Financial performance and distress profiles: from classification according to financial ratios to compositional classification

Financial ratios are often used in cluster analysis to classify firms according to the similarity of their financial structures. Besides the dependence of distances on ratio choice, ratios themselves have a number of serious problems when subject to a cluster analysis such as skewed distributions, outliers, and redundancy. Some solutions to overcome those drawbacks have been proposed in the literature, but have proven problematic. In this work we put forward an alternative financial statement analysis method for classifying firms which aims at solving the above mentioned shortcomings and draws from compositional data analysis. The method is based on the use of existent clustering methods with standard software on transformed data by means of the so-called isometric logarithms of ratios. The method saves analysis steps (outlier treatment and data reduction) while defining distances among firms in a meaningful way which does not depend on the particular ratios selected. We show examples of application to two different industries and compare the results with those obtained from standard ratiosThis work was supported by the Catalan Autonomous Government [grant number 2014SGR551], the Spanish Health Ministry [grant number CB06/02/1002], FEDER/Spanish Economy and Competitiveness Ministry [grant number MTM2015-65016-C2-1-R], and the University of Girona [grant numbers MPCUdG2016/069, MPCUdG2016/098, and MPCUdG2016/107