Robust Logistic Regression to Static Geometric Representation of Ratios

Abstract

Problem statement: Some methodological problems concerning financial ratios such as nonproportionality, 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.

Recognition of Hand Printed Characters Based on Simple Geometric Features

“…The interest devoted to this field is not explained only by the exciting challenges involved, but also the huge benefits that a system, designed in the context of a commercial application, could bring (Morita et al, 2003). Two classes of recognition systems are usually distinguished: online systems (Tappert et al, 1990;Namboodiri and Jain, 2004;Liu et al, 2004;Bahiraie et al, 2009) for which handwriting data are captured during the writing process, which makes available the information on the ordering of the strokes and offline systems (Steinherz et al, 1999) for which recognition takes place on a static image captured once the writing process is over. With the increase in popularity of portable computing devices such as PDAs and handheld computers, non-keyboard based methods for data entry are receiving more attention in the research communities and commercial sector.…”