Genetic Neural Network Based Financial Distress Prediction

Abstract:

Enterprise financial distress prediction based on neural network has some disadvantages, such as complex structure, slow convergence rate and easily falling into local minimum points. The paper presents the genetic neural network based enterprise financial distress prediction. Firstly, the structural parameters of neural network model are encoded and connected into gene sequence to obtain an individual. A certain number of individuals make up a population. Secondly, after the reproduction, crossover and mutation operations upon the population, the best individual, that is the optimal structure parameters of neural network model, is obtained. Finally, the neural network model with the optimal structure parameters is trained by the training samples and the trained neural network model can realize enterprise financial distress prediction. The testing results show that the method achieves higher training speed and lower error rate.

• Periodical: Advanced Materials Research (Volume 933)
• Pages: 921-925
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.933.921
• Online since: May 2014
• Authors: Xin Yun Liu*, Heng Jun Liu
• Keywords: Financial Distress Prediction, Genetic Algorithm (GA), Neural Network (NN)

References

[1] Altman E. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 1968, 23(4): 589-610.

DOI: 10.1111/j.1540-6261.1968.tb00843.x

[2] Martin D. Early warning of bank failure: a logit regression approach. Journal of Banking and Finance, 1977, 1(3): 249-276.

[3] Wu Desheng , Liang Liang, Yin Yin. A comparison of each models ability to warn for financial distress. Journal of Industrial Engineering and Engineering Management, 2004, 18(2): 105-108.

[4] Coats P, Fant L. Recognizing financial distress patterns using a neural network tool. Financial Management, 1993, 22(3): 142-155.

DOI: 10.2307/3665934

[5] Malhotra R, Malhotra D K. Differentiating between good credits and bad credits using neural-fuzzy systems. European Journal of Operational Research, 2002, 136(1): 190-211.

DOI: 10.1016/s0377-2217(01)00052-2

[6] Liang L, Wu D S. An application of pattern recognition on scoring Chinese financial conditions based on back propagation neural network. Computers & Operations Research, 2005, 32(5): 1115-1129.

DOI: 10.1016/j.cor.2003.09.015

[7] Desai V, Crook J, Overstreet G. A comparison of neural networks and linear scoring models in the union environment. European Journal of Operational Research, 1996, 95(1): 24-37.

DOI: 10.1016/0377-2217(95)00246-4