Abstract
The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China’s stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a-shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China’s stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long-term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems.
Highlights
The stock market is a typical complex system with multiple stock prices fluctuating from equilibrium to deviation and to equilibrium again
It can2017 be seen that the correlation coefficient between average weight and Chinese Volatility index (VIX) is 0.4763 during and is more sensitive, which proves the effectiveness of the systemic risk index derived from the interval since the Chinese VIX launched
A complex network provides an important tool for the study of the stock market, which is a self-organizing complex system with multi-agent interactions
Summary
The stock market is a typical complex system with multiple stock prices fluctuating from equilibrium to deviation and to equilibrium again. In former studies, the capital asset pricing model (CAPM) framework was usually used to analyze financial systematic risks as a basic theory [5,6,7,8]. Most studies on systematic risk are based on Beta values This theory is widely adopted, it usually comes with a number of hypotheses, such as homogenous investors in capital markets. A complex network, which is based on physics and mathematics theory, can tackle complicated practical problems [11] It is especially suitable for modeling, analysis, and calculation in complex finance systems [12]. A dynamic complex network of individual stocks in China’s stock market is constructed in this paper to measure the dynamic systemic risk of China’s stock market.
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