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To analysis dynamic early-warning of operational risks, Chinese manufacturing industry publicly listed companies were selected as research object. A measure model of early-warning index of operational risks in Chinese manufacturing industry listed companies based on grey Kalman Filter was constructed. With the early-warning results of sample listed companies in manufacturing industry through calculating process noise covariance, measurement noise covariance and Kalman gain matrix; thirdly, a model of investors\u2019 reactions was designed to test correctness of early-warning index of operational risks. Results demonstrate that, for the case study, it can get precise result of early-warning results by using Kalman filter, and investors react negatively towards the operational risks. The conclusions contribute to prior literatures on the dynamic evaluation of early-warning of operational risks by providing operational processes.<\/jats:p>","DOI":"10.3233\/jifs-179161","type":"journal-article","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T12:33:41Z","timestamp":1559651621000},"page":"5801-5808","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent early-warning analysis of operational risks based on grey Kalman filter"],"prefix":"10.1177","volume":"37","author":[{"given":"Ying","family":"Peng","sequence":"first","affiliation":[{"name":"School of Management, Wuhan University of Technology, Wuhan, China"},{"name":"Business School, Jianghan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youtang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Management, Wuhan University of Technology, Wuhan, 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