Using machine learning to measure financial risk in China

Alexander Al-Haschimi, Apostolos Apostolou, Andres Azqueta-Gavaldon and Martino Ricci

No 2767, Working Paper Series from European Central Bank

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. JEL Classification: C32, C65, E32, F44, G15

Keywords: China; financial risk; LDA; machine learning; textual analysis; topic modelling (search for similar items in EconPapers)
Date: 2023-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cna, nep-fdg, nep-fmk and nep-rmg
Note: 2338703
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20232767

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