Traditional bankruptcy prediction models, designed using classification or regression techniques, achieve short-term performances (1 year) that are fairly good, but that often worsen when the prediction horizon exceeds 1 year. We show how to improve the performance of such models beyond one year using models that take into account the evolution of firm financial health over a short period of ti...

This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt and non-bankrupt firms selected across the timeline of 1985-2013. The main goal of this paper is to explore the predictive power of an expanded variable set tailored to the construction industry compared to ...

Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. ...

Classification refers to a set of methods that predict the class of an object from attributes or features describing the object. In this paper we present a fuzzy classification algorithm to predict bankruptcy. Our classification algorithm is modified from a subspace clustering algorithm called fuzzy subspace clustering (FSC). As our algorithm associates each feature of a class with a fuzzy memb...

In this paper, we examine the effect of intra-industry credit contagion using three different kinds of intra-industry measures in our empirical study. We apply the competitive strategic measure (CSM) of Sundaram et al. (1996) to capture the strategic interaction faced by firms. We also consider the measure of industry-wide financial distress and the measure of equity correlations. We use the fo...

In this paper, we apply the CART ,C5.0 , GP decision tree classifiers and compares with logic model and ANN model for Taiwan listed electronic companies bankruptcy prediction. Results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out sample. That is to say, GP decision tree model have the highest ac...

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The interest in the prediction of corporate bankruptcy is increasing due to the implications associated with this phenomenon (e.g. economic, and social) for investors, creditors, competitors, government, although this is a classical problem in the financial literature. Two kinds of models are generally adopted for bankruptcy prediction: (i) accounting ratios based models and (ii) market based m...

We compare several accounting based models for bankruptcy prediction. The models are developed and tested on large data sets containing annual financial statements for Norwegian limited liability firms. Out-of-sample and out-of-time validation shows that generalized additive models significantly outperform popular models like linear discriminant analysis, generalized linear models and neural ne...

In this paper we develop statistical models for bankruptcy prediction of Norwegian firms in the limited liability sector using annual balance sheet information. We fit generalized linear-, generalized linear mixedand generalized additive models in a discrete hazard setting. It is demonstrated that careful examination of the functional relationship between the explanatory variables and the proba...