Comprehensive Income and Financial Distress Prediction An Applied Study of Egyptian Listed Companies | ||
| مجلة الاسکندرية للبحوث المحاسبية | ||
| Article 14, Volume 9, Issue 3, September 2025, Pages 65-89 PDF (1.49 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/aljalexu.2025.455282 | ||
| Authors | ||
| Mohamed Ahmed Saleh* ; Yasmine Magdy Ragab | ||
| Accounting Assistant Professor Faculty of Management- Modern University for Technology and Information | ||
| Abstract | ||
| Purpose- This study investigates whether integrating comprehensive income (CI) variables into Altman’s Z-score models through artificial neural networks (ANNs) improves financial distress prediction for Egyptian listed firms. Design/methodology/approach- Using a sample of 83 Egyptian listed firms (581 firm-year observations) covering the period 2016–2022, we incorporated CI variables to Altman’s original (1968) and revised (1983) Z-score models. Findings- The proposed models improved the prediction of financial distress accuracy of Altman's models by 1.5% and 1.2%, respectively. Type I error rate is 2.45% and 3.35% lower for both Altman's models. Practical implications- The proposed distress prediction models are effective in evaluating credit risk for stakeholders, including banks and other financial organizations. Utilizing such algorithms, they might discern enterprises having an elevated danger of default in their lending judgments. Originality/value- This work contributes to the literature in different aspects. First, it provides the first empirical evidence in the Egyptian context for integrating CI variables with Altman’s Z-score models through ANN techniques. Second, it demonstrates the impact of economic volatility on companies’ performance in emerging markets. | ||
| Keywords | ||
| Bankruptcy؛ Financial failure؛ Other comprehensive income (OCI)؛ Artificial neural networks (ANN)؛ Altman’s Z- score; Egypt | ||
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