The Distributional Properties of Financial Ratios: The Case of Czech Bankruptcy Data

The Distributional Properties of Financial Ratios: The Case of Czech Bankruptcy Data

Peer-reviewed article not indexed in WoS or Scopus

The purpose of this paper is to analyse the distributional properties of financial data, suitable for building a bankruptcy forecast model, in the sense of normality deviation and the existence of outliers. In praxis, financial data in the form of financial ratios is very often not normally distributed. A Shapiro-Wilks procedure was used to test normality (Shapiro, Wilk, 1965) and a Box-Cox transformation (Box, Cox, 1964) for normalizing financial ratios. We would like to contributed to the previous pieces of research in following ways. Firstly, by analysing a greater range of accounting ratios or indicators (i.e. 44), secondly, by focusing on data of a different character (data suitable for building a bankruptcy forecast model), thirdly, by explaining cases in which the parameter l is not possible to estimate, and finally fourthly, identifying a possible cause of transformation failure in achieving normality of financial ratios. Before the transformation none of the analysed financial ratios met the condition of one-dimensional normality, not even on the 1-% level. After transformation, the condition of one-dimensional normality was met, at the 1-% level, by 34% of the analysed financial ratios. The same condition, but at the 5 or 10-% level, was met by 27% of the analysed financial ratios. The parameter l was not possible to estimate in the case of 18% of financial ratios. The condition of normality for untransformed Czech bankruptcy data seems almost as impossible to fulfil. This conclusion implies the use of non-parametric methods, such as artificial neural networks. However, the comparison of the parametric methods performance using untransformed or transformed data is the subject of further research.

The purpose of this paper is to analyse the distributional properties of financial data, suitable for building a bankruptcy forecast model, in the sense of normality deviation and the existence of outliers. In praxis, financial data in the form of financial ratios is very often not normally distributed. A Shapiro-Wilks procedure was used to test normality (Shapiro, Wilk, 1965) and a Box-Cox transformation (Box, Cox, 1964) for normalizing financial ratios. We would like to contributed to the previous pieces of research in following ways. Firstly, by analysing a greater range of accounting ratios or indicators (i.e. 44), secondly, by focusing on data of a different character (data suitable for building a bankruptcy forecast model), thirdly, by explaining cases in which the parameter l is not possible to estimate, and finally fourthly, identifying a possible cause of transformation failure in achieving normality of financial ratios. Before the transformation none of the analysed financial ratios met the condition of one-dimensional normality, not even on the 1-% level. After transformation, the condition of one-dimensional normality was met, at the 1-% level, by 34% of the analysed financial ratios. The same condition, but at the 5 or 10-% level, was met by 27% of the analysed financial ratios. The parameter l was not possible to estimate in the case of 18% of financial ratios. The condition of normality for untransformed Czech bankruptcy data seems almost as impossible to fulfil. This conclusion implies the use of non-parametric methods, such as artificial neural networks. However, the comparison of the parametric methods performance using untransformed or transformed data is the subject of further research.

bankruptcy, financial ratios, outlier detection, normality, data transformation

bankruptcy, financial ratios, outlier detection, normality, data transformation

KARAS, M.; REŽŇÁKOVÁ, M.

2014

08.04.2013

Brno

1802-8527

Trendy ekonomiky a managementu

VII

13

Czech Republic

56

67

12