Title :
Genetic Neural Network Model of Forecasting Financial Distress of Listed Companies
Author_Institution :
Sch. of Econ. & Manage., North China Electr. Power Univ., Baoding, China
Abstract :
This paper uses the global optimization of genetic algorithm to construct a genetic neural network model (GANN) forecasting listed company financial crisis. The model optimizes input variables of neural network model forecasting financial crisis. Forecasting of financial distress of listed companies in Shanghai and Shenzhen A share markets indicates that this model bears a better ability to predict financial distress compared with ANN model.
Keywords :
economic forecasting; financial management; genetic algorithms; neural nets; GANN forecasting; Shanghai; Shenzhen; financial distress forecasting; genetic algorithm; genetic neural network model; global optimization; listed company financial crisis; neural network model forecasting; Analytical models; Artificial neural networks; Companies; Computational modeling; Forecasting; Predictive models; Training; Financial distress; Genetic algorithm; Neural network;
Conference_Titel :
Information Management, Innovation Management and Industrial Engineering (ICIII), 2011 International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-61284-450-3
DOI :
10.1109/ICIII.2011.124
