Abstract
The intent of this study is to compare the predictive strength of two neural network models —a standard feedforward neural network trained using a genetic algorithm and the more recently developed collapsible neural network using genetic algorithms — to determine corporate financial distress (bankruptcy). This research expands existing bankruptcy models on several levels. First, it utilizes financial ratios based on the cash flow statements in addition to the usual ratios based on balance sheets and income statements. It also incorporates qualitative data that may indicate financial distress such as change in management and change in auditor. Lastly, it provides a promising algorithm using collapsible neural networks to provide parsimony in the model structure and insight into the process used by the neural network in its decision making process.
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Booker, Q., Johnson, J.D., Dorsey, R.E. (1998). Predicting Corporate Financial Distress Using Quantitative and Qualitative Data: A Comparison of Standard and Collapsible Neural Networks. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_27
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_27
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