Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr 12:2022:1086945.
doi: 10.1155/2022/1086945. eCollection 2022.

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Bingxiang Li  1 , Rui Tao  1 , Meng Li  1
Affiliations

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Bingxiang Li et al. Comput Intell Neurosci. .

Abstract

In order to solve the problem that corporate financial risks seriously affect the healthy development of enterprises, credit institutions, securities investors, and even the whole of China, the K-means clustering algorithm, the risk screening process, and the Gaussian mixture clustering algorithm, the risk screening process, are proposed; experiments have shown that although the number of high-risk companies selected by the K-means algorithm is small, only 9% of the full sample, the high-risk cluster can contain nearly 30% of the new "special treatment" companies. If the time period is extended to the next 5 years, this proportion will be higher. Finally we found that if the prediction of "special handling" events is used as the criterion for evaluating high-risk clusters, then K-means clustering can effectively screen out those risky companies that need to be treated with caution by investors. The validity of the experiment is verified.

PubMed Disclaimer

Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Identification of corporate financial risks.
Figure 2
Figure 2
Histogram of return on equity of listed companies in 2016.
Figure 3
Figure 3
Histogram of return on equity of listed companies in 2017.
Figure 4
Figure 4
Schematic diagram of the results of the K-means clustering algorithm.
Figure 5
Figure 5
Schematic diagram of P-R curve.
Figure 6
Figure 6
The total number of listed companies from 1997 to 2018 and the number of companies listed on ST in the following year.
Figure 7
Figure 7
Schematic diagram of K-means clustering after PCA dimensionality reduction.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Gong Y., Lin S., He F., He Y., Song J. Damage identification of prefabricated reinforced concrete box culvert based on improved fuzzy clustering algorithm and acoustic emission parameters. Advances in Materials Science and Engineering . 2021;2021(7):1–13. doi: 10.1155/2021/6660915. - DOI
    1. Brunner-Kirchmair T. M., Wiener M. Knowledge is power-conceptualizing collaborative financial risk assessment. The Journal of Risk Finance . 2019;20(3):226–248. doi: 10.1108/jrf-05-2018-0083. - DOI
    1. Perez-Sanchez I., Cervantes B., Medina-Perez M. A., Monroy R., Herrera F. An indexing algorithm based on clustering of minutia cylinder codes for fast latent fingerprint identification. IEEE Access . 2021;9(99):p. 1. doi: 10.1109/access.2021.3088314. - DOI
    1. Li J. False financial statement identification based on fuzzy c-means algorithm. Complexity . 2021;2021(1):1–11. doi: 10.1155/2021/5522894. - DOI
    1. Moskvichenko I., Krysyuk L., Chebanova T. Influence of financial instability on exchange risks of port sector enterprises. Economic Analysis . 2019;29(1):85–91. doi: 10.35774/econa2019.01.085. - DOI

LinkOut - more resources