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A neural network approach to the prediction of going concern status

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  • Hian Koh
  • Sen Tan

Abstract

The assessment of a firm's going concern status is not an easy task. To assist auditors, going concern prediction models based on statistical methods such as multiple discriminant analysis and logit/probit analysis have been explored with some success. This study attempts to look at a different and more recent approach—neural networks. In particular, a neural network model of the feedforward, backpropagation type was constructed to predict a firm's going concern status from six financial ratios, using a data set containing 165 non-going concerns and 165 matched going concerns. On an evenly distributed hold-out sample, the trained network model correctly predicted all 30 test cases. The results suggest that neural networks can be a promising avenue of research and application in the going concern area.

Suggested Citation

  • Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    • Handle: RePEc:taf:acctbr:v:29:y:1999:i:3:p:211-216
    DOI: 10.1080/00014788.1999.9729581
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    1. 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.
    2. Krishnagopal Menon & Kenneth B. Schwartz, 1987. "An empirical investigation of audit qualification decisions in the presence of going concern uncertainties," Contemporary Accounting Research, John Wiley & Sons, vol. 3(2), pages 302-315, March.
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    Cited by:

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    2. Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.
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    11. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
    12. 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.
    13. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    14. Nirosh Karuppu, 2009. "Evidence on Auditors Use of Business Continuity Models as an Analytical Procedure," Accounting & Taxation, The Institute for Business and Finance Research, vol. 1(1), pages 63-74.
    15. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    16. Yusuf Ali Al-Hroot, 2015. "The Influence Of Sample Size And Selection Of Financial Ratios In Bankruptcy Model Accuracy," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 13(1), pages 7-19, May.
    17. Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.
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