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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Penerbit ukm
2008
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ukm-18622011-06-15T07:04:05Z http://journalarticle.ukm.my/1862/ Comparison between rough set theory and logistic regression for classifying firm’s performance Bahtiar Jamili Zaini, Siti Mariyam Shamsuddin, Saiful Hafizah Jaaman , 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 Penerbit ukm 2008-07 Article PeerReviewed Bahtiar Jamili Zaini, and Siti Mariyam Shamsuddin, and Saiful Hafizah Jaaman , (2008) Comparison between rough set theory and logistic regression for classifying firm’s performance. Journal of Quality Measurement and Analysis, 4 (1). pp. 141-153. ISSN 1823-5670 http://www.ukm.my/~ppsmfst/jqma/index.html |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Kebangasaan Malaysia |
| building |
UKM Institutional Repository |
| collection |
Online Access |
| description |
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 |
| format |
Article |
| author |
Bahtiar Jamili Zaini, Siti Mariyam Shamsuddin, Saiful Hafizah Jaaman , |
| spellingShingle |
Bahtiar Jamili Zaini, Siti Mariyam Shamsuddin, Saiful Hafizah Jaaman , Comparison between rough set theory and logistic regression for classifying firm’s performance |
| author_facet |
Bahtiar Jamili Zaini, Siti Mariyam Shamsuddin, Saiful Hafizah Jaaman , |
| author_sort |
Bahtiar Jamili Zaini, |
| title |
Comparison between rough set theory and logistic
regression for classifying firm’s performance
|
| title_short |
Comparison between rough set theory and logistic
regression for classifying firm’s performance
|
| title_full |
Comparison between rough set theory and logistic
regression for classifying firm’s performance
|
| title_fullStr |
Comparison between rough set theory and logistic
regression for classifying firm’s performance
|
| title_full_unstemmed |
Comparison between rough set theory and logistic
regression for classifying firm’s performance
|
| title_sort |
comparison between rough set theory and logistic
regression for classifying firm’s performance |
| publisher |
Penerbit ukm |
| publishDate |
2008 |
| url |
http://journalarticle.ukm.my/1862/ http://journalarticle.ukm.my/1862/ |
| first_indexed |
2023-09-18T19:34:32Z |
| last_indexed |
2023-09-18T19:34:32Z |
| _version_ |
1777405182276009984 |