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Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT

Author

Listed:
  • Ren-Yuan Lyu

    (College of Management, Chang Gung University, 259 Wenhua First Road, Taoyuan 33302, Taiwan)

  • Ren-Raw Chen

    (Gabelli School of Business, Fordham University, 45 Columbus Avenue, Room 609, New York, NY 10019, USA)

  • San-Lin Chung

    (College of Management, Chang Gung University, 259 Wenhua First Road, Taoyuan 33302, Taiwan
    College of Management, National Taiwan University, 1 Section 4 Roosevelt Road, Taipei 106319, Taiwan)

  • Yilu Zhou

    (Gabelli School of Business, Fordham University, 140 W. 62nd Street, Room 407, New York, NY 10023, USA)

Abstract

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk.

Suggested Citation

  • Ren-Yuan Lyu & Ren-Raw Chen & San-Lin Chung & Yilu Zhou, 2024. "Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT," FinTech, MDPI, vol. 3(2), pages 1-28, May.
    • Handle: RePEc:gam:jfinte:v:3:y:2024:i:2:p:16-301:d:1395458
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Lopez-Lira & Jihoon Kwon & Sangwoon Yoon & Jy-yong Sohn & Chanyeol Choi, 2025. "Bridging Language Models and Financial Analysis," Papers 2503.22693, arXiv.org.

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