  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.
