scholarly journals Quantifying the High-Frequency Trading “Arms Race”

2021 ◽  
Author(s):  
Matteo Aquilina ◽  
Eric Budish ◽  
Peter O’Neill
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
Limit Order Book ◽  
Price Impact ◽  
High Frequency Trading ◽  
Order Book ◽  
Limit Order ◽  
The Cost ◽  
Latency Arbitrage

Abstract We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5–10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market’s cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone.

scholarly journals The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response *

2015 ◽  
Vol 130 (4) ◽  
pp. 1547-1621 ◽  
Author(s):  
Eric Budish ◽  
Peter Cramton ◽  
John Shim
Keyword(s):  
High Frequency ◽  
Market Design ◽  
Limit Order Book ◽  
Theory Model ◽  
Arms Race ◽  
High Frequency Trading ◽  
Continuous Limit ◽  
Order Book ◽  
Limit Order ◽  
Arbitrage Opportunities

Abstract The high-frequency trading arms race is a symptom of flawed market design. Instead of the continuous limit order book market design that is currently predominant, we argue that financial exchanges should use frequent batch auctions: uniform price double auctions conducted, for example, every tenth of a second. That is, time should be treated as discrete instead of continuous, and orders should be processed in a batch auction instead of serially. Our argument has three parts. First, we use millisecond-level direct-feed data from exchanges to document a series of stylized facts about how the continuous market works at high-frequency time horizons: (i) correlations completely break down; which (ii) leads to obvious mechanical arbitrage opportunities; and (iii) competition has not affected the size or frequency of the arbitrage opportunities, it has only raised the bar for how fast one has to be to capture them. Second, we introduce a simple theory model which is motivated by and helps explain the empirical facts. The key insight is that obvious mechanical arbitrage opportunities, like those observed in the data, are built into the market design—continuous-time serial-processing implies that even symmetrically observed public information creates arbitrage rents. These rents harm liquidity provision and induce a never-ending socially wasteful arms race for speed. Last, we show that frequent batch auctions directly address the flaws of the continuous limit order book. Discrete time reduces the value of tiny speed advantages, and the auction transforms competition on speed into competition on price. Consequently, frequent batch auctions eliminate the mechanical arbitrage rents, enhance liquidity for investors, and stop the high-frequency trading arms race.

scholarly journals High-frequency trading in a limit order book

2008 ◽  
Vol 8 (3) ◽  
pp. 217-224 ◽  
Author(s):  
Marco Avellaneda ◽  
Sasha Stoikov
Keyword(s):  
High Frequency ◽  
Limit Order Book ◽  
High Frequency Trading ◽  
Order Book ◽  
Limit Order

Statistical characteristics of price impact in high-frequency trading

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Can Jia ◽  
Tianmin Zhou ◽  
Handong Li
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
Price Change ◽  
Price Impact ◽  
High Frequency Trading ◽  
Statistical Characteristics ◽  
Long Term Memory ◽  
Time Interval ◽  
Trade Volume

AbstractTrading volume changes based on market microstructure will impact asset prices, which will lead to transaction price changes. Based on the extended Hasbrouck–Foster–Viswanathan (HFV) model, we study the statistical characteristics of daily permanent price impact and daily temporary price impact using high-frequency data from Chinese Stock Markets. We estimate this model using tick-by-tick data for 16 selected stocks that are traded on the Shanghai Stock Exchange. We find the following: (1) the time series of both the permanent price impact and temporary price impact exist in stationarity and long-term memory; (2) there is a strong correlation between the permanent price impact among assets, while the correlation coefficient of the temporary price impact is generally weak; (3) the time interval has no significant influence on the trade volume and the price change at the tick frequency, which means that it is not necessary to take into account the time interval between adjacent transaction in high-frequency trading; and (4) the bid-ask spread is an effective factor to explain trading price change, but has no significant impact on trade volume.

scholarly journals Optimal inventory management and order book modeling

2019 ◽  
Vol 65 ◽  
pp. 145-181 ◽  
Author(s):  
Nicolas Baradel ◽  
Bruno Bouchard ◽  
David Evangelista ◽  
Othmane Mounjid
Keyword(s):  
Inventory Management ◽  
High Frequency ◽  
Limit Order Book ◽  
Order Book ◽  
Value Functions ◽  
Limit Order ◽  
Terminal Wealth ◽  
Market Makers ◽  
Market Participants ◽  
The One

We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [12, 18, 19], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.

scholarly journals Trade informativeness and liquidity in Bitcoin markets

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255515
Author(s):  
J. Christopher Westland
Keyword(s):  
Trading Volume ◽  
Limit Order Book ◽  
Small Sample ◽  
Market Liquidity ◽  
Order Book ◽  
Information Asymmetries ◽  
Limit Order ◽  
Limit Orders ◽  
Market Orders ◽  
Book Markets

Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten’s seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.

scholarly journals Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia

2020 ◽  
Vol 17 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Perdana Wahyu Santosa
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
High Frequency Trading ◽  
High Price ◽  
Research Approach ◽  
Price Model ◽  
Bid Ask Spread ◽  
Price Reversal ◽  
New Research

This article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The research method uses purposive random sampling, which classified price fractions into three groups, specifically low price, medium price, and high price, which are analyzed by logistic panel regression. The research variables used include price reversal (dependent), stock return, trading volume, transaction frequency, volume/frequency (V/F) proxy, volatility, and liquidity. According to low price model research findings, all variables show a significant effect on price reversal; for medium price model, all variables except liquidity show a significant effect on price reversal; and for high price model, all variables have a significant effect on price reversal, except trading volume and volatility. In conclusion, low price shares tend to have higher price reversal probability compared to continuity because they tend to be liquid, low institutional ownership, and minimal reporting/analysis and are controlled by HFTs (uninformed traders). Some variables are not significant because of the bounce effect around the bid-ask spread. AcknowledgmentMany thanks to Armida S. Alisjahbana, Roy H. Sembel, Budiono, Rahardi S. Rahmanto, and the anonymous referee/reviewer for valuable inputs and feedback.

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