A. E. Bouchti, Younes Tribis, Tarik Nahhal, C. Okar
{"title":"Forecasting Financial Risk using Quantum Neural Networks","authors":"A. E. Bouchti, Younes Tribis, Tarik Nahhal, C. Okar","doi":"10.1109/ICDIM.2018.8847063","DOIUrl":null,"url":null,"abstract":"There has been enormous attention in quantum algorithms for reinforcing machine learning (ML) algorithms. In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and we build a new financial risk forecasting model. We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDIM.2018.8847063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
There has been enormous attention in quantum algorithms for reinforcing machine learning (ML) algorithms. In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and we build a new financial risk forecasting model. We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).