Genetically optimized neural network classifiers for bankruptcy prediction-an empirical study

The use of financial statement data to predict the future financial health of an economic entity is generally considered a complex problem where non-linear pattern recognition methods such as neural networks (NN) can provide a performance advantage. In fact, bankruptcy prediction has emerged as a popular benchmark for neural network performance. However, the use of neural networks in bankruptcy prediction as well as in business applications in general has been hindered by the fact that large numbers of parameters have to be fine-tuned before NN can be used successfully while no analytical solution for this optimization problem exists. Our study uses a large sample of real-world financial statements from German corporations. The performance of the neural networks is compared on the basis of the beta-error (misclassification of solvent companies), while the (more costly) alpha-error (misclassification of insolvent companies) is kept constant