Robust Logistic Regression to Static Geometric Representation of Ratios

Noor Akma Ibrahim,Ismail Bin Mohd,A.K.M Azhar,Alireza Bahiraie

doi:10.3844/jmssp.2009.226.233

Abstract

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.

Highlights

In recent decades, business failure prediction has been one of the major research domains in financial researches to evaluate the financial health of companies[14]
Altman [1] extended this narrow interpretation by investigating a set of financial ratios as well as economic ratios as possible determinants of corporate failures using multiple discriminant analysis, in particular the Z-score model
All developments are on the theoretical derivations of outliers in logit method and there is a lack in applications of financial fields

Summary

Introduction

Business failure prediction has been one of the major research domains in financial researches to evaluate the financial health of companies[14]. It is obvious that Bankruptcy involves large costs and corporate failure prediction has been stimulated both by private and government sectors all over the world[9]. Bankruptcy prediction models have been proven necessary to obtain a more accurate statement of firm’s financial situation[18]. First Beaver[7] showed that corporate failure could be reliably predicted through the combined use of sophisticated quantitative using selected financial ratios. Altman [1] extended this narrow interpretation by investigating a set of financial ratios as well as economic ratios as possible determinants of corporate failures using multiple discriminant analysis, in particular the Z-score model. Since Altman[1], literature on predicting bankruptcy has witnessed numerous extensions and modifications.

Objectives

Methods

Results

Conclusion