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)

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

Network Structure

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

Fitting Comparison of NN and Discriminant Analysis with Training Data

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

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