Financial Bankruptcy Risk Prediction Based on Accounting, Market and Hybrid Models by using RBF and MLP Neural Networks Technique in TSE

Document Type : applied

Authors

1 Assistant Prof., Business management, shahab danesh University, Qom, Iran

2 MSc.in Business Administration, Qom University, Qom, Iran

3 Assistant Prof., Business management Department, Qom University, Qom, Iran

Abstract

In this paper have been studied Financial Bankruptcy Risk Prediction based on accounting and market and hybrid models (combining the two above models) with the use of MLP and RBF techniques of neural networks and the results of these techniques are compared based on the mean square error index in the three mentioned models. The results show that the RBF neural network is more efficient than the MLP network in all three models (accounting, market and hybrid variables) and the accuracy of the hybrid model is more than the accounting and market models.
JEL: C45, G17, G33
How to cite this paper: atefatdoost, A., mahmoudi, M., & ramooz, N. (2017). Financial Bankruptcy Risk Prediction Based on Accounting, Market and Hybrid Models (Combination of Two Models)by using RBF and MLP Neural Networks Technique in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 320–339. (In Persian)

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References

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Journal of Risk modeling and Financial Engineering
Volume 2, Issue 3 - Serial Number 5
September 2017
Pages 320-339

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