The Lasting Impact of Flickering Quotes
A new study reveals that flickering quotes, or limit orders canceled within one second, play a major role in price discovery on financial markets. Despite representing less than 0.3 percent of canceled order lifetimes, these orders drive about 20 percent of price discovery. Using Euronext Amsterdam data and advanced statistical methods, researchers show flickers, mainly used by high-frequency traders, trigger significant market reactions. The findings challenge the belief that fleeting orders are uninformative.
What it examines
This paper studies 'flickering quotes'—limit orders quickly cancelled—using detailed order book data from Euronext Amsterdam. It examines whether these short-lived orders affect price discovery, using econometric models and statistical analysis to measure their impact and understand how market participants react to them.
What it concludes
Flickering quotes have a lasting impact on prices and contribute significantly to price discovery, especially through high-frequency trading. These findings can help improve trading algorithms, market surveillance, and regulatory policies. Future research could explore their role in other markets and refine models for better market efficiency and transparency.
Evidence objects
A new study reveals that 'flickering quotes'limit orders canceled within one secondsignificantly influence price discovery, overturning the belief that such fleeting orders are uninformative in financial markets.
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Despite representing less than 0.3% of canceled order lifetimes, flickering quotes account for about 20% of price discovery from limit orders, triggering rapid reactions and reshaping the order book and prices.
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Using Euronext Amsterdam data and advanced econometric methods, researchers show flickers are mainly deployed by high-frequency trading firms at best quotes, but findings are limited to one market and a single month.
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This paper challenges prevailing market microstructure theory by empirically demonstrating that flickering quotes in limit order books, often dismissed as uninformed, significantly impact price discovery. Employing advanced econometric methods, including signed price impact regression and SVAR, and identifying HFTs as key drivers, it offers novel, compelling insights into electronic market information flow.
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Raw abstract and provenance
Rob Graumans. University of Oxford - Oxford-Man Institute of Quantitative Finance; Autoriteit Financiële Markten (AFM). Date Written: December 24, 2025
Source row: 1966 · abstract type: snippet