A neural network application for bankruptcy prediction

Raghupathi, W.; Schkade, Lawrence L.; Raju, Biju

doi:10.1109/hicss.1991.184054

Cited by 37 publications

(29 citation statements)

References 5 publications

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“…For example, Raghupathi et al [48] In order to detect maximal dierence between bankrupt and nonbankrupt ®rms, many studies employ matched samples based on some common characteristics in their data collection process. Characteristics used for this purpose include asset or capital size and sales [19,36,63], industry category or economic sector [48], geographic location [55], number of branches, age, and charter status [61]. This sample selection procedure implies that sample mixture ratio of bankrupt to nonbankrupt ®rms is 50% to 50%.…”

Section: Bankruptcy Prediction With Neural Networkmentioning

confidence: 99%

“…They ®nd that GANNA and backpropagation algorithm are comparable in terms of the predictive capability but GANNA saves them time and eort in building an appropriate network structure. Raghupathi [47] conducts an exploratory study to compare eight alternative neural network training algorithms in the domains of bankruptcy prediction. He ®nds that the Madaline algorithm is the best in terms of correct classi®cations.…”

Section: Bankruptcy Prediction With Neural Networkmentioning

confidence: 99%

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

Zhang

Patuwo

et al. 1999

European Journal of Operational Research

539

280

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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

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Section: Bankruptcy Prediction With Neural Networkmentioning

confidence: 99%

Section: Bankruptcy Prediction With Neural Networkmentioning

confidence: 99%

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

Zhang

Patuwo

et al. 1999

European Journal of Operational Research

539

280

View full text Add to dashboard Cite

show abstract

“…The results are associated with the findings of Odom & Sharda (1990) and Raghupathi & Schkade and Raju (1991), Koh & Tan (1999) and Charitou et al (2004). They also found that the models developed with neural networks (NN) can achieve a better classification accuracy rate than other statistical methods.…”

Section: Resultsmentioning

confidence: 77%

A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis

Al-Hroot¹

2016

IBR

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The main purpose of this study is to develop and compare the classification accuracy of bankruptcy prediction models using the multilayer perceptron neural network, and discriminant analysis, for the industrial sector in Jordan. The models were developed using the ten popular financial ratios found to be useful in earlier studies and expected to predict bankruptcy. The study sample was divided into two samples; the original sample (n=14) for developing the two models and a hold-out sample (n=18) for testing the prediction of models for three years prior to bankruptcy during the period from 2000 to 2014.The results indicated that there was a difference in prediction accuracy between models in two and three years prior to failure. The results indicated that the multilayer perceptron neural network model achieved a higher overall classification accuracy rate for all three years prior to bankruptcy than the discriminant analysis model. Furthermore, the prediction rate was 94.44% two years prior to bankruptcy using multilayer perceptron neural network model and 72.22% using the discriminant analysis model. This is a significant difference of 22.22%. On the other side, the prediction rate of 83.34% three years prior to bankruptcy using multilayer perceptron neural network model and 61.11% using discriminant analysis model. We indicate there was a difference exists of 22.23%. In addition, the multilayer perceptron neural network model provides in the first two years prior to bankruptcy the lowest percentage of type I error.

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“…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].…”

Section: Variablesmentioning

confidence: 99%

“…The group that showed solvency issues was identified using COMPU-STAT, and it includes all the companies for which all the required information was available. However, it excludes utilities, financial services and transportation companies since they are structurally different and have bankruptcy environments that are different from the rest [14]. The date of bankruptcy filings was obtained through inspection of documents filed in the Securities and Exchange Commission and accessible through the Electronic Data Gathering, Analysis, and Retrieval System (EDGAR).…”

Section: Datamentioning

confidence: 99%

Early bankruptcy prediction using ENPC

et al. 2007

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Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.

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A neural network application for bankruptcy prediction

Cited by 37 publications

References 5 publications

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

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

A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis

Early bankruptcy prediction using ENPC

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