Financial distress prediction using the hybrid associative memory with translation

• We explore the hybrid associative memory with translation for default prediction.
• We analyze the behavior of this neural network under the presence of class imbalance.
• We study how the class overlapping affects the performance of the associative memory.
• We compare its performance with that of other prediction models.
• The associative memory is the best model, especially to predict the default cases.

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.

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