Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Abstract

The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

Gatekeeper Networks, Knowledge Inertia, and Knowledge Performance in Business Incubators

“…In addition, researches on gatekeeper networks are also scattered among global R&D networks (Kim & Park, 2010), hyperlinked networks (Barnett et al, 2017), supplier networks (Van Der Ven, 2018, multilayer time series networks (Wan et al, 2020), dynamic business networks (Poblete et al, 2022), and collaborative networks (Zacharias et al, 2021).…”

“…Researches on gatekeeper functions have focused on knowledge acquisition externally and knowledge diffusion internally (Le Gallo & Plunket, 2020;Meissner & Shmatko, 2017;Vermeulen & Pyka, 2017). Researches on gatekeeper networks are distributed among knowledge networks (Yu et al, 2022), business networks (Díez-Vial & Montoro-Sánchez, 2020), regional innovation networks (Françoso & Vonortas, 2023), global R&D networks (Kim & Park, 2010), hyperlinked networks (Barnett et al, 2017), supplier networks (Van Der Ven, 2018), multilayer time series networks (Wan et al, 2020), dynamic business networks (Poblete et al, 2022), collaborative networks (Zacharias et al, 2021), and so on.…”

The Impact of China Economic Policy Uncertainty on CSI 300: An Analysis of the Mediating Effect of Investor Sentiment

“…It includes 300 A-share stocks with relatively large market capitalization and good liquidity. The CSI300 is an important benchmark in the Chinese A-share market and is widely used to gauge the overall performance of the Chinese stock market [16]. Therefore, we select the CSI300 as the variable representing stock market performance.…”

The Spread of Information in Virtual Communities

“…A standard way to measure node i 0 's importance in a network is by the extent of the infection that begins at i 0 [39]. To this end, we define the information-spreading rate of a node i 0 as C(i 0 ) � n i 0 /n, where n i 0 is the number of infected nodes at the end of the spreading process and n is the total number of nodes.…”