Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)

Authors:
Javier De Andrés;Pedro Lorca;Francisco Javier de Cos Juez;Fernando Sánchez-Lasheras
Affiliations:
University of Oviedo, Department of Accounting, Avda. del Cristo s/n, Oviedo 33006, Spain;University of Oviedo, Department of Accounting, Avda. del Cristo s/n, Oviedo 33006, Spain;University of Oviedo, Department of Exploitation and Exploration of Mines, c/ Independencia No 13, Oviedo 33004, Spain;Tecniproject SL, Research Department, c/ Marqués de Pidal No 7, Oviedo 33004, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

During the last years, hybrid models have proven to be a promising approach for the design of classification systems for the forecasting of bankruptcy. In the present research we propose a hybrid system which combines fuzzy clustering and MARS. Both models are especially suitable for the bankruptcy prediction problem, due to their theoretical advantages when the information used for the forecasting is drawn from company financial statements. We test the accuracy of our approach in a real setting consisting of a database made up of 59,336 non-bankrupt Spanish companies and 138 distressed firms which went bankrupt during 2007. As benchmarking techniques we used discriminant analysis, MARS and a feed-forward neural network. Our results show that the hybrid model outperforms the other systems, both in terms of the percentage of correct classifications and in terms of the profit generated by the lending decisions.