Risk box

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.

Abstract: Problem statement: Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. Approach: This study presented new geometric technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box measure (RB). We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors), accuracy of each estimation model and the representation of each model for transformed and common ratios. Results: We provided evidence of extent to which changes in values of this index were associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic Programming (GP) models were compared as different classification models and results showed the classifiers outperform by modified ratios. Conclusion/Recommendations: In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. Genetic programming method is substantially superior to the traditional methods such as MDA or Logistic 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, GP will provide heuristic non linear regression as a tool in providing forecasting regression for studies associated with financial data. Genetic programming as one of the modern classification method out performs by the use of modified ratios. Our new method would be a general methodological guideline associated with financial data analysis.

Abstract: This paper presents a complementary technique for empirical analysis of financial ratios and bankruptcy risk. Within this new framework, we propose the use of a new measure of risk, the Generalized Risk Box (GRB) measure. This method would be a general methodological guideline associated with financial data, including solving some methodological problems concerning financial ratios such as nonproportionality, non-asymmetry and non-scalability. In this paper, bankruptcy prediction and better accuracy rates obtained with GRB approach in compare to employing common ratios. This paper also suggests a Robust Logit method, which extends the Logit model by taking outlier into account. We employ Logit and Robust Logit Regression to assess our new method and sample forecast performances. Accuracy results show Robust Loigt method is substantially superior to the Logit method in financial studies. Mathematics Subject Classification: 90B50, 91B30, 91G40, 91B06

Abstract: In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms' impending failure for investors, creditors, borrowing firms, and governments By the time, several methods have been attempted in the use of financial ratios on predicting bankruptcy but some of them suffer from underlying shortcomings Recently, Genetic Programming (GP) has received great attention in academic and empirical fields of solving high complex problems The paper proposes the use of Dynamic Risk Space measure (DRS) on bankruptcy prediction utilized with Genetic Programming technique The paper provides the evidence of the extent to which changes in values of this index are associated with changes in each values axis and how this may alter our economic interpretation of changes in the patterns and direction of risk Results of Dynamic Geometric Genetic Programming (DGGP) classification methodology is compared with common and transformed ratios Results confirm the better accuracy which Genetic classification tree achieved (overall 9514% accuracy rate) using transformed ratios approach while original ratios model achieved only 8885% accuracy rate