Bankruptcy risk prediction models based on artificial
neural networks
Author:
Doina PRODAN-PALADE
JEL:
M41, C53, G33
DOI:
10.20869/AUDITF/2017/147/418
Keywords:
Artificial Neural Networks; backpropagation;
bankruptcy risk; overall liquidity ratio; overall solvency
ratio
Abstract:
The purpose of this research is to study the ability of
artificial neural networks to forecast the companies’ risk
of financial distress. We predicted the bankruptcy risk
using the associated financial ratios (overall liquidity
ratio and the overall solvency ratio) and two artificial
neural network models based on the backpropagation
algorithm. The proposed models were implemented and
tested using the PyBrain software and have been
applied to 55 companies listed on the Bucharest Stock
Exchange during 2010-2014. After a total of 19,944
iterations for the learning stage, the two algorithms
converged and the errors obtained during the tests
reached the fixed target. The empirical results showed
that the artificial neural network models are efficient and
reliable in detecting the risk of bankruptcy. The artificial
neural networks are very useful in economic analysis
when the complexity of data makes it difficult to
implement functions that proper describe the link
between economic variables. The use of the neural
networks method for predicting the risk of bankruptcy is
less common in Romania. This study intends to fill this
gap in the literature and we believe it could be of interest
not only for the companies listed on the stock exchange,
but also for investors, shareholders and banks.
Abstract(243KB)
Article(1040KB)