A hybrid neural network model based on improved PSO and SA for bankruptcy prediction

Fatima Zahra Azayite; Said Achchab

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arXiv · 1907.12179

A hybrid neural network model based on improved PSO and SA for bankruptcy prediction

Fatima Zahra Azayite·Said Achchab

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Abstract

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.

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q-fin.RM cs.LG cs.NE stat.ML

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Fatima Zahra Azayite, Said Achchab. 2019-07-16. A hybrid neural network model based on improved PSO and SA for bankruptcy prediction. https://arxiv.org/abs/1907.12179

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