Using Machine Learning, Neural Networks And Statistics To Predict Corporate Bankruptcy: A Comparative Study
ARTIFICIAL INTELLIGENCE IN ECONOMICS AND MANAGEMENT: AN EDITED PROCEEDINGS ON THE FOURTH INTERNATIONAL WORKSHOP: AIEM4(1996)
摘要
Recent literature strongly suggests that machine learning approaches to classification outperform ''classical'' statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees and neural networks in predicting corporate bankruptcy Linear discriminant analysis represents the ''classical'' statistical approach to classification, whereas classification frees and neural networks represent artificial intelligence approaches. A proper statistical design is used to be able to test whether observed differences in predictive performance are statistically significant The dataset consists of two large collections of annual reports from Belgian companies. The first collection contains the reports of 994 industrial companies and the second collection contains the reports of 576 construction companies. We use stratified 10-fold cross-validation on the training set to choose ''good'' parameter values for the different learning methods. The test set is used to obtain an unbiased estimate of the true prediction error, For both industrial and construction companies the result of neural networks is better than the results of the other learning techniques. Upon rigorous statistical testing only the difference between the classification free and the neural network for the construction companies is found to be significant.
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