Radial Basis Function in Artificial Neural Network for Prediction of Bankruptcy

Alireza Mehrazin, Mohammad Taghipour, Omid Froutan, Bashir Ghabdian, Hamid Soleimani

Abstract


Development of financial markets and consequences of economic crises at international level caused effects on
job environment and the companies’ future financial situation is a vital factor for different beneficiary groups,
bankruptcy prediction can be used a mean to help them. Prediction methods are constantly evolving, and
artificial neural networks have nowadays found a special position among these methods. Since learning
constitutes a significant part of neural network models, learning methods of training these models are of
particular importance. Therefore, finding a proper training method to reach the desired goals is necessary. Thus,
this study seeks to find a better method of building and training artificial neural networks which leads to more
accurate predictions of bankruptcy. Meanwhile, three neural networks of radial basis function type were built and
trained separately by Altman model (1983), Zmijewski model (1984) and combinatory models’ variables. After
evaluating the ability of these three models of bankruptcy prediction, their accuracy has been compared. Time
span of 2004 to 2012 (eight years) has been used to select samples from the listed companies in Tehran Stock
Exchange. Results show that all three models have the ability of predicting bankruptcy and the model trained
with Altman Model’s variables is more accurate than the other two models in this regard.

Full Text: PDF DOI: 10.5539/ibr.v6n8p121

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International Business Research  ISSN 1913-9004 (Print), ISSN 1913-9012 (Online)

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