A neural network for classifying the financial health of a firm

Lacher, R. C.; Coats, Pamela K.; Sharma, Shanker C.; Fant, L. Franklin

doi:10.1016/0377-2217(93)e0274-2

Cited by 148 publications

(62 citation statements)

References 12 publications

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“…Based on the pioneering work by Altman [2], most researchers simply use the same set of ®ve predictor variables as in Altman's original model [11,33,38,49,57,66]. These ®nancial ratios are (1) working capital/total assets; (2) retained earnings/ total assets; (3) earnings before interest and taxes/ total assets; (4) market value equity/book value of total debt; (5) sales/total assets.…”

Section: Bankruptcy Prediction With Neural Networkmentioning

confidence: 99%

“…Many researchers in bankruptcy forecasting including Lacher et al [33], Sharda and Wilson European Journal of Operational Research 116 (1999) 16±32 [57], Tam and Kiang [61], and Wilson and Sharda [66] report that neural networks produce signi®-cantly better prediction accuracy than classical statistical techniques. However, why neural networks give superior classi®cation is not clearly explained in the literature.…”

Section: Introductionmentioning

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: Introductionmentioning

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 suggested mathematical model comprises the following elements: -Data Envelopment Analysis (DEA), a method of vector optimization based on linear programming, [12]; -DEA-Neuron signal processor, providing the opportunity of inclusion of DEA algorithms and efficiency ratio into neural networks, [13]; -Cascade Correlation Neural Network (Cascor), [14], and evidence of its successful application in finance [15].…”

Section: Methodsmentioning

confidence: 99%

“…A training process stops when either acceptable accuracy is achieved or addition of the next hidden neuron does not increase the accuracy significantly. A training algorithm for the Cascor neural network is given in Figure 1 and is as follows; see [15] for detail: -Step A. Begin with a linear network, with network inputs fully connected to each input neuron, and with parameters initialized randomly.…”

Section: Methodsmentioning

confidence: 99%

A Dea-Cascor Model for High-Frequency Stock Trading:Computational Experiments in the U.S.Stock Market

Vaninsky¹

2010

IJNGN

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“…Among them we can highlight Bell et al (1990) and Koh & Tan (1999), using the multilayer perceptron model; Coats & Fant (1993) and Lacher et al (1995), using the Cascor method (cascade correlation) as a learning algorithm; and Serrano & Martin (1993), using a multilayer perceptron net and Kohonen's self-organizing maps.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Pereira¹,

Basto²,

Silva³

2014

JMS

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A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context.The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003-09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure.Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

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A neural network for classifying the financial health of a firm

Cited by 148 publications

References 12 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 Dea-Cascor Model for High-Frequency Stock Trading:Computational Experiments in the U.S.Stock Market

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

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