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A hybrid genetic model for the prediction of corporate failure

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  • Anthony Brabazon
  • Peter Keenan

Abstract

This study examines the potential of a neural network (NN) model, whose inputs and structure are automatically selected by means of a genetic algorithm (GA), for the prediction of corporate failure using information drawn from financial statements. The results of this model are compared with those of a linear discriminant analysis (LDA) model. Data from a matched sample of 178 publicly quoted, failed and non-failed, US firms, drawn from the period 1991 to 2000 is used to train and test the models. The best evolved neural network correctly classified 86.7 (76.6)% of the firms in the training set, one (three) year(s) prior to failure, and 80.7 (66.0)% in the out-of-sample validation set. The LDA model correctly categorised 81.7 (75.0)% and 76.0 (64.7)% respectively. The results provide support for a hypothesis that corporate failure can be anticipated, and that a hybrid GA/NN model can outperform an LDA model in this domain. Copyright Springer-Verlag Berlin/Heidelberg 2004

Suggested Citation

  • Anthony Brabazon & Peter Keenan, 2004. "A hybrid genetic model for the prediction of corporate failure," Computational Management Science, Springer, vol. 1(3), pages 293-310, October.
    • Handle: RePEc:spr:comgts:v:1:y:2004:i:3:p:293-310
    DOI: 10.1007/s10287-004-0017-6
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    Citations

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    Cited by:

    1. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    2. Peel Michael J., 2016. "Owner-Managed UK Corporate Start-Ups: An Exploratory Study of Financing and Failure," Entrepreneurship Research Journal, De Gruyter, vol. 6(4), pages 345-367, October.
    3. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.
    4. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    5. Amendola, Alessandra & Restaino, Marialuisa & Sensini, Luca, 2015. "An analysis of the determinants of financial distress in Italy: A competing risks approach," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 33-41.
    6. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    7. Fayçal Mraihi & Inane Kanzari, 2019. "Predicting financial distress of companies: Comparison between multivariate discriminant analysis and multilayer perceptron for Tunisian case," Working Papers 1328, Economic Research Forum, revised 21 Aug 2019.
    8. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
    9. Luca Sensini, 2016. "An Empirical Analysis of Financially Distressed Italian Companies," International Business Research, Canadian Center of Science and Education, vol. 9(10), pages 75-85, October.
    10. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
    11. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    12. Der-Jang Chi & Chien-Chou Chu, 2021. "Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    13. Piasecki Krzysztof & Wójcicka-Wójtowicz Aleksandra, 2017. "Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 129-143, December.
    14. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.

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