Bankruptcy risk prediction models based on artificial neural networks
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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Chamber of Financial Auditors of Romania
2017-08-01
|
| Series: | Audit Financiar |
| Subjects: | |
| Online Access: |
http://revista.cafr.ro/temp/Article_9543.pdf
|
| _version_ | 1819180735422005248 |
|---|---|
| author | Doina PRODAN-PALADE |
| author_facet | Doina PRODAN-PALADE |
| author_sort | Doina PRODAN-PALADE |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T22:19:04Z |
| format | Article |
| id | doaj.art-511566b392f943c096cbe64782ac4ffa |
| institution | Directory Open Access Journal |
| issn | 1844-8801 |
| language | English |
| last_indexed | 2024-12-22T22:19:04Z |
| publishDate | 2017-08-01 |
| publisher | Chamber of Financial Auditors of Romania |
| record_format | Article |
| series | Audit Financiar |
| spelling | doaj.art-511566b392f943c096cbe64782ac4ffa2022-12-21T18:10:42ZengChamber of Financial Auditors of RomaniaAudit Financiar1844-88012017-08-011514741842910.20869/AUDITF/2017/147/4189543Bankruptcy risk prediction models based on artificial neural networksDoina PRODAN-PALADE0 Alexandru Ioan Cuza University, Iasi, Romania 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. http://revista.cafr.ro/temp/Article_9543.pdf Artificial Neural Networks; backpropagation; bankruptcy risk; overall liquidity ratio; overall solvency ratio |
| spellingShingle | Doina PRODAN-PALADE Bankruptcy risk prediction models based on artificial neural networks Audit Financiar Artificial Neural Networks; backpropagation; bankruptcy risk; overall liquidity ratio; overall solvency ratio |
| title | Bankruptcy risk prediction models based on artificial neural networks |
| title_full | Bankruptcy risk prediction models based on artificial neural networks |
| title_fullStr | Bankruptcy risk prediction models based on artificial neural networks |
| title_full_unstemmed | Bankruptcy risk prediction models based on artificial neural networks |
| title_short | Bankruptcy risk prediction models based on artificial neural networks |
| title_sort | bankruptcy risk prediction models based on artificial neural networks |
| topic | Artificial Neural Networks; backpropagation; bankruptcy risk; overall liquidity ratio; overall solvency ratio |
| url |
http://revista.cafr.ro/temp/Article_9543.pdf
|
| work_keys_str_mv | AT doinaprodanpalade bankruptcyriskpredictionmodelsbasedonartificialneuralnetworks |