Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT-Reference-Cited by-同舟云学术

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

Published:2024-05-16 Issue:2 Volume:3 Page:274-301
ISSN:2674-1032
Container-title:FinTech
language:en
Short-container-title:FinTech

Author:

Lyu Ren-Yuan¹,Chen Ren-Raw²,Chung San-Lin¹³,Zhou Yilu⁴

Affiliation:

1. College of Management, Chang Gung University, 259 Wenhua First Road, Taoyuan 33302, Taiwan

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

3. College of Management, National Taiwan University, 1 Section 4 Roosevelt Road, Taipei 106319, Taiwan

4. 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.

Funder

San-Lin Chung acknowledges the financial support from the National Science and Technology Council, Taiwan

Publisher

MDPI AG

Link

https://www.mdpi.com/2674-1032/3/2/16/pdf

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