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Assessing systemic risk in financial markets using dynamic topic networks.

Mike K P So1, Anson S W Mak2, Amanda M Y Chu3

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This study introduces a dynamic topic network approach to assess financial systemic risk by analyzing news data. The method effectively correlates topic connections with market volatility and predicts market recovery, offering real-time risk management insights.

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Area of Science:

  • Financial Economics
  • Computational Finance
  • Data Science

Background:

  • Systemic risk in financial markets poses a significant threat due to global events, leading to widespread instability and financial losses.
  • Traditional methods for assessing systemic risk often struggle to capture the dynamic and interconnected nature of market behaviors.
  • The need for advanced analytical tools to detect and predict systemic risk events in real-time is paramount for effective financial regulation and management.

Purpose of the Study:

  • To propose and validate a novel Dynamic Topic Network (DTN) approach for assessing systemic risk in financial markets.
  • To demonstrate the capability of the DTN approach in identifying abnormal market movements and predicting recovery periods.
  • To provide a practical, data-driven tool for daily risk management and real-time systemic risk prediction.

Main Methods:

  • Utilizing Latent Dirichlet Allocation (LDA) for semantic analysis of financial news articles to extract key topics.
  • Constructing dynamic topic similarity networks over time based on extracted topics.
  • Correlating network topological features and abnormal topic behaviors with financial market volatility and indices (e.g., Dow Jones Industrial Average).

Main Results:

  • The study successfully demonstrates that topic connectivity within the DTN framework can be correlated with financial market volatility.
  • Analysis of historical events, including the 2015-2016 stock market selloff and the COVID-19 pandemic, showed the DTN approach accurately indicated abnormal market movements.
  • The DTN approach provided timely signals for market recovery following major disruptive events.

Conclusions:

  • The proposed Dynamic Topic Network (DTN) approach offers a robust and innovative method for quantifying and predicting systemic risk in financial markets.
  • This technique enables proactive risk management by providing daily, real-time insights into market stability and potential recovery trajectories.
  • The DTN approach enhances financial market surveillance and regulatory oversight by offering a dynamic, data-driven perspective on systemic vulnerabilities.