Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278306 
Year of Publication: 
2023
Series/Report no.: 
ECB Working Paper No. 2767
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad.
Subjects: 
China
financial risk
textual analysis
machine learning
topic modelling
LDA
JEL: 
C32
C65
E32
F44
G15
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-5509-6
Document Type: 
Working Paper

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