Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China

Joint Authors

Yang, Ruicheng
Jiang, Qi

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

Mathematics

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