the robustness and applicability of the findings.
Co-Authors Mahmoud Elsayed Mahmoud Taufiq Arifin