Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market

Abstract

This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk.

Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network

“…The dynamic conditional correlations method could be used to study network linkage effects of financial market, especially in stock market. By using the GMM model, Qiao discussed the influences of inner nodes in different positions on stock returns and found that financial risks were easier to transmit from the central area to the marginal area of network [70].…”

“…By means of complex network theory, many scholars explain microscopic mechanisms and dynamic characteristics of some practical issues in society. For example, complex network can be used to reveal the transmission mechanism of computer viruses [70][71][72], the spread process of epidemics [73,74], and the diffusion intensity of rumor [72,75]. For the researches on dynamics of complex network, many research achievements are based on the traditional model of virus spread, such as epidemic models.…”

Investigating the Evolution of Linkage Dynamics among Equity Markets Using Network Models and Measures: The Case of Asian Equity Market Integration

“…Empirical studies in the past have provided evidence that future returns of portfolios are considerably impacted by the present and the future level of interdependence amongst the securities constituting this portfolio. Essentially, the level of the intrinsic correlation risk is represented by the closeness for stocks [73]. To be specific, the securities possessing the highest linkages in the equity network acquire the maximum value of expected return amongst the central nodes in the network.…”

“…Therefore, it is a reasonable expectation by the investors that securities located centrally should deliver relatively higher returns on investments, as they perceive this as a premium for the enhanced level of contagion risk associated with such securities. Hence, the security comovements as reflected in equity networks play a significant part in the determination of the mechanisms of asset pricing [73]. Taking all this into consideration, international portfolios constituted by selected peripheral markets would possess relatively lesser risk and higher returns than portfolios constituted by selected centrally-located markets (in which the network centrality measures quantify the centrality or peripherality of the market).…”

Trend Analysis of Global Stock Market Linkage Based on a Dynamic Conditional Correlation Network

“…Subsequently, network science has been applied in many fields, such as sociology (Porat, Benguigui 2016), biology and medicine (Wang et al 2016;Huang et al 2017), physics (Sui et al 2016), information dissemination (Carley et al 2016;Park et al 2016), interdisciplinary knowledge exchange (Shan et al 2014), technology integration (Lee et al 2014), and art design (Jiang 2016). Almost at the same time, the application of complex network analysis were introduced into financial markets (Mantegna 1999) and has been widely applied to many fields as macroeconomic indices (Gao et al 2013), business cycles (Caraiani 2013), stock markets (Hwang et al 2016;Brida et al 2016;Coletti 2016;Majapa, Gossel 2016;Qiao et al 2016), foreign exchange (Naylor et al 2007;Brida, Risso 2010) and financial market risk (Huang et al 2016).…”