Assessing systemic risk in financial markets using dynamic topic networks Article Swipe

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Mike K. P. So , Anson S. W. Mak , Amanda M. Y. Chu ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1038/s41598-022-06399-x

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015–2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.

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Systemic risk Financial market Latent Dirichlet allocation Volatility (finance) Computer science Stock (firearms) Construct (python library) Stock market Topic model Financial economics Business Financial crisis Finance Economics Artificial intelligence Macroeconomics Paleontology Mechanical engineering Biology Engineering Programming language Horse

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Type: article
Language: en
Landing Page: https://doi.org/10.1038/s41598-022-06399-x
PDF: https://www.nature.com/articles/s41598-022-06399-x.pdf
OA Status: gold
Cited By: 22
References: 51
Related Works: 10
OpenAlex ID: https://openalex.org/W4212860010

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DOI: https://doi.org/10.1038/s41598-022-06399-x

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Title: Assessing systemic risk in financial markets using dynamic topic networks

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-02-17

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Authors: Mike K. P. So, Anson S. W. Mak, Amanda M. Y. Chu

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Landing page: https://doi.org/10.1038/s41598-022-06399-x

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PDF URL: https://www.nature.com/articles/s41598-022-06399-x.pdf

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Concepts: Systemic risk, Financial market, Latent Dirichlet allocation, Volatility (finance), Computer science, Stock (firearms), Construct (python library), Stock market, Topic model, Financial economics, Business, Financial crisis, Finance, Economics, Artificial intelligence, Macroeconomics, Paleontology, Mechanical engineering, Biology, Engineering, Programming language, Horse

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Cited by: 22

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Citations by year (recent): 2025: 8, 2024: 6, 2023: 5, 2022: 3

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