Tracking High-Frequency Trading Activity in China and Its Effects on Market Quality
Authors: Li Guo, Frank Weikai Li, Ding Qi, Lijian Sun
Abstract
We develop a new order-level proxy for high-frequency trading (HFT) activity based on precise order placement and study its effects on market quality. With quotes updating multiple times per second, millisecond-level order-book access makes high-frequency traders more likely to submit active exact-match orders, defined as limit orders priced exactly at the prevailing best opposite-side quote. Using Shenzhen Stock Exchange data, we validate the proxy: active exact-match orders respond faster to quote updates, cancel more rapidly, concentrate when depth imbalance is favorable and spreads are narrow, and predict mid-quote returns from 100 milliseconds through ten minutes. A difference-in-differences design exploiting cross-channel variation in order dissemination latency at the market open shows that lower latency increases active exact-match shares. This variation yields plausibly causal evidence that latency-induced HFT raises short-horizon price variation, wide-spread incidence, and adverse-selection exposure, while realized spreads decline and pricing error falls modestly. Unlike developed markets, China's T+1 constraint, absence of maker-taker rebates, and limits on rapid quote revision tilt speed advantages toward directional liquidity taking, highlighting how market design shapes the channel through which speed affects market quality.
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The Importance of Low Latency to Order Book Imbalance Trading StrategiesDavid Byrd, Sruthi Palaparthi, Maria Hybinette et al. · 2020 · Trading and Market Microstructure · preprint, not peer reviewed · cited 4 times · AI summary available
The study tests how low-latency order-book access affects order-imbalance strategy profitability, while this paper examines latency’s effects on HFT activity and market quality.
Abstract
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The study analyzes Chinese stock-market trade packages and differences between institutional and individual investors, rather than latency-sensitive order placement.
Abstract
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Abstract
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Abstract
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Abstract
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Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural NetworksTimothée Fabre, Damien Challet · 2025 · Trading and Market Microstructure · cited 3 times · AI summary available
Abstract
This paper investigates real-time detection of spoofing activity in limit order books, focusing on cryptocurrency centralized exchanges. We first introduce novel order flow variables based on multi-scale Hawkes processes that account both for the size and placement distance from current best prices of new limit orders. Using a Level-3 data set, we train a neural network model to predict the conditional probability distribution of mid price movements based on these features. Our empirical analysis highlights the critical role of the posting distance of limit orders in the price formation process, showing that spoofing detection models that do not take the posting distance into account are ina…
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Exploring the Advantages of Transformers for High-Frequency TradingFazl Barez, Paul Bilokon, Arthur Gervais et al. · 2023 · Statistical Finance · cited 8 times · AI summary available
Abstract
This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model, called \textbf{HFformer}, is then introduced for time series forecasting which incorporates a Transformer encoder, linear decoder, spiking activations, and quantile loss function, and does not use position encoding. Furthermore, possible high-frequency trading strategies for use with the HFformer model are discussed, including trade sizing, trading signal aggregation, and minimal trading threshold. Ultimately, the performance of the HFformer and Long Short-Term Memory model…
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Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index FuturesJing-Chao Chen, Yu Zhou, Xi Wang · 2017 · Statistical Finance · cited 14 times · AI summary available
Abstract
Technical trading rules have been widely used by practitioners in financial markets for a long time. The profitability remains controversial and few consider the stationarity of technical indicators used in trading rules. We convert MA, KDJ and Bollinger bands into stationary processes and investigate the profitability of these trading rules by using 3 high-frequency data(15s,30s and 60s) of CSI300 Stock Index Futures from January 4th 2012 to December 31st 2016. Several performance and risk measures are adopted to assess the practical value of all trading rules directly while ADF-test is used to verify the stationarity and SPA test to check whether trading rules perform well due to intrinsic…
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