Publications

Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry

Published in , 2024

2023 was a turbulent year for the banking industry, with several banks announcing bankruptcy within just 2 months. Notably, the bank run at Silicon Valley Bank marked the third-largest bank failure in U.S. history. Considering its strong ties to tech companies, we looked into how the risks may have propagated in the tech industry by estimating a VAR model and network analysis. Find out more details by clicking the link to see whether risk contagion occurred in the tech industry, which companies acted as risk transmitters, and how these companies cluster into communities.

Recommended citation: Wu, F.; Liu, A.; Chen, J.; Li, Y. Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry. J. Risk Financial Manag. 2024, 17, 427. https://doi.org/10.3390/jrfm17100427
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A novel network risk index

Working paper

Paper Overview: The current risk measure does not consider the interconnectedness of financial markets. To address this limitation, we propose a two-layer information spillover network capturing both volatility and sentiment dynamics. By extracting network-based properties that quantify the intensity of the spillovers, we propose a risk index. Empirical results show the significance of this risk index in signalling the market risk.

Does one follow the other? A time-varying information spillover network of social media sentiment and news sentiment

Working paper

Paper Overview: Investor sentiment has been studied extensively to have an impact on asset prices and volatilities. In this study, we propose the transfer entropy-based information spillover networks to examine how different sentiment sources can spillover from one asset to another. By capturing the directional and non-linear information flows dynamics, we find that the constructed sentiment spillover networks reveal some unique properties to explain the sentiment spillovers in the financial markets. This work is currently under review.

Spatial-Temporal Dynamics for Enhanced Temporal Link Prediction in Stock Market Crisis Forecasting

Working paper

This is a joint work with Ruizhi Zhang, Wei Wei, Qiming Wang, Yuhua Li, Jing Chen and Anqi Liu. We proposed a link prediction model by employing a Graph Convolutional Network and leveraging a transformer model. This work is currently under review.