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Bankruptcy risk prediction models based on artificial neural networks

Author

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  • Doina PRODAN-PALADE

    (Alexandru Ioan Cuza University, Iasi, Romania)

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.

Suggested Citation

  • Doina PRODAN-PALADE, 2017. "Bankruptcy risk prediction models based on artificial neural networks," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 418-418.
    • Handle: RePEc:aud:audfin:v:15:y:2017:i:147:p:418
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    References listed on IDEAS

    as
    1. Birol Yildiz & Ari Yezegel, 2010. "Fundamental Analysis With Artificial Neural Network," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(1), pages 149-158.
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    4. Laura Maria BADEA (STROIE), 2013. "Supporting Management Decisions by Using Artificial Neural Networks for Exchange Rate Prediction," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 12(4), pages 578-594, December.
    5. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    6. H Peyton Young & Paul Glasserman, 2013. "How Likely is Contagion in Financial Networks?," Economics Series Working Papers 642, University of Oxford, Department of Economics.
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    Cited by:

    1. Bogdan POPA, 2022. "Measuring the Risk of Bankruptcy in the Romanian Economy. Developments and Perspectives," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(24), pages 91-104, November.

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    More about this item

    Keywords

    Artificial Neural Networks; backpropagation; bankruptcy risk; overall liquidity ratio; overall solvency ratio;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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