乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:55:30Z","timestamp":1740149730087,"version":"3.37.3"},"reference-count":75,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T00:00:00Z","timestamp":1539907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71671193; 71371020"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.<\/jats:p>","DOI":"10.3390\/e20100805","type":"journal-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T14:08:02Z","timestamp":1539958082000},"page":"805","source":"Crossref","is-referenced-by-count":9,"title":["Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market"],"prefix":"10.3390","volume":"20","author":[{"given":"Qiuna","family":"Lv","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beihang University, Beijing 100083, China"}]},{"given":"Liyan","family":"Han","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beihang University, Beijing 100083, China"}]},{"given":"Yipeng","family":"Wan","sequence":"additional","affiliation":[{"name":"Math Club Center, Acalanes High School, Lafayette, CA 94549, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0193-6735","authenticated-orcid":false,"given":"Libo","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Finance, Central University of Finance and Economics, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jbankfin.2015.02.007","article-title":"Economic links and credit spreads","volume":"55","author":"Signori","year":"2015","journal-title":"J. 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