Kan AI 90% van het high-frequency trading-volume sturen door marktmicrostructuurgebeurtenissen te voorspellen en vorm te geven voordat ze zich voordoen ?

AI-modellen lopen snel op menselijke handelaren voor in latentie en patroonherkenning. Door hele marktecosystemen te simuleren, kunnen deze systemen orderstromen voorspellend manipuleren, wat cascaderende effecten kan veroorzaken. Toezichthouders worstelen met het detecteren of indammen van dergelijke geautomatiseerde destabilisatie.

Background

AI models are rapidly outpacing human traders in latency and pattern recognition. By simulating entire market ecosystems, these systems could preemptively manipulate order flows, triggering cascading effects. Regulators struggle to detect or contain such automated destabilization. Today’s best AI systems can model order-book dynamics and microsecond-scale liquidity imbalances well enough to anticipate short-term price moves with modest accuracy, but they rarely drive anything close to 90 percent of high-frequency trading volume. Firms combine machine-learning signals with colocation, FPGA-accelerated execution, and regulatory-compliant arbitrage strategies to achieve sub-10-millisecond latency, yet they still depend on human oversight for risk controls and fail to predict or shape most microstructure events before they occur. Evidence from exchange-level data shows peak AI-driven participation around 30–40 percent of notional volume in the most liquid futures and equities markets. — Enriched May 10, 2026 · Source: Bank for International Settlements