Radial Basis Function in Artificial Neural Network for Prediction of Bankruptcy

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

doi:10.5539/ibr.v6n8p121

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.

Highlights

In one of the first academic studies of bankruptcy theory, bankruptcy has been defined as a company’s unprofitability which increases the probability of the company’s inability to repay its principal debt and its interest (Gordon, 1971)
Kim and Gu’s study (2006) deals with the comparison of performance accuracy to predict bankruptcy of two models of multiple discriminant analysis and logit analysis using financial data of 36 bankrupt and non-bankrupt companies from 1986 to 1998. 12 independent variables have been used including financial ratios and the results show that logit model’s performance accuracy to predict bankruptcy has been 93% which is the same as that of multiple discriminant analysis and they both have had similar performance
Hypothesis two is evaluated by McNemar and Fisher’s exact statistical tests and the result is as follows: As it is perceived in table 2, the valid values of dependent variable predicted by 95% confidence interval are not independent of one another

Summary

Introduction

In one of the first academic studies of bankruptcy theory, bankruptcy has been defined as a company’s unprofitability which increases the probability of the company’s inability to repay its principal debt and its interest (Gordon, 1971) In another definition, bankruptcy is considered a situation in which a company’s cash flows are less than its total interest costs of long-term debt (Whitaker, 1999). In one of the first academic studies of bankruptcy field, bankruptcy has been defined as company’s unprofitability which increases the probability of the company’s inability to repay its principal debt and its interest (Gordon, 1971) Failure in repaying debts happens when the proportion of a company’s assets and debts wipes out (Chen & Du, 2009) and the company fails to repay its debts

Methods

Results

Conclusion