Applying artificial neural networks to bank-decision simulations

被引：0
作者：
Witkowska D. [1 ]
机构：
[1] Technical University of Lodz,
来源：
International Advances in Economic Research | 1999年 / 5卷 / 3期
关键词：
Neural Network; Economic Growth; Testing Sample; Artificial Neural Network; Classification Accuracy;
D O I：
10.1007/BF02296417
中图分类号：
学科分类号：
摘要：
Artificial neural networks are nonlinear models that can be trained to extract hidden structures and relationships that govern the data. They can be used for analyzing relationships among economic and financial phenomena. This paper presents research on applying a back propagation algorithm to firm classification. Experiments were provided for three neural network architectures by applying training and testing samples constructed from actual data of the firms that applied for credit in regional banks for the period 1994-97. To study the effect of proportion between the number of firms that obtained and did not obtain credit, three proportions of the training and testing set compositions were created: 1:1, 2:1, and 4:1. Classification accuracy was evaluated in terms of errors made by the neural networks. (JEL O50, C10).
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页码：350 / 368
页数：18
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