Using Kida Model in Predicting Financial Failure in Egyptian Firms
| المصدر: | مجلة البحوث في العلوم المالية والمحاسبية |
|---|---|
| الناشر: | جامعة محمد بوضياف المسيلة - كلية العلوم الاقتصادية والتجارية وعلوم التسيير |
| المؤلف الرئيسي: | Elsayed, Nehad Hosny Yusuf (Author) |
| مؤلفين آخرين: | Aelasmer, Sara Zakari (Co-Author) , Ebrahime, Rofida Samir (Co-Author) , Keedis, Mira Adel (Co-Author) , Mansour, Hager Moheb (Co-Author) |
| المجلد/العدد: | مج8, ع1 |
| محكمة: | نعم |
| الدولة: |
الجزائر |
| التاريخ الميلادي: |
2023
|
| الشهر: | جوان |
| الصفحات: | 696 - 711 |
| DOI: |
10.37169/1992-008-001-035 |
| ISSN: |
2543-3725 |
| رقم MD: | 1408550 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Kida Model | Financial Failure | Bankruptcy | Firm Performance | Egyptian Firms
|
| رابط المحتوى: |
عدد مرات التحميل
5
| المستخلص: |
Understanding the financial failure of firms is a vital accounting research endeavour. Because of the critical importance of financial failure to managers' decision-making, investors, shareholders, and others, many studies examined several developed models for predicting financial failure using Linear multiple discriminant approaches (LMDA), logistic regression, artificial intelligence approaches and data mining techniques (neural network (NN), genetic algorithm (GA), expert systems (ESs), and support vector machines (SVMs). In Egypt, there doesn't seem to be much similar work. This study aims to close this knowledge gap, especially after many financial consequences, including financial failure, have been experienced by a number of Egyptian-listed firms in recent years despite government and professional initiatives to reduce financial failure in Egypt. The current study uses the Kida model for predicting financial failure in Egyptian firms. According to the findings, the Kida model is deemed adequate for predicting Egyptian firms' financial failure, which was negative for most study years/firms. |
|---|---|
| ISSN: |
2543-3725 |
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