K-means Algorithm And Its Application
For Clustering Companies Listed In
Zhejiang Province
Author(s)
Y. Qian
Abstract
There exist many problems in the credit market where we have data that needs to
be classified into distinct groups. This paper will introduce a financial K-means
algorithm, which based on the historical financial ratios, applies the cluster
analysis technology to analyze the listed enterprises in Zhejiang province. We
analyze indicators related to financial attributes and choose nine finance
indicators. According to better valuation on the companies listed, we apply \“trial
and error” and choose four as the number of clustering. Testing shows that
companies belong to cluster 2 and cluster 3 add up to 71 companies, including
87% in all. They are all companies worthy of making loans, which is inconsistent
with the good economic situation of Zhejiang province. Category 4 has nine
companies including 11% that are judged as high risk business. So banks should
provide these customers for loans with a mortgage or guarantee.
Keywords: K-means algorithm, clustering analysis, financial ratios, listed
companies.
1 Introduction
In credit market, banks want to analyze the customer’s preferences to make loan
decision, to offer loans and set loan rate, and to decide their market strategy, and
to provide customized guide to their potential customers [1].
In today’s information based society, there is an urge for bank managers have
only vague idea, to find the needed information from the overwhelming
resources, who is a good client and who is a bad client (whom to watch carefully
to minimize the bank loses. some company’s financial reports contain a lot of
Keywords
K-means algorithm, clustering analysis, financial ratios, listed
companies.
Related Book
Other papers in this volume
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