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Second, according to the difference between the differential and the difference, the nonlinear delay grey prediction model is established. Next, the model parameters are estimated and the solving steps are obtained. The nonlinear parameters and delay time are optimized by the particle swarm optimization algorithm. Finally, the new model is applied to the prediction of the Shanghai stock market and the Shenzhen stock market closing indexes; the results show that the new model can effectively predict stock prices, which is much better than the existing four grey models and a time series model.<\/jats:p>","DOI":"10.3233\/jifs-210726","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T10:59:05Z","timestamp":1627037945000},"page":"3395-3413","source":"Crossref","is-referenced-by-count":3,"title":["A novel grey model of impulse delay and its application in forecasting stock price"],"prefix":"10.1177","volume":"41","author":[{"given":"Huiming","family":"Duan","sequence":"first","affiliation":[{"name":"School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"given":"Jiangbo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"given":"Siqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"given":"Chenglin","family":"He","sequence":"additional","affiliation":[{"name":"School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210726_ref1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/B978-0-444-53683-9.00006-2","article-title":"Forecasting Stock Returns","volume":"2","author":"Rapach","year":"2013","journal-title":"Handbook of Economic Forecasting"},{"issue":"1","key":"10.3233\/JIFS-210726_ref2","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s10614-017-9691-7","article-title":"Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series","volume":"54","author":"Paola","year":"2019","journal-title":"Computational Economics"},{"issue":"4","key":"10.3233\/JIFS-210726_ref3","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.jksuci.2015.06.002","article-title":"Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach","volume":"29","author":"Rout","year":"2015","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"key":"10.3233\/JIFS-210726_ref4","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.procs.2016.07.077","article-title":"The enhanced classification for the stock index prediction","volume":"91","author":"Hyeuk","year":"2016","journal-title":"Procedia Computer Science"},{"key":"10.3233\/JIFS-210726_ref5","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1016\/j.eswa.2014.10.031","article-title":"Predicting stock market index using of machine learning techniques","volume":"42","author":"Jigar","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-210726_ref6","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.eswa.2018.08.003","article-title":"Decision-making for financial trading: A fusion approach of machine learning and portfolio selection","volume":"115","author":"Paiva","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-210726_ref7","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-540-72432-2_25","article-title":"M-factor high order fuzzy time series forecasting for road accident data: analysis and design of intelligent systems using soft computing techniques","volume":"41","author":"Jilani","year":"2007","journal-title":"Adv Soft Computer"},{"key":"10.3233\/JIFS-210726_ref8","unstructured":"Deng J.L. , Foundations of Grey Theory. 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