Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China
المؤلفون المشاركون
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-21
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
By combining the similarity matching (SM) method with the utilities additives discriminates (UTADIS) method, we propose a hybrid SM-UTADIS approach to detect falsified financial statements (FFS) of listed companies.
To evaluate the performance of this hybrid approach, we conduct experiments using the annual financial ratios of listed traditional Chinese medicine (TCM) companies in China.
There are three stages in the detection procedure.
First, we use the cosine similarity matching method to select matched companies for each considered company, derive the deviation data of each considered company as a sample dataset to capture the intrinsic law of the financial data, and further divide these into training and testing datasets for the next two stages.
Second, we put the training dataset into the UTADIS to train the SM-UTADIS model.
Finally, we use the trained SM-UTADIS model to classify the testing dataset and evaluate the performance of the proposed method.
Furthermore, we use other approaches, such as single UTADIS and logistic and SM-logistic regression models, to detect FFS.
By comparing these results to those of the hybrid SM-UTADIS approach, we find that the proposed hybrid approach greatly improves the accuracy of FFS detection.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yang, Ruicheng& Jiang, Qi. 2020. Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1153561
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yang, Ruicheng& Jiang, Qi. Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1153561
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yang, Ruicheng& Jiang, Qi. Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1153561
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153561
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر