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.
Highlights
A complex network as a viable alternative is widely utilized in the financial system [1,2]
Market dynamics [11,16,30], paper employs local indicatorssignificant as controlled variables inwith the Considering the localthis characteristics of network networks containing information analysis of the network entropy effect on market returns and trading
In order to ensure the robustness of the regression results, and to examine whether network entropy provides a different perspective from the local network indicators or macro factors, we introduced four groups of local network indicators, and economic policy uncertainty (EPU) and volatility index (VIX) as controlled variables in the regressions to test Hypothesis 2: Tradt = α + βentt + θzt + μt where zt = ( Dt, ACCt, G1t, G2t, EPUt, V IXt ); Dt denotes a variable of diameter; ACCt denotes a variable of average closeness centrality; G1 represents a variable of average degree or average clustering coefficient, G2 denotes a variable of average betweenness centrality or average path length; θ is a 6 × 1 coefficient vector
Summary
A complex network as a viable alternative is widely utilized in the financial system [1,2]. This proposal provides a powerful framework for understanding and modeling the static topology of interactions among stock markets. Initial research mainly focused on the topological analysis of static networks. Štefan Lyócsa et al [12] suggested that a dynamic conditional correlation approach was available for understanding the dynamic evolution mechanisms of a stock-market structure. Following this idea, some researchers considered the dynamic conditional correlation
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