ارسل ملاحظاتك

ارسل ملاحظاتك لنا







الرئيسية > The Effect of Using Machine Le...>مارك

The Effect of Using Machine Learning Algorithms Alternatives on the Prediction Accuracy of Going Concern Opinion

المصدر: المجلة العلمیة للدراسات والبحوث المالیة والإداریة
الناشر: جامعة مدينة السادات - كلية التجارة
المؤلف الرئيسي: Abd El Gawad, Samar Fathy (Author)
المجلد/العدد: مج15, عدد خاص
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2023
الشهر: سبتمبر
الصفحات: 1 - 24
ISSN: 2682-2113
رقم MD: 1438888
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
حوكمة الشركات | الأوراق المالية | الاحتيال المالي | الذكاء الاصطناعي
كلمات المؤلف المفتاحية:
Going Concern Prediction | Machine Learning | Support Vector Machine (SVM) | Decision Tree | Logistic Regression
رابط المحتوى:
PDF (صورة)      PDF (نص)      HTML
صورة الغلاف QR قانون

عدد مرات التحميل

4

حفظ في:
LEADER 02957nam a22002177a 4500
001 2185594
041 |a eng 
044 |b مصر 
100 |9 761832  |a Abd El Gawad, Samar Fathy   |e Author 
245 |a The Effect of Using Machine Learning Algorithms Alternatives on the Prediction Accuracy of Going Concern Opinion 
260 |b جامعة مدينة السادات - كلية التجارة  |c 2023  |g سبتمبر 
300 |a 1 - 24 
336 |a بحوث ومقالات  |b Article 
520 |b The purpose of this paper is to analyze and examine the effect of machine learning algorithms alternatives on the prediction accuracy of going concern opinion and which one is more effective in predicting the accuracy of going concern opinion. To achieve this purpose, the research will address the accuracy of going concern opinion from a professional view, determinants of the accuracy of going concern opinion, measurements of accuracy of going concern opinion, machine learning from a professional view, and analysis the effect of the machine learning algorithms on the accuracy of going concern opinion. In order to test the research hypotheses, the researcher will use the decision trees (DT), logistic regression, support vector machines (SVM). The sample used in the current study consists of 87 non-financial companies listed in Egyptian Stock Exchange during the period (2019-2021). The research concludes that SVM and Logistic regression has the highest accuracy to predict going concern doubts, where the accuracy rate is 86%, then the decision tree model doubts, where the accuracy rate is 79 %. In light of the research objectives and its problem, and the results it concluded, the research recommends that auditors should be interested in developing their skills to be able to use artificial intelligence, such as machine learning, in issuing audit opinion, as they face some difficulties in using artificial intelligence in the audit field. Regarding the proposed research areas, the most important of them are the following: (a) the effect of using data analytics on the prediction accuracy of going concern opinion, (b) The effect of artificial intelligence technologies on audit evidence, (c) The effect of machine learning on detecting misstatements on financial statements. 
653 |a حوكمة الشركات  |a الأوراق المالية  |a الاحتيال المالي  |a الذكاء الاصطناعي 
692 |b Going Concern Prediction  |b Machine Learning  |b Support Vector Machine (SVM)  |b Decision Tree  |b Logistic Regression 
773 |4 الاقتصاد  |4 الإدارة  |6 Economics  |6 Management  |c 004  |f Al-Mağallah Al-ʿilmiyyaẗ Lil Dirāsāt wa Al-Buḥṯ Al-Māliyyaẗ wa Al-’idāriyyaẗ  |l 999  |m مج15, عدد خاص  |o 2225  |s المجلة العلمیة للدراسات والبحوث المالیة والإداریة  |v 015  |x 2682-2113 
856 |u 2225-015-999-004.pdf 
930 |d y  |p y  |q y 
995 |a EcoLink 
999 |c 1438888  |d 1438888 

عناصر مشابهة


© 2024 المنظومة. جميع الحقوق محفوظة.