{"title":"上市公司财务困境预测的遗传神经网络模型","authors":"W. Xinli","doi":"10.1109/ICIII.2011.124","DOIUrl":null,"url":null,"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.","PeriodicalId":229533,"journal":{"name":"2011 International Conference on Information Management, Innovation Management and Industrial Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Genetic Neural Network Model of Forecasting Financial Distress of Listed Companies\",\"authors\":\"W. Xinli\",\"doi\":\"10.1109/ICIII.2011.124\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":229533,\"journal\":{\"name\":\"2011 International Conference on Information Management, Innovation Management and Industrial Engineering\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Information Management, Innovation Management and Industrial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIII.2011.124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Information Management, Innovation Management and Industrial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIII.2011.124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic Neural Network Model of Forecasting Financial Distress of Listed Companies
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.