A Dynamic Limit Order Market with Fast and Slow Traders

By Peter Hoffmann. Social Science Research Network, 2014

262 citations recorded by the source corpus.

We study the role of high-frequency trading in a dynamic limit order market. Being fast is valuable because it enables traders to revise outstanding limit orders upon news arrivals when interacting with slow market participants. On the one hand, the existence of fast traders can help to reduce the inefficiency that is rooted in the risk of being "picked off" after unfavourable price movements and therefore allows more gains from trade to be realized. On the other hand, slow traders face a relative loss in bargaining power which leads them to strategically submit limit orders with a lower execution probability, thereby reducing trade. Due to this negative externality, the equilibrium level of investment is always welfare-reducing. The model generates additional testable implications regarding the effects of high-frequency trading on order flow statistics.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b28b36a9967788b2c7c5414580dacbd4a5729d1c

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Citations in the corpus

High-Frequency Market Making to Large Institutional Trades (2018, background): “On the theoretical side, Hoffman (2013) finds that fast traders extract rents from other traders, which triggers a costly arms race that reduces social welfare. Budish, Cramton, and Shim (2014) show that a continuous time market structure creates frequent technical arbitrage opportunities that are a”
What Is the Optimal Trading Frequency in Financial Markets (2017, background): “This is different from the welfare question in a number of high-frequencytrading studies, namely whether investments in high-speed trading technology are socially wasteful (see Biais, Foucault, and Moinas (2015), Pagnotta and Philippon (2013), Budish, Cramton, and Shim (2015), and Hoffmann (2014)).”
Every Cloud Has a Silver Lining: Fast Trading, Microwave Connectivity, and Trading Costs (2020, background): “Several theory models support this notion, suggesting that when speed differentials between traders exist, liquidity may become more expensive (e.g., Hoffmann, 2014; Biais, Foucault and Moinas, 2015; Budish, Cramton and Shim, 2015; Foucault, Hombert and Roşu, 2016).”
Price Discovery without Trading: Evidence from Limit Orders (2019, background): “8 Relative to fast traders, slow traders in Hoffmann (2014) submit relatively more limit orders than market orders when volatility is high.”
The Economics of High-Frequency Trading: Taking Stock (2016, background): “Hoffmann (2014) analyzes a model where HFTs quickly cancel their outstanding limit orders after news.”
A visual analytics approach to understanding cycling behaviour (2012, unknown): “In Strotos et al. [37] the breakup initiation time was correlated as a function of We and Re numbers (valid for low Oh and high ε numbers), while later Stefanitsis et al. [29] proposed a correction factor to account also for changes in the density ratio ε .”
News Trading and Speed (2012, unknown): “λt = (Σ0 + σ 2 v) 1/2 σu 1 ( 1 + σ 2 e σ2 v f )1/2 (1 + f) , (21)”
Risk and Return in High-Frequency Trading (2018, background): “This is consistent with theoretical models, such as Foucault et al. (2016) on active trading; and Hoffmann (2014) and Aït- Sahalia and Saglam (2014) on passive trading.”