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Understanding stock market instability via graph auto-encoders.

Dragos Gorduza1, Stefan Zohren1, Xiaowen Dong1

  • 1Oxford-Man Institute of Quantitative Finance, University of Oxford, Walton Street, Oxford, UK.

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This study introduces a novel method using graph auto-encoders to predict stock market volatility. By analyzing financial network structures, it offers a new way to forecast market instability and improve risk management.

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Area of Science:

  • Financial Economics
  • Network Science
  • Machine Learning

Background:

  • Stock market instability poses significant risks to financial management.
  • Market disruptions are linked to changes in stock correlation structures, increasing volatility.
  • Financial networks, where companies are nodes and correlations are edges, model these co-movements.

Purpose of the Study:

  • To develop a timely indicator for stock market breakdowns and volatility.
  • To leverage graph machine learning for financial network analysis.
  • To enhance understanding of financial stability and volatility forecasting.

Main Methods:

  • Utilized a graph auto-encoder to analyze the topological structure of financial networks.
  • Employed edge reconstruction accuracy as a proxy for market-wide connection homogeneity.
  • Applied the model to the Standard and Poor's index data from 2015-2022.

Main Results:

  • Reconstruction errors from the graph auto-encoder correlated with market volatility spikes.
  • The proposed method improved out-of-sample autoregressive volatility modeling.
  • Demonstrated that changes in financial network connection homogeneity can predict market instability.

Conclusions:

  • Market instability can be predicted by shifts in the homogeneity of financial network connections.
  • Graph machine learning offers a promising approach for volatility estimation and financial stability policy.
  • The study expands the understanding of stock market instability through network analysis.