Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influences significantly the model's accuracy and considering the problem of local minima, we propose a hybrid ANN based on variables selection techniques. Moreover, we evolve the convergence of Particle Swarm Optimization (PSO) by proposing a training algorithm based on an improved PSO and Simulated Annealing. A comparative performance study is reported, and the proposed hybrid model shows a high performance and convergence in the context of missing data.
@article{arxiv.1907.12179,
title = {A hybrid neural network model based on improved PSO and SA for bankruptcy prediction},
author = {Fatima Zahra Azayite and Said Achchab},
journal= {arXiv preprint arXiv:1907.12179},
year = {2019}
}