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Abstract
Classification and prediction of bankruptcy of businesses is a widely-addressed topic at the international level, but there are few studies of this type applied to Chilean companies. In this context, the objective of this research is to identify which model classifies and predicts bankruptcy of businesses in Chile with the highest degree of reliability. To this end, we compare three commonly used models: multiple-discriminant analysis (MDA), logistic regression (LOGIT), and neural networks, which use different financial indexes, macroeconomic variables, and other control variables. The models were applied to a sample of 98 randomly selected companies with unrestricted lines of business, 49 bankrupt and 49 not bankrupt. The result of the research shows that while the neural networks model was superior, the MDA model as well as the LOGIT, with respect to classification and prediction, all require other tools to determine the optimal set of variables to use.
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