Credit Risk
Table of contents for The risk of Bankruptcy
Other Methods
A number of other statistical methods have also been used to predict future company failure and credit risk, see: (Atiya, 2001), (Chandra, Ravi, Bose, 2009) and (Bastos, 2008). A recent study (Boguslauskas, Mileris , 2009) analyzed 30 scientific publications comprising 77 models:
- 63% used artificial neural networks (ANN)
- 53% used logistic regression (LR)
- 37% used discriminant analysis (DA)
- 23% used decision trees and (DT)
- 33% used various other methods
The general accuracy of the different models was evaluated: the proportion of companies correctly classified (figure 3 in the article):
The box and whisker plot above shows that logistic regression (87%) and artificial neural networks (87%) gives almost the same accuracy while decision trees (83%) and discriminant analysis (77%) seems to be less reliable methods.
However from the boxes it is evident that decision trees as a method have a much larger variance in classification accuracy than the others and that artificial neural network have the lowest variance. For logistic regression and discriminant analysis the variance is approximately the same.
Comparing methods based on different data sets can easily be misleading. Accurate parameter estimation relies heavily on available data and their usability for that particular method.
Atiya, Amir F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE TRANSACTIONS ON NEURAL NETWORKS, 12(4), Retrieved from http://ieee-cis.org/pubs/tnn/
Bastos, Joao. (2008, April 01). Credit scoring with boosted decision trees. Retrieved from http://mpra.ub.uni-muenchen.de/8156/
Boguslauskas, Vytautas. Mileris, Ricardas. (2009). Estimation of credit risk by artificial neural networks models. ECONOMICS OF ENGINEERING DECISIONS, 4(64), Retrieved from http://internet.ktu.lt/en/science/journals/econo/inzek064.html
Chandra, D. K., Ravi, V., Bose, I. (2009). Failure prediction of dotcom companies using hybrid intelligent techniques. Expert Systems with Applications, (36), 4830–4837.
