---
title: Systemic Risk in FinTech and Traditional Finance
url: https://www.ml-quant.com/papers/repec/taf-eurjfi-v-30-y-2024-i-18-p-2157-2190/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:taf:eurjfi:v:30:y:2024:i:18:p:2157-2190
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1351847X.2024.2358940%3Bh%3Drepec%3Ataf%3Aeurjfi%3Av%3A30%3Ay%3A2024%3Ai%3A18%3Ap%3A2157-2190
featured: 2024-12-12
citations: unknown
topic: Risk, Credit & Banking
---


# Systemic Risk in FinTech and Traditional Finance

The study uses machine learning to identify key factors affecting systemic risk in FinTech and traditional financial institutions, including market volatility, individual stock volatility, and market capitalization, especially under extreme market conditions.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1351847X.2024.2358940%3Bh%3Drepec%3Ataf%3Aeurjfi%3Av%3A30%3Ay%3A2024%3Ai%3A18%3Ap%3A2157-2190
- Identifier: RePEc:taf:eurjfi:v:30:y:2024:i:18:p:2157-2190
- Released: 2024-12-12
- First featured: Quant Letter No. 78 (2024-12-12): https://www.ml-quant.com/issues/2024-12-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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