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International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605

Volume 9, Issue 2, March 2014 pp 50-55

Research Article

Assessing Indian Industries on the Basis of Financial Ratios Using Certain Data Mining Tools

R. Chandrasekaran (Retd.)¹, G. Manimannan², R. Lakshmi Priya³

{¹Associate Professor and Head (Retd.), ²Assistant Professor}

Department of Statistics, Madras Christian College, Chennai 600059, Tamil Nadu, INDIA.

Academic Editor: Dr. Dase R. K.

Analyzing financial performance of companies and grading them in today’s information-rich society can be a daunting task. With the evolution of the technology, Internet access to massive amounts of financial data, typically in the form of financial statements, is widespread.  Managers and stakeholders of the industries are in need of the data-mining tools to quickly and accurately analyze the financial data to know about the position of their companies.  An emerging technique that may be suited for such applications is the self-organizing map. The purpose of the present study is to evaluate the performances of 247 companies consist of five major industries from Indian corporate database. The time frame of the data pertaining the present study is 2001-2010. The salient feature of this study is the application of Factor, K-means clustering and Self Organizing Map (SOM) analyses as data mining tools to develop the hidden structure present in the data for each of the study periods.  The scores from extracted factors are used to find initial groups by K-means clustering algorithm. A few outlier industries, which could not be classified to any of the larger groups, are discarded as some of the ratios possessed higher values. Finally, SOM is applied and the groups are identified as companies belonging to A-Class (High performance), B-Class (Moderate performance) and C-Class (Low performance) in that order. The results of the study indicate that self-organizing maps can be a feasible tool for the financial analysis of large amounts of financial ratio data.