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Using machine learning to measure financial risk in China

Author

Listed:
  • Al-Haschimi, Alexander
  • Apostolou, Apostolos
  • Azqueta-Gavaldon, Andres
  • Ricci, Martino
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

Suggested Citation

  • Al-Haschimi, Alexander & Apostolou, Apostolos & Azqueta-Gavaldon, Andres & Ricci, Martino, 2023. "Using machine learning to measure financial risk in China," Working Paper Series 2767, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20232767
    Note: 2338703
    as

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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2767~120c0f6466.en.pdf
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    References listed on IDEAS

    as
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    6. Arrigoni, Simone & Bobasu, Alina & Venditti, Fabrizio, 2020. "The simpler the better: measuring financial conditions for monetary policy and financial stability," Working Paper Series 2451, European Central Bank.
    7. Christine Wong, 2011. "The Fiscal Stimulus Programme and Public Governance Issues in China," OECD Journal on Budgeting, OECD Publishing, vol. 11(3), pages 1-22.
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    13. Apostolou, Apostolos & Al-Haschimi, Alexander & Ricci, Martino, 2022. "Financial risks in China’s corporate sector: real estate and beyond," Economic Bulletin Articles, European Central Bank, vol. 2.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.

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    More about this item

    Keywords

    China; financial risk; LDA; machine learning; textual analysis; topic modelling;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F44 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Business Cycles
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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