The Flash Crash: The Impact of High Frequency Trading on an Electronic Market

By Andrei A. Kirilenko, M. Samadi, Albert S. Kyle, Tugkan Tuzun. 2011

603 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f997411ca4c133dd790f78d7dc5f6936b5d221af

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Citations in the corpus

Competing on Speed (2018, unknown): “Early theoretical analyses of fragmentation include those of Mendelson (1987) and Pagano (1989). These static models focus on the tradeoff between liquidity externalities, market power, and trading costs.”
Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis (2013, background): “The analysis of the efficiency of the emerging ecology of partially connected trading pools is questioned, especially since the flash crash [25,34] during which the US equity market has lost around 10 % of its value in 10 min, regaining it in 20 min.”
Limit order books (2010, background): “Email: gouldm@maths.ox.ac.uk optimal order execution strategies (Obizhaeva and Wang 2013); market impact minimization (Eisler et al. 2012); designing better electronic trading algorithms (Engle et al. 2006); and assessing market stability (Kirilenko et al. 2011).”
Intraday Trading Invariance in the E-mini S&P 500 Futures Market (2015, unknown): “Kirilenko et al. (2014) report (their Table II) that traders representing institutional investors (Fundamental Buyers and Sellers) execute larger average trades than those who intermediate the trading process. In the last few minutes prior to the nadir of the crash, the participation rate of high-fr”
Unsolved Problems in ML Safety (2021, background): “In the 2010 Flash Crash [100], automated trading systems unexpectedly overreacted to market aberrations, created a feedback loop, and wiped away a trillion dollars of stock value in a matter of minutes.”
Statistical Modeling of High-Frequency Financial Data (2011, background): “…(typically, daily or longer), trading strategies, at different frequencies may interact in a complex manner, leading to ripples across time scales which propagate from high frequency to low frequency and even leading to possible market disruptions, as shown by the Flash Crash of May 2010 [16, 44].”
The Microstructure of the “Flash Crash”: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading (2011, methodology): “This explanation was shortlived because E-mini S&P 500 tick data demonstrated that the market was already down by the time Procter & Gamble stock plummeted (Phillips [2010]).”
A Dynamic Limit Order Market with Fast and Slow Traders (2014, unknown): “In case the best available bid was posted by a fast buyer (and therefore was revised before the arrival of the market order trader), the trading cost is given by τ t,ST,FT = vt−1 + σ −B t−1,FT (21) for εt = +σ and τ−σ t,ST,FT = vt−1 − σ −B−σ t−1,FT (22) for εt = −σ.”