Abstract
<p>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.</p><p>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.</p>
Highlights
It is more than 80 years since the first study by Fitzpatrick (1932) on bankruptcy
The most important financial ratios that investors can use for making their decisions based on the discriminant analysis (DA) model are; Return on Assets (ROA), Debt Ratio and Margin before Interest and Tax
The results indicated that the multilayer perceptron neural network (MLPNN) model achieved the highest overall classification accuracy rate for all three years prior to bankruptcy than the DA model
Summary
It is more than 80 years since the first study by Fitzpatrick (1932) on bankruptcy. Researchers use statistical techniques, such as logistic regression, discriminant analysis and neural networks to build prediction models for assessing and predicting bankruptcy (business failure), with a very high accuracy rate reached in many studies. Since the late 1980s, researchers in Jordan have been working to build prediction models using statistical techniques for assessing and predicting business failure, such as discriminant analysis or by applying the Altman model. The main objective of the current study is to build two prediction models with data from the Jordanian Industrial Sector during the period 2000 to 2014 for a total of 32 companies, using the multilayer perceptron neural network (MLPNN) and discriminant analysis (DA) to predict the risk of bankruptcy three years prior to the event and compare the performance of the two models. Section four discusses empirical results, and the final section presents the findings of the study and the conclusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
