Abstract
In this study we compared the classification accuracy rates of neural networks to those from ordinal logit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal logit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial distress.
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Zurada, J.M., Foster, B.P., Ward, T.J., Barker, R.M. (1997). A Comparison of the Ability of Neural Networks and Logit Regression Models to Predict Levels of Financial Distress. In: Wojtkowski, W.G., Wojtkowski, W., Wrycza, S., Zupančič, J. (eds) Systems Development Methods for the Next Century. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5915-3_24
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DOI: https://doi.org/10.1007/978-1-4615-5915-3_24
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