Comparison between rough set theory and logistic regression for classifying firm’s performance
Superior prediction and classification in determining company’s performance are major concerns for practitioners and academic researchers due to the importance of providing useful information to shareholders and potential investors. The general practice is to analyze firm’s performance based on f...
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Format: | Article |
Published: |
Penerbit ukm
2008
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Online Access: | http://journalarticle.ukm.my/1862/ http://www.ukm.my/~ppsmfst/jqma/index.html |
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Summary: | Superior prediction and classification in determining company’s performance are major
concerns for practitioners and academic researchers due to the importance of providing useful
information to shareholders and potential investors. The general practice is to analyze firm’s
performance based on financial indicators measured on the financial statements published in
the annual report, i.e. the balance sheet, income and cash flow statements. To do so, numerous
financial indicators are used in order to classify the performance of firm accordingly involving
many financial variables in order to determine firm’s performance. Some of these financial
ratios may be irrelevant and may correlate with each other giving redundant information for
classification. Hence, this study investigates and determines the financial ratios that notably
affect firm’s performance so as to predict future performance. In this study, firm is considered
as high performance firm if its share returns perform above returns provided by Kuala Lumpur
Composite Index. Using various financial ratios as the independent variables, this study
investigates and determines the financial indicators that significantly affect firm’s share
performance so as to predict its future performance. Financial performance predictions
normally involve large information to explore, using traditional statistical methods such as
multiple discriminant analysis or binary logistic regression analysis. Parametric statistical
models are based on normality assumption requirements for the interpretation of the tests of
significance, and if the data does not satisfy this assumption, the results obtained may be
biased. However, it is noted that most financial ratios are skewed and non-normally distributed
suggesting that non-parametric test is a superior alternative. Non-parametric model does not
assume the data to have any specific characteristics. In this study comparison between rough
set and logistic regression methodology is employed to identify the most significant indicators
in classifying firm’s performance. It was found that rough set model could accurately predict
86% of the testing dataset whereas logistic regression model shows 61.6% accuracy rate |
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