1. A case in Neural Network - A Nerual Network for Bankruptcy Prediction
Based on a paper
Published in Decision Support Systems, 1994
By Rick Wilson and Ramesh Sharda
ANN Architecture
Three-layer (input-hidden-output) MLP
Backpropagation (supervised) learning network
Training data
Small set of well-known financial ratios
Data available on bankruptcy outcomes
Moody’s industrial manual (between 1975 and 1982)

2. Bankruptcy Prediction with ANN
Application Design Specifics
Five Input Nodes
X1: Working capital/total assets
X2: Retained earnings/total assets
X3: Earnings before interest and taxes/total assets
X4: Market value of equity/total debt
X5: Sales/total assets
Single Output Node: Final classification for each firm
Bankruptcy (0) or
Nonbankruptcy (1)
Development Tool: NeuroShell

3. Network Structure

4. Bankruptcy Prediction with ANN
Training & Testing
Data Set: 129 firms
Training Set: 74 firms - 38 bankrupt, 36 not
Test Set (Hold out set) : 55 firms - 27 bankrupt firms, 28
nonbankrupt firms
Two Methods are compared:
The neural network
A conventional discriminant analysis program

5. Fitting Comparison of NN and Discriminant Analysis with Training Data

7. Performance Comparison with Test(hold out) data set
50/50
80/20
90/10 NN
0.818
0.782 DA
0.745
0.69
*NN: Neural Networks
*DA: Discriminant Analysis

8. Bankruptcy Prediction with ANN
Results
The neural network correctly predicted (50/50):
81.8percent bankrupt cases (NN)
74.5 percent nonbankrupt cases(DA)
Accuracy of about 80 percent is usually acceptable for this problem domain