From Speed to Stability: The Influence of Low Latency Trading on Market Quality
Year: 2026
Type: article
Abstract: This repository contains the research poster “From Speed to Stability: The Influence of Low-Latency Trading on Market Quality”, presented at QuantMinds 2024. The work investigates the role of ultra-fast and high-frequency traders in modern electronic markets, with a focus on their participation patterns and their impact on market microstructure, efficiency, and volatility. Using nanosecond-resolution order-level data from Deutsche Börse’s T7 platform (January 2021 to September 2024), the study proposes a latency-based classification of market participants into Ultra-Fast Traders (UFT), High-Frequency Traders (HFT), and conventional traders, based on their reaction time to preceding trades. The analysis covers highly liquid futures and ETFs traded on Eurex and Xetra, including Euro STOXX 50 and DAX futures as well as MSCI World and S&P 500 iShares ETFs. Market activity is aggregated at the one-minute level to compute key microstructure metrics such as traded volume, quoted and effective spreads, depth, order imbalance ratio, and price volatility. Participation is measured in notional terms and analyzed across quantiles to assess how varying levels of low-latency participation relate to market quality outcomes. The results indicate that low-latency traders account for a limited share of total market participation, exhibit distinct behavioral patterns between UFTs and HFTs, and are associated with mostly positive correlations with standard market quality measures. Differences in signal-to-noise behavior between UFTs and HFTs suggest heterogeneous strategies and technological constraints within the low-latency trading ecosystem. This poster summarizes ongoing research. Future work includes a causal impact analysis, intraday pattern detection around macroeconomic announcements using NLP-based event identification, and an investigation of extreme events such as flash crashes.
Author: Taibi, Gabin
Institution: University of Twente
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7118720211
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