CLUSTERING TECHNIQUES IN FINANCIAL DATA ANALYSIS APPLICATIONS ON THE U.S. FINANCIAL MARKET

Abstract

In the economic and financial analysis, the need to classify companies in terms of categories, thedelimitation of which has to be clear and natural occurs frequently. The differentiation of companies bycategories is performed according to the economic and financial indicators which are associated to the above.The clustering algorithms are a very powerful tool in identifying the classes of companies based on theinformation provided by the indicators associated to them. The last decade imposed to the economic andfinancial practice the use of economic value added as an indicator of synthesis of the entire activity of acompany. Our study uses a sample of 106 companies in four different fields of activity; each company isidentified by: Economic Value Added, Net Income, Current Sales, Equity and Stock Price. Using the ascendinghierarchical classification methods and the partitioning classification methods, as well as Ward’s method and kmeansalgorithm, we identified on the considered sample an information structure consisting of 5 rating classes.

Keywords

• ANOVA analysis
• clustering algorithms
• rating
• Economic Value Added (EVA)