Advances in Economics, Management and Political Sciences
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Proceedings of the 2023 International Conference on Management Research and Economic Development
Series Vol. 20 , 13 September 2023
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Predicting Fraud in U.S.-Listed Chinese Companies: An Empirical Analysis based on M-Score and F-Score Models
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Abstract
M-Score and F-Score are financial fraud prediction models that are commonly used in Western capital markets. In this paper, we calculate M-Scores and F-Scores for each dataset of 15 fraudulent Chinese companies (including Hong Kong companies) listed in the U.S. from 2006 to 2020 and compare their M-Scores and F-Scores with 15 companies in the same industry that are not fraudulent. Firstly, we used Student's t-test on data from both models respectively. The result shows that both of them are able to predict fraud well for Chinese stocks listed in the U.S. With the result, we further compare and test the differences between the financial fraudulent samples and the comparison samples in the dimensions of each variable in the M-Score and F-Score models. Finally, we draw a result that they are effective in predicting whether a Chinese company committed financial fraud.
Keywords
M-Score, F-Score, U.S.-listed Chinese companies, predictive models, financial fraud
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-915371-83-6
- ISBN (Online)
- 978-1-915371-84-3
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/20/20230177
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited