## A Comparison of Statistical Techniques and Artificial Neural Network Models in Corporate Bankruptcy Prediction |

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### Contents

Firm Selection and Data Acquisition | 7 |

Modeling the Bankruptcy Process | 34 |

Network | 47 |

5 other sections not shown

### Common terms and phrases

05 level activation function artificial neural network backpropagation artificial neural backpropagation network bankrupt and nonbankrupt Bankrupt Firms Non-Bankrupt bankruptcy prediction Caudill Chapter Classification Performance Logistic Compustat counterpropagation artificial neural counterpropagation network critical value CSE/TD Delta rule discriminant models error Estimation Sample Prediction expert systems False Negative Rate False Positive Rate Figure financial ratios Firms Non-Bankrupt Firms Full Sample hidden units logistic function logistic regression model logit model Mann-Whitney Matched mean squared error Measure of Classification Model Hit Rates Negative Rate Measure network architecture network results neural network models Neuralware Non-Bankrupt Firms Total nonparametric discriminant analysis number of firms Oplnc/TA p-value percent perceptron network Performance Logistic Regression prediction models Prediction Sample Sensitivity predictive ability presented processing elements Reduced Sample Regression Model Hit Regression Tests Regression vs Backpropagation Rumelhart Sample Critical Probability Sample Prediction Sample SIC Code significant stimation Tample TA/TT Table Tales/TA techniques threshold training examples TT/TT weight vector