2024-03-29T14:31:26Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1600832024-02-19T11:50:09Zcom_10234_43662com_10234_9col_10234_43643
00925njm 22002777a 4500
dc
Cleofás Sánchez, Laura
author
García, Vicente
author
Marqués Marzal, Ana Isabel
author
Sánchez Garreta, Josep Salvador
author
2016
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.
1568-4946
1872-9681
http://hdl.handle.net/10234/160083
http://dx.doi.org/10.1016/j.asoc.2016.04.005
Associative memory
Neural network
Financial distress
Bankruptcy
Credit risk
Financial distress prediction using the hybrid associative memory with translation