Early warning of systemic risks of financial institutions based on complex network theory
TSAI
1. Complex network theory: unlocking the password of financial institution association
Complex network theory breaks through the limitation of viewing financial institutions in isolation in traditional financial analysis, and regards the entire financial system as a complex network composed of many interconnected financial institutions. In this network, each financial institution is a node, and the business transactions, capital flows, credit relationships, etc. between them constitute the connection edges between nodes.
The strength of these connection edges is not static, but dynamically adjusted with the changes in the business scale and cooperation degree between financial institutions. For example, the larger the scale of interbank lending between two banks and the longer the cooperation time, the higher the connection strength in the association network. The connection edge also has directionality, such as the debt relationship of one bank to another bank, which determines the one-way transmission characteristics of information and risk on this connection.
2. Construction of association network model: comprehensive mapping of financial ecology
The financial institution association network model constructed by TSAI maps the financial ecology in an all-round way through multi-dimensional data collection and integration. In terms of data sources, it covers internal data such as the balance sheet, transaction flow, credit rating report of financial institutions, as well as external data such as macroeconomic data and industry regulatory data.
In terms of node construction, not only traditional financial institutions such as banks, securities, and insurance are included, but also emerging financial technology companies and Internet financial platforms are included to ensure that the network model can fully reflect the overall picture of the financial system. In the construction of connection edges, the existence and strength of connection edges are determined based on the equity relationship, capital lending relationship, business cooperation relationship, etc. between financial institutions.
For example, if a bank holds part of the equity of another securities company, then in the associated network, there is a connection edge based on the equity relationship between the two institutions, and its strength can be determined according to the equity ratio; if there is reinsurance business cooperation between two insurance companies, then a connection edge based on the business cooperation relationship will be formed between them, and the strength depends on the scale and frequency of business cooperation.
3. Risk warning indicator analysis: Accurately capture risk signals
Connection strength analysis: Connection strength is a key indicator to measure the closeness of the connection between financial institutions. When the connection strength between a financial institution and many other institutions is generally enhanced, it means that its influence and importance in the financial network are constantly increasing, and it also means that once the institution encounters risks, its spread and impact will be greater.
Information dissemination path analysis: The information dissemination path in the associated network of financial institutions is complex and diverse. TSAI can predict the direction and speed of risk propagation in advance by analyzing the information propagation path. When it is found that the information of a risk event is rapidly propagating along a specific path in the network and the number of nodes involved is increasing, an early warning signal can be issued in time. For example, if a small financial institution has risk information about a broken capital chain, through the analysis of the associated network propagation path, it is found that the information is rapidly spreading to several large banks with which it has a capital lending relationship, and the propagation speed is far beyond the normal level, which indicates that systemic risks are accumulating and immediate measures need to be taken to prevent them.
Based on the analysis of connection strength and information propagation path, the early warning system issued a systemic risk signal two weeks in advance. After receiving the warning, investors adjusted their asset allocation in time, reduced their investment in affected financial institutions, and increased their allocation to safe-haven assets. According to statistics, by referring to TSAI's risk warning, investors successfully avoided about 30% of potential losses.
The systemic risk early warning system of financial institutions constructed by TSAI using complex network theory provides a strong guarantee for the stable operation of the financial market and the asset security of investors. Through in-depth analysis of the financial institution's network, we can achieve early insight into systemic risks and accurate early warning, helping investors to adjust strategies and avoid risks in a complex and ever-changing financial market. With the continuous development and improvement of technology, we believe that this early warning system will play a more important role in the field of financial risk management.