High-Frequency Early Warning of Systemic Financial Stress in Europe Using Financial and Non-Financial Data with Machine Learning
Year: 2026
Type: conference-paper
Abstract: Early detection of systemic financial stress is challenging in fast-moving, nonlinear environments. Traditional early warning systems rely on low-frequency indicators and linear models, limiting their real-time relevance. This paper develops a high-frequency machine-learning framework to monitor systemic financial stress in Europe using daily financial, banking, macro-financial, and sentiment indicators. Diverse data sources are integrated in a strictly time-ordered pipeline to avoid look-ahead bias, with systemic financial stress states defined by threshold exceedances of the ECB's Composite Indicator of Systemic Stress. Feature selection and evaluation are conducted using stability-based regularization, time-series cross-validation, and rare-event metrics. Models combining financial and sentiment indicators outperform linear benchmarks, particularly during periods of intensifying stress. The results suggest that high-frequency machine-learning models can improve the timeliness of systemic stress monitoring and complement existing macroprudential tools.
Authors: Dmytro Diachkov, Afshin Ashofteh
Institution: Universidade Nova de Lisboa
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7203833745