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All Journal International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)
Mahmoud Elsayed Mahmoud
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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.
ASSESSING THE IMPACT OF THE FLOATING EXCHANGE RATE REGIME: DYNAMICS BETWEEN EXCHANGE RATES AND STOCK PRICES IN THE EGYPTIAN MARKET Mahmoud Elsayed Mahmoud; Doddy Setiawan; Yanni Aryani
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 2 No. 4 (2024): August
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v2i4.228

Abstract

The study aims to measure the effect of liberalizing the exchange rate and the transition from the fixed exchange rate to the flexible exchange rate on both the exchange rate stock prices and to measure the correlation between both the exchange rate and stock prices on the Egyptian stock, exchange before and after Floating of the exchange rate during the period 2019 and 2023, the variable under the study represent the exchange rate of the Egyptian pound against the us dollar and the EGX30 before and after the Floating of the exchange rate , 2 independent sample both the Mann Whitney test and the spearman’s correlation coefficient were used before and the after Floating of the exchange rate. The analysis revealed a significant impact on both the exchange rate and stock prices subsequent to the floatation of the exchange rate. Additionally, noteworthy significant correlations were observed between the exchange rate and stock prices, both pre and post the exchange rate floatation. These findings emphasize the influential relationship between the exchange rate and stock prices, indicating a notable effect of the exchange rate changes on the stock market dynamics both before and after the exchange rate was floated.
SYSTEMATIC LITERATURE REVIEW OF ADVANCEMENTS IN CORPORATE BANKRUPTCY PREDICTION Mahmoud Elsayed Mahmoud; Taufiq Arifin
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 2 No. 6 (2024): December
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v2i6.283

Abstract

This systematic review examines the evolution of corporate bankruptcy prediction models, synthesizing insights from a wide array of high-quality studies. Statistical methods, notably logit analysis and discriminant analysis, are predominant in bankruptcy prediction, but there is a discernible rise in the adoption of artificial intelligence techniques. Accounting-based methodologies, particularly accrual-based approaches, are prevalent, emphasizing the importance of financial ratios in assessing companies' financial health. By elucidating key trends and methodologies, this review aims to inform future research and enhance the effectiveness of bankruptcy prediction models in corporate finance.
Co-Authors Doddy Setiawan Neng dilla Aprilianto Taufiq Arifin Yanni Aryani