A neural network approach to the prediction of going concern status

Hian Koh and Sen Tan

Accounting and Business Research, 1999, vol. 29, issue 3, 211-216

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.

Date: 1999
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DOI: 10.1080/00014788.1999.9729581

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