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

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

“PERFORMANCE TREND” AND “PERFORMANCE CURRENT” RATINGS BY ECONOMIC VALUE ADDED (EVA)

The Economic Value Added (EVA) is an index of “durable development.” It was proposed by the Stern-Stewart Office and represents the true economic profit of companies. A company reports economic profit only if thereported accounting profit is higher than the opportunity cost of equity. The authors of the EVA model (Stern-Stewart) have also proposed the ways of restating the financial statements to get a “fair image” of the results, thuseliminating the “accounting distortions.”Based on the performance indexes calculated based on EVA and second-generation indexes relating toEVA, we calculated two types of rating: Rating Performance Current and Rating Performance Trend. The rating iscalculated by means of the percentiles technique and the results are split into 22 rating classes.The used database is Russell 3000

CLASSIFICATION OF COMPANIES FROM THE PERFORMANCE STANDPOINT USING WARD’S METHOD

In this paper I will try to apply an algorithm of hierarchic classification of the economic and financial performance for a group of companies in the chemical field, listed with the New York Stock Exchange (NYSE), a classification which will be performed taking into consideration the Return on Equity (ROE), Net income, Economic Value Added (EVA), EVA/Equity and Stock Price. The classification will be performed using the cluster analysis