| The outbreak of the COVID-19 pandemic has caused our society to fall into a certain stagnation at the end of 2019.A-shares plummeted on February 3,2020,and the external crisis facing the stock market has increased dramatically.The impact of the COVID-19 pandemic on China’s stock market is quite different from the traditional stock market financial crisis,such as the stock market crash in China’s stock market in 2015.Compared with the stock market crash,the COVID-19 pandemic has a deeper and more lasting impact on the stock market,but the COVID-19 pandemic is predictable and controllable.Therefore,it is of great significance for future shocks caused by external risks by comparing China’s stock market under the COVID-19 pandemic during the stock market crash.This thesis explores the specific characteristics of stock market under the COVID-19 pandemic by comparing the stock market in the crisis period from the macro perspective of the overall structure of stock market network and the micro perspective of nodes in the network based on the CSI 300.Firstly,we obtain the relationship between different stocks through Granger causality test,and establishes a stock market network model in each periods.On the one hand,we analyze the differences in network topological characteristics across periods by comparing the static stock market networks of four periods(before the turbulence of 2015-2016,during the turbulence of2015-2016,before the COVID-19 pandemic of 2020-2021,and during the COVID-19 pandemic of 2020-2021).On the other hand,we use the sliding window algorithm to establish dynamic stock market networks and analyze the correlation in the topological characteristics of time-varying networks.Secondly,this thesis considers the important influence of nodes,and analyzes the characteristics of node influence distribution in different periods based on the static network model of four periods,and removes the largest influence nodes in different periods,simulating the network state of the stock market after being affected by risk.In addition,the stocks are classified by industry,the average influence of the industry in each period is obtained,and the industry influence in different periods and the influential stocks in the industry are analyzed.According to the analysis results of network topology characteristics,the impact of the COVID-19 pandemic on the CSI 300 is smaller than that during the the turbulence of 2015,and the stock market network before and after the COVID-19 pandemic is relatively stable.When multiple topological features in the network fluctuate violently in a short period of time,the stock market network is more prone to crisis.Moreover,according to the influence analysis of nodes,the results show that the distribution of node influence in different periods conforms to the power-law distribution,and the long-tail effect of node distribution during the turbulence of 2015 and the COVID-19 pandemic is more obvious,but there are more influential nodes in the turbulence of2015 than in the COVID-19 pandemic.After attacking through directed nodes,the loss of important nodes lead to different degrees of changes in the network in the four periods.The network structure during the turbulence of 2015 is the most affected,followed by the COVID-19 pandemic,and the number of network clusters increase in each period.From the industry sector level,Real estate and Finance are the most influential sectors in the period before and after the COVID-19 pandemic,which is basically consistent with China’s actual development situation.Affected by the COVID-19 pandemic and the full opening of China’s financial market,the influence of pharmaceutical and securities stocks has increased.The results of this thesis can provide a valuable references for regulators to guard against the risks of the stock market. |
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