A hybrid neural network model based on improved PSO and SA for bankruptcy prediction
This work addresses bankruptcy prediction for investors and decision-makers, but it is incremental as it builds on existing methods with hybrid improvements.
The paper tackled bankruptcy prediction by proposing a hybrid artificial neural network model that integrates variable selection and an improved training algorithm combining particle swarm optimization and simulated annealing, achieving high performance and convergence, particularly in handling missing data.
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.