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Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction

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  • Chris Charalambous

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  • Andreas Charitou

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  • Froso Kaourou

Abstract

This study compares the predictive performance of three neural network methods, namely the learning vector quantization, the radial basis function, and the feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and non-bankrupt US firms for the period 1983–1994. The results of this study indicate that the contemporary neural network methods applied in the present study provide superior results to those obtained from the logistic regression method and the backpropagation algorithm. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
  • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:403-425:10.1023/a:1019292321322
    DOI: 10.1023/A:1019292321322
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    Cited by:

    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Mattia Iotti & Giuseppe Bonazzi, 2018. "Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”," Sustainability, MDPI, Open Access Journal, vol. 10(4), pages 1-23, March.
    3. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    4. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    5. Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
    6. Manuel Castejón-Limas & Joaquín Ordieres-Meré & Ana González-Marcos & Víctor González-Castro, 2011. "Effort estimates through project complexity," Annals of Operations Research, Springer, vol. 186(1), pages 395-406, June.
    7. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    8. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    9. Jacek Welc, 2016. "Empirical Safety Thresholds for Liquidity and Indebtedness Ratios on the Polish Capital Market," European Financial and Accounting Journal, University of Economics, Prague, vol. 2016(3), pages 39-52.
    10. Philippe Jardin, 0. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 0, pages 1-36.
    11. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    12. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    13. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    14. Jane Haider & Zhirong Ou & Stephen Pettit, 2019. "Predicting corporate failure for listed shipping companies," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 415-438, September.
    15. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.

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