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Title

Extracting Interesting Financial Indicators Through Rough Set Approach

Author

Faudziah Ahmada, Azuraliza Abu Bakarb, Abdul Razak Hamdanc

Citation

Vol. 9  No. 9  pp. 189-194

Abstract

Financial data has been a popular source and used to analyze companies' performance. Measures based on financial data are many and among these are current ratio, quick ratio, net income, working capital, operational income, revenue, sales growth, earnings per share, gross profit, book value, stock price, and stock volume. All these measures are relevant indicators in measuring success. The use of all relevant indicators in assessing a company would present a tremendous burden in terms of data collection, analysis, and cost. Evidence in the literature, indicates that there are a limited number of critical areas necessary to the successfulness functioning of organizations. In this paper, a method of identifying interesting financial indicators is proposed. The method involves several steps: Problem Identification, Requirement Gathering, Indicator Extraction, and Evaluation. The underlying theory used in the proposed method is Rough Set. The main process in identifying relevant indicators is in the Indicator Extraction Phase. This phase consists of 6 steps: Data selection, Data Preprocessing, Discretization, Split Data, Reduction, and Classification. A dataset of 427 records have been used for experimentation. The datasets which contains financial information from several companies consists of 30 dependant indicators and one independent indicator. The major contribution of this work is the extraction method for identifying reduced indicators. Results obtained have shown competitive accuracies in classifying new cases, thus showing that the quality of knowledge is maintained through the use of a reduced set of indicators.

Keywords

companies’ performance, reduction, extraction, rough set

URL

http://paper.ijcsns.org/07_book/200909/20090924.pdf