【摘要】This talk aims to develop a novel network characteristic indicator to rapidly and accurately detect the financial crisis. Specifically, we select the daily closing price of stocks spanning from 2006 to 2020 in China's A-share market to establish a series of complex networks, and extract Laplacian Energy measure as a new network indicator. By employing the method of seasonal-trend decomposition procedure based on loess, the proposed indicator successfully detects the global financial crisis, the Eurozone debt crisis, the Chinese stock market crash, the Sino-US trade friction and the COVID-19 pandemic. Furthermore, compared with the traditional topological indicators (e.g., global efficiency, average clustering coefficient, characteristic path length and network density), the proposed indicator demonstrates the outstanding characteristics of higher identification accuracy, wider application range and faster response speed. Lastly, the robustness of the Laplacian Energy measure in the financial crisis detection is further confirmed in the US, UK, German, French and Spanish stock markets.

 

【报告人简介】 黄创霞，博士，教授，博士生导师，长沙理工大学副校长，主要从事微分方程与动力系统、复杂网络与金融风险管理等研究。主持国家自科面上项目2项、青年项目1项，湖南省杰青等省部级课题10余项，以第一作者在Journal of Differential Equations, Nonlinearity, International Review of Financial Analysis, European Journal of Finance 等期刊发表SCI/SSCI收录论文50余篇，获湖南省政府特殊津贴（2019）, 湖南省“芙蓉学者奖励计划”青年学者人选（2019），湖南省第二届“优秀研究生导师”（2021），入选科睿唯安“全球高被引科学家”榜单（2021）、爱思唯尔(Elsevier)中国高被引学者榜单（2020，2021）。