Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

Abstract

In this paper, we present a general framework for understanding the role of arti®cial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classi®cation theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 ®rms, our ®ndings indicate that neural networks are signi®cantly better than logistic regression models in prediction as well as classi®cation rate estimation. In addition, neural networks are robust to sampling variations in overall classi®cation performance. Ó

VIP-targeted CRM strategies in an open market

“…Though it is really difficult to make a universally approved definition of CRM, it can be explained as a comprehensive strategy for acquiring, retaining and partnering with selective customers to create value for both a company and its customers. Many previous CRM-related researches have applied data mining techniques to analyse and understand customer behaviours and characteristics, and most of them have worked well (Bortiz et al ., 1995;Fletcher et al ., 1993;Langley et al ., 1995;Lau et al ., 2003;Salchenberger et al ., 1992;Su et al ., 2002;Tam et al ., 1992;Zhang et al ., 1999). In this section, we review previous researches mainly on classification and association rules for a variety of tasks in CRM domain.…”

Early bankruptcy prediction using ENPC

“…The result was the identification of five variables that he used to develop one of the most influential models in bankruptcy prediction, the Z-score. These variables have been widely used to test different bankruptcy prediction models differently [11][12][13]. The set of explanatory variables that we will use in our analysis is provided in Table 1.…”

“…The list basically mirrors the financial items suggested by Altman plus the current ratio (Current Assets / Current Liabilities). This additional variable is supposed to be a good indicator of short-term solvency and has also been used in the past [13,14].…”

Adaptive Neuro-Fuzzy Inference System in Predicting the Success of Student’s in a Particular Course

“…But, it will not be suitable for some circumstances. As Zhang, Hu, Patuwo, Indro (1999) stated in their articles; evaluation in that the characteristics of the test may be very different from those of the training and the estimated classification rate can be very different from the true classification rate particularly when small-size samples are involved [5]. For all these reasons, cross-validation method is used in this application.…”

“…Owing to this method, the performance of the ANFIS model can be more accurately defined. Also the cross-validation analysis yields valuable insights on the reliability of the ANFIS mfodel with respect to sampling variation [5]. Further information about cross validation is given in the Section III.…”