Bankruptcy prediction models: probabilistic neural networks versus discriminant analysis and backpropagation neural networks1

The purpose of this paper is present probabilistic neural networks (PNN) as an alternative quantitative technique to both linear discriminant analysis (LDA) and backpropagated neural networks (BPNN) for forecasting corporate solvency. Although traditionally this task has been approached with rather simpler linear techniques such as LDA, there is increasing empirical evidence of the superiority of BPNN models to LDA in terms of its ability to accurately forecast corporate financial health. However, some recent work seems to indicate the superiority in forecasting performance displayed by BPNN may not be as significant as first thought. This paper suggests an alternative technique, probabilistic neural networks, for the assessment of corporate financial distress which can produce superior performance to both LDA and BPNN's while not suffering from the shortcomings found in BPNN's.