Optimization of the prediction of financial problems in Spanish private health companies using genetic algorithms

Abstract

Objective: This paper presents a methodology to optimize, using Altman's Z-Score for private companies, the prediction of private companies of the Spanish health sector entering a situation of bankruptcy. Method: The proposed method consists of the application of genetic algorithms (GA) to find the coefficients of the formula of the chain of ratios proposed by Altman in the version of the score for private companies which optimize the prediction for Spanish private health companies, maximizing sensitivity and specificity, and thereby reducing type I and type II errors. For this purpose, a sample of 5,903 companies from the Spanish private health sector obtained from the database of the Iberian Balance Analysis System (SABI) between 2007 and 2015 was used. Results: The results show that the predictive model obtained with the AG presents greater accuracy, sensitivity and specificity than that proposed by Altman for private companies with both test data and all sample data. Conclusions: The most important finding of this study was to establish a methodology that can identify the optimized coefficients for the Altman Z-Score, which allows a more accurate prediction of bankruptcy in Spanish private healthcare companies.