0505412 - ÚI 2020 GB eng J - Journal Article
Bemš, J. - Starý, O. - Macas, M. - Žegklitz, Jan - Pošík, P.
Innovative default prediction approach.
Expert Systems With Applications. Roč. 42, 17-18 (2015), s. 6277-6285. ISSN 0957-4174. E-ISSN 1873-6793
Keywords : bankruptcy prediction * corporate bankruptcy * feature-selection * financial ratios * algorithm * Magic * Square * Default * Prediction * Scoring
Impact factor: 2.981, year: 2015
DOI: https://doi.org/10.1016/j.eswa.2015.04.053

This paper introduces a new scoring method for company default prediction. The method is based on a modified magic square (a spider diagram with four perpendicular axes) which is used to evaluate economic performance of a country. The evaluation is quantified by the area of a polygon, whose vertices are points lying on the axes. The axes represent economic indicators having significant importance for an economic performance evaluation. The proposed method deals with magic square limitations; e.g. an axis zero point not placed in the axes origins, and extends its usage for an arbitrary (higher than 3) number of variables. This approach is applied on corporations to evaluate their economic performance and identify the companies suspected to default. In general, a company score reflects their economic performance; it is calculated as a polygon area. The proposed method is based on the identification of the parameters (axes order, parameters weights and angles between axes) needed to achieve maximum possible model performance. The developed method uses company financial ratios from its financial statements (debt ratio, return on costs etc.) and the information about a company default or bankruptcy as primary input data. The method is based on obtaining a maximum value of the Gini (or Kolmogorov Smimov) index that reflects the quality of the ordering of companies according to their score values. Defaulted companies should have a lower score than non-defaulted companies. The number of parameter groups (axes order, parameters weights and angles between axes) can be reduced without a negative impact on the model performance. Historical data is used to set up model parameters for the prediction of possible future companies default. In addition, the methodology allows calculating the threshold value of the score to separate the companies that are suspicious to the default from other companies. A threshold value is also necessary for a model true positive rate and true negative rate calculations. Training and validation processes for the developed model were performed on two independent and disjunct datasets. The performance of the proposed method is comparable to other methods such as logistic regression and neural networks. One of the major advantages of the proposed method is a graphical interpretation of a company score in the form of a diagram enabling a simple illustration of individual factor contribution to the total score value. (C) 2015 Elsevier Ltd. All rights reserved.

Permanent Link: http://hdl.handle.net/11104/0296919