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.