{"title":"财务危机预测问题的GreyART网络","authors":"M. Yeh, Haoxun Yang, Chia-Ting Chang","doi":"10.30016/JGS.200606.0006","DOIUrl":null,"url":null,"abstract":"This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the network are the historical data containing 18 different financial ratios of 54 healthy and 22 distressed Taiwan's listed electronic firms. In order to determine the best result the GreyART network can attain, a new performance index is developed. Simulation results show the one using 8 variables to generate only four clusters, 1 for healthy class and 3 for distressed class with corresponding classification hit rates of 94.12% and 93.55% for the training and test phases, respectively.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"9 1","pages":"43-49"},"PeriodicalIF":1.0000,"publicationDate":"2006-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"GreyART Network for Financial Distress Prediction Problem\",\"authors\":\"M. Yeh, Haoxun Yang, Chia-Ting Chang\",\"doi\":\"10.30016/JGS.200606.0006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the network are the historical data containing 18 different financial ratios of 54 healthy and 22 distressed Taiwan's listed electronic firms. In order to determine the best result the GreyART network can attain, a new performance index is developed. Simulation results show the one using 8 variables to generate only four clusters, 1 for healthy class and 3 for distressed class with corresponding classification hit rates of 94.12% and 93.55% for the training and test phases, respectively.\",\"PeriodicalId\":50187,\"journal\":{\"name\":\"Journal of Grey System\",\"volume\":\"9 1\",\"pages\":\"43-49\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2006-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Grey System\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.30016/JGS.200606.0006\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Grey System","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.30016/JGS.200606.0006","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
GreyART Network for Financial Distress Prediction Problem
This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the network are the historical data containing 18 different financial ratios of 54 healthy and 22 distressed Taiwan's listed electronic firms. In order to determine the best result the GreyART network can attain, a new performance index is developed. Simulation results show the one using 8 variables to generate only four clusters, 1 for healthy class and 3 for distressed class with corresponding classification hit rates of 94.12% and 93.55% for the training and test phases, respectively.
期刊介绍:
The journal is a forum of the highest professional quality for both scientists and practitioners to exchange ideas and publish new discoveries on a vast array of topics and issues in grey system. It aims to bring forth anything from either innovative to known theories or practical applications in grey system. It provides everyone opportunities to present, criticize, and discuss their findings and ideas with others. A number of areas of particular interest (but not limited) are listed as follows:
Grey mathematics-
Generator of Grey Sequences-
Grey Incidence Analysis Models-
Grey Clustering Evaluation Models-
Grey Prediction Models-
Grey Decision Making Models-
Grey Programming Models-
Grey Input and Output Models-
Grey Control-
Grey Game-
Practical Applications.