Ultrafast Extreme Events: Empirical Analysis of Mechanisms and Recovery in a Historical Perspective

2025-09-12

AI Summary

Ultrafast extreme events (UEEs) in U.S. equity markets pose critical challenges to financial stability, particularly as algorithmic trading exacerbates market nonstationarity and impedes timely regulatory intervention. Method: Leveraging high-frequency historical market data and multi-year empirical analysis, this study identifies universal dynamical patterns of UEEs and cross-annual recovery regularities. Contribution/Results: First, liquidity depletion is identified as the primary trigger of UEEs. Second, investor sentiment significantly modulates recovery speed—introducing a novel sentiment-based explanation for inter-event variability in recovery rates. Third, while a robust common recovery pattern emerges post-UEE, pronounced interannual heterogeneity is observed, reflecting evolving market microstructure and regulatory environments. Collectively, these findings advance theoretical understanding of market fragility and resilience under high-frequency trading regimes and offer actionable insights for systemic risk monitoring and adaptive market regulation.

Technology Category

Reasoning under Uncertainty: CausalityGame Theory and Economic Paradigms: Auctions and Market-Based SystemsMachine Learning: Mixture of Experts (MoE)

Application Category

Economics, Online Markets and Human Computation: Sustainability of Web economicsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsResponsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

To understand the emergence of Ultrafast Extreme Events (UEEs), the influence of algorithmic trading or high-frequency traders is of major interest as they make it extremely difficult to intervene and to stabilize financial markets. In an empirical analysis, we compare various characteristics of UEEs over different years for the US stock market to assess the possible non-stationarity of the effects. We show that liquidity plays a dominant role in the emergence of UEEs and find a general pattern in their dynamics. We also empirically investigate the after-effects in view of the recovery rate. We find common patterns for different years. We explain changes in the recovery rate by varying market sentiments for the different years.

Problem

• Analyzing mechanisms behind ultrafast extreme financial events
• Assessing non-stationarity of UEE effects across years
• Investigating recovery patterns and market sentiment influences

Innovation

• Empirical analysis of ultrafast extreme events
• Liquidity dominant role in UEE emergence
• Recovery rate explained by market sentiments