Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-21
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153561