Financial distress prediction by a radial basis function network with logit analysis learning

Ченг, Чиа-Чи; Chen, Ching‐Lung; Fu, Chung‐Jen

doi:10.1016/j.camwa.2005.07.016

Cited by 57 publications

(35 citation statements)

References 25 publications

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“…there is an effort to eliminate redundant variables. Such an approach is known in the literature as the creation of the reduced form of the model, and represents the most common approach to the creation of the model that was used, for example, in the studies (Lin, Liang, Chen, 2011;Wang, Lee, 2008;Niemann et al, 2008;Tseng, Hu, 2010;Psillaki, Tsolas, Margaritis, 2009;Cheng, Chen, Fu, 2006).…”

Section: Methodsmentioning

confidence: 99%

The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies

Karas¹,

Režňáková²

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The current development of bankruptcy models usually goes in the direction of testing different classification algorithms, while the potential hidden in financial indicators is given less attention. Their analysis is often only restricted to the comparison between their respective statuses in bankrupt and healthy companies, while the dynamics of the indicators, i.e. the change in their values in time, is not paid much attention. The aim or our research is to analyse partial potential of financial ratios for predicting bankruptcy. Twenty-eight indicators were examined in a sample of 1,355 construction companies operating in the Czech Republic, as well as their development over the past five periods. A non-parametric chi-square test was used to evaluate the significance of predictors. The variables were categorised for the application of the test. Our research confirmed the assumption as to the importance of using the indicators in dynamic (change) form. Indicators that are significant only in their change form were identified. Moreover, the use of the dynamic form of the indicators can increase the significance of the bankruptcy model. This was tested by using the stepwise version of linear discrimination analysis.

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Section: Methodsmentioning

confidence: 99%

The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies

Karas¹,

Režňáková²

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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“…In general, hybrid learning methods are understood as systems that combine two or more different techniques in order to benefit from the synergistic effect between the individual components [71,76]. For instance, a hybrid prediction model may consist of one unsupervised learner to pre-process the training data into homogeneous clusters and one supervised algorithm to build the classifier from the clustering result [29,71], or it may use a feature selection strategy to choose the most relevant explanatory variables and then these are employed to design the predictor [44,45,56], or even it may be built from different cascading predictors in order to build an ensemble of classifiers [20,21,35]. However, as already described previously, the HACT model simply makes use of the fundamental ideas of two types of associative memories (one for the learning phase and the other for the recall phase), but there is not hybridization between them.…”

Section: Hybrid Associative Classifier With Translationmentioning

confidence: 99%

“…Similarly, Lee et al [45] explored the performance of credit scoring by integrating the linear discriminant analysis approach into a three-layer back-propagation neural network, revealing that the proposed hybrid approach converges much faster than the conventional neural network model and outperforms the discriminant analysis and logistic regression approaches. Cheng et al [20] adopted an RBF to construct the financial prediction model and then carried out a logit analysis on the groups of similar firms present in the hidden layer of the network.…”

Section: A Review Of Neural Network Applied To Financial Distress Prmentioning

confidence: 99%

Financial distress prediction using the hybrid associative memory with translation

Cleofas-Sánchez

García

Marqués

et al. 2016

Applied Soft Computing

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This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.

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“…Prediction accuracy can be computed as the percentage of the firms that are correctly classified to the healthy or bankrupt firms. This concept is a widely used measure of predictive accuracy [26]. Type I error occurs when a healthy firm is incorrectly classified as a bankrupt firm and Type II error occurs when a bankrupt firm is being classified as a healthy firm.…”

Section: Assessment and Comparison Of Model's Performancementioning

confidence: 99%

Applying Adaptive NeuroFuzzy Model for Bankruptcy Prediction

Zanganeh¹,

Rabiee²,

Zarei³

2011

IJCA

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This study demonstrates effectiveness of ANFIS in bankruptcy prediction which has received a few attentions in the previous bankruptcy studies. A data set consisting of financial ratios of 136 matched bankrupt and non-bankrupt firms in Tehran Stock Exchange (TSE) during 1997-2008. Moreover, two different procedures are used for selecting the predictive variables. The first procedure is using T-statistic feature selection method. Another one is not using any feature selection method. In second procedure, just examination of former researches is used for selecting the predictive variables. The resulting models are estimated with three different data set partitioning patterns. Analysis of empirical results indicates:(1) The ANFIS model outperforms Logistic Regression (LR) model in both training and testing samples. (2) The subset of frequent variables in the former literature yields better prediction models rather than variables are selected based on T-statistic feature selection method.

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Financial distress prediction by a radial basis function network with logit analysis learning

Cited by 57 publications

References 25 publications

The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies

The Potential of Dynamic Indicator in Development of the Bankruptcy Prediction Models: the Case of Construction Companies

Financial distress prediction using the hybrid associative memory with translation

Applying Adaptive NeuroFuzzy Model for Bankruptcy Prediction

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