Predicting financial market stress with machine learning

Iñaki Aldasoro, Peter Hördahl, Andreas Schrimpf and Sonya Zhu

No 1250, BIS Working Papers from Bank for International Settlements

Abstract: Using newly constructed market condition indicators (MCIs) for three pivotal US markets (Treasury, foreign exchange, and money markets), we demonstrate that tree-based machine learning (ML) models significantly outperform traditional timeseries approaches in predicting the full distribution of future market stress. Through quantile regression, we show that random forests achieve up to 27% lower quantile loss than autoregressive benchmarks, particularly at longer horizons (3–12 months). Shapley value analysis reveals that funding liquidity, investor overextension and the global financial cycle are important predictors of future tail realizations of market conditions. The MCIs themselves play a prominent role as well, both in the same market (self-reinforcing dynamics within markets) and across markets (spillovers across markets). These results highlight the value of ML in forecasting tail risks and identifying systemic vulnerabilities in real time, bridging the gap between highfrequency data and macroeconomic stability frameworks.

Keywords: machine learning; financial stress; quantile regressions; forecasting; Shapley value (search for similar items in EconPapers)
JEL-codes: C53 G01 G12 G17 G28 (search for similar items in EconPapers)
Date: 2025-03
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.bis.org/publ/work1250.pdf Full PDF document (application/pdf)
https://www.bis.org/publ/work1250.htm (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bis:biswps:1250

Access Statistics for this paper

More papers in BIS Working Papers from Bank for International Settlements Contact information at EDIRC.
Bibliographic data for series maintained by Martin Fessler ().