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Robust logistic regression to static geometric representation of ratios

Problem statement: Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-salacity were solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. This new method would be a gene...

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Main Authors: Bahiraie, Alireza, Ibrahim, Noor Akma, Abdul Karim, Mohd Azhar, Mohd, Ismail
Format: Article
Language:English
Published: Science Publications 2009
Online Access:http://psasir.upm.edu.my/id/eprint/16592/1/Robust%20logistic%20regression%20to%20static%20geometric%20representation%20of%20ratios.pdf
http://psasir.upm.edu.my/id/eprint/16592/
http://thescipub.com/html/10.3844/jmssp.2009.226.233
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spelling my.upm.eprints.165922015-09-03T02:14:21Z http://psasir.upm.edu.my/id/eprint/16592/ Robust logistic regression to static geometric representation of ratios Bahiraie, Alireza Ibrahim, Noor Akma Abdul Karim, Mohd Azhar Mohd, Ismail Problem statement: Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-salacity were solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. This new method would be a general methodological guideline associated with financial data and bankruptcy risk. Approach: We proposed the use of a new measure of risk, the Share Risk (SR) measure. We provided evidence of the extent to which changes in values of this index are associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and directions. Our simple methodology provided a geometric illustration of the new proposed risk measure and transformation behavior. This study also employed Robust logit method, which extends the logit model by considering outlier. Results: Results showed new SR method obtained better numerical results in compare to common ratios approach. With respect to accuracy results, Logistic and Robust Logistic Regression Analysis illustrated that this new transformation (SR) produced more accurate prediction statistically and can be used as an alternative for common ratios. Additionally, robust logit model outperforms logit model in both approaches and was substantially superior to the logit method in predictions to assess sample forecast performances and regressions. Conclusion/Recommendations: This study presented a new perspective on the study of firm financial statement and bankruptcy. In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. With respect to forecast results, robust loigt method was substantially superior to the logit method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, robust logit regression can be employed as a tool of regression in providing regression for studies associated with financial data. Science Publications 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16592/1/Robust%20logistic%20regression%20to%20static%20geometric%20representation%20of%20ratios.pdf Bahiraie, Alireza and Ibrahim, Noor Akma and Abdul Karim, Mohd Azhar and Mohd, Ismail (2009) Robust logistic regression to static geometric representation of ratios. Journal of Mathematics and Statistics, 5 (3). pp. 226-233. ISSN 1549-3644; ESSN: 1558-6359 http://thescipub.com/html/10.3844/jmssp.2009.226.233 10.3844/jmssp.2009.226.233
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Problem statement: Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-salacity were solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. This new method would be a general methodological guideline associated with financial data and bankruptcy risk. Approach: We proposed the use of a new measure of risk, the Share Risk (SR) measure. We provided evidence of the extent to which changes in values of this index are associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and directions. Our simple methodology provided a geometric illustration of the new proposed risk measure and transformation behavior. This study also employed Robust logit method, which extends the logit model by considering outlier. Results: Results showed new SR method obtained better numerical results in compare to common ratios approach. With respect to accuracy results, Logistic and Robust Logistic Regression Analysis illustrated that this new transformation (SR) produced more accurate prediction statistically and can be used as an alternative for common ratios. Additionally, robust logit model outperforms logit model in both approaches and was substantially superior to the logit method in predictions to assess sample forecast performances and regressions. Conclusion/Recommendations: This study presented a new perspective on the study of firm financial statement and bankruptcy. In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. With respect to forecast results, robust loigt method was substantially superior to the logit method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, robust logit regression can be employed as a tool of regression in providing regression for studies associated with financial data.
format Article
author Bahiraie, Alireza
Ibrahim, Noor Akma
Abdul Karim, Mohd Azhar
Mohd, Ismail
spellingShingle Bahiraie, Alireza
Ibrahim, Noor Akma
Abdul Karim, Mohd Azhar
Mohd, Ismail
Robust logistic regression to static geometric representation of ratios
author_facet Bahiraie, Alireza
Ibrahim, Noor Akma
Abdul Karim, Mohd Azhar
Mohd, Ismail
author_sort Bahiraie, Alireza
title Robust logistic regression to static geometric representation of ratios
title_short Robust logistic regression to static geometric representation of ratios
title_full Robust logistic regression to static geometric representation of ratios
title_fullStr Robust logistic regression to static geometric representation of ratios
title_full_unstemmed Robust logistic regression to static geometric representation of ratios
title_sort robust logistic regression to static geometric representation of ratios
publisher Science Publications
publishDate 2009
url http://psasir.upm.edu.my/id/eprint/16592/1/Robust%20logistic%20regression%20to%20static%20geometric%20representation%20of%20ratios.pdf
http://psasir.upm.edu.my/id/eprint/16592/
http://thescipub.com/html/10.3844/jmssp.2009.226.233
_version_ 1643826259212369920
score 13.211869

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