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

Fatima Zahra Azayite and Said Achchab

Papers from arXiv.org

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.

Date: 2019-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-ore
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Published in International Journal of Computer Science Issues, Vol 16, Issue 1, January 2019

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1907.12179

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