This paper presents an alternative technique for financial distress prediction systems.
The method is based on a type of neural network, which is called hybrid
associative memory with translation. While many different neural network architectures
have successfully been used to predict credit risk and corporate failure, the
power of associative memories for financial decision-making has not been explored
in any depth as yet. The performance of the hybrid associative memory with translation
is compared to four traditional neural networks, a support vector machine
and a logistic regression model in terms of their prediction capabilities. The experimental
results over nine real-life data sets show that the associative memory here
proposed constitutes an appropriate solution for bankruptcy and credit risk prediction,
performing significantly better than the rest of models under class imbalance
and data overlapping conditions in terms of the true positive rate and the geometric
mean of true positive and true negative rates.This work has partially been supported by the Mexican CONACYT through the Postdoctoral Fellowship Program [232167], the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062] and the Mexican PRODEP [DSA/103.5/15/7004]. We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially
