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A study on the analysis of stability indicators in financial statements using Fuzzy C-Means clustering

Authors
Kim, M.S.Choi, E.S.Lee, J.Y.Kang, M.S.
Issue Date
2017
Publisher
Research India Publications
Keywords
Clustering; Fuzzy C-Means; Machine learning; Stability indicators
Citation
International Journal of Applied Engineering Research, v.12, no.20, pp 9863 - 9865
Pages
3
Journal Title
International Journal of Applied Engineering Research
Volume
12
Number
20
Start Page
9863
End Page
9865
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/6090
ISSN
0973-4562
Abstract
IoT and A.I researches are active as the 4th industrial revolution comes. Innovative companies have already applied artificial intelligence to some functions. In addition, there will be an increasing number of companies that apply artificial intelligence to business analysis, and in the future, business management such as management and management finance experts will be replaced by artificial intelligence. In this paper, we selected 334 corporations which are corporations with total assets of 12 billion more out of the list specified by Korea Information & Communication Construction Association. We calculated the debt ratio, current ratio, BIS ratio from financial statement. These three things are stability indices. The debt ratio, the current ratio, and BIS ratio, which are key business analysis indicators of the stability indices, are clustered and analyzed by applying the Fuzzy C-Means clustering algorithm based on Fuzzy theory. As a result of clustering, we could see that it was divided into a certain section. © Research India Publications.
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