{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:32Z","timestamp":1777704932788,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate daily new infected numbers as an additional predictor to reflect the impact of COVID-19. We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19. Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG, enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models.<\/jats:p>","DOI":"10.3233\/jifs-232479","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T13:08:14Z","timestamp":1698757694000},"page":"12329-12339","source":"Crossref","is-referenced-by-count":4,"title":["Forecast stock price based on GRA-LoGo model of information filtering networks"],"prefix":"10.1177","volume":"45","author":[{"given":"Qingyang","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Informatics, Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramin","family":"Yahyapour","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232479_ref1","doi-asserted-by":"crossref","first-page":"115879","DOI":"10.1016\/j.eswa.2021.115879","article-title":"COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction","volume":"187","author":"Ronaghi","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-232479_ref2","doi-asserted-by":"crossref","first-page":"100326","DOI":"10.1016\/j.jbef.2020.100326","article-title":"Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns","volume":"27","author":"Al-Awadhi","year":"2020","journal-title":"Journal of Behavioral and Experimental Finance"},{"key":"10.3233\/JIFS-232479_ref3","doi-asserted-by":"crossref","first-page":"125605","DOI":"10.1016\/j.physa.2020.125605","article-title":"Construction of minimum spanning trees from financial returns using rank correlation","volume":"566","author":"Millington","year":"2021","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"10.3233\/JIFS-232479_ref4","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.physa.2014.09.011","article-title":"The maximum number of 3- and 4-cliques within a planar maximally filtered graph","volume":"417","author":"Birch","year":"2015","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"10.3233\/JIFS-232479_ref5","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.physa.2017.12.037","article-title":"Constructing financial network based on PMFG and threshold method","volume":"495","author":"Nie","year":"2018","journal-title":"Physica a-Statistical Mechanics and Its Applications"},{"key":"10.3233\/JIFS-232479_ref6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.physa.2014.09.011","article-title":"The maximum number of 3-and 4-cliques within a planar maximally filtered graph","volume":"417","author":"Birch","year":"2015","journal-title":"Physica a-Statistical Mechanics and Its Applications"},{"issue":"2","key":"10.3233\/JIFS-232479_ref7","first-page":"161","article-title":"Massara, T. 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