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How to rapidly and accurately detect the financial crisis is one of the fundamental and challenging problems in the field of financial risk management. This paper aims to develop a novel network characteristic indicator to deal with this issue. 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.

Can financial crisis be detected? Laplacian energy measure