---
title: Predicting Financial Market Stress with Machine Learning
url: https://www.ml-quant.com/papers/repec/cpr-ceprdp-20439/
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:cpr:ceprdp:20439
source_url: https://econpapers.repec.org/RePEc:cpr:ceprdp:20439
featured: 2026-09-25
citations: unknown
topic: ML & AI Methods
---


# Predicting Financial Market Stress with Machine Learning

Tree-based machine learning models predict the full distribution of financial market stress 27% better than traditional time-series methods, with macro uncertainty and monetary policy expectations as key drivers.

- Source: https://econpapers.repec.org/RePEc:cpr:ceprdp:20439
- Identifier: RePEc:cpr:ceprdp:20439
- Released: 2026-09-17
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Aldasoro, Inaki, Hördahl, Peter, Schrimpf, Andreas, Zhu, Sonya

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