The Trading Profits of High Frequency Traders

Baron, Matthew; Brogaard, Jonathan; Kirilenko, Andrei

doi:10.2139/ssrn.2106158

Cited by 113 publications

(92 citation statements)

References 39 publications

(33 reference statements)

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“…Baron, Brogaard, and Kirilenko (2012) show that they make more profit than other HFTs, but the distribution is also very dispersed, in line with Numerical result 2. Brogaard, Hendershott, and Riordan (2011) Moreover, as "mini flash crashes" seem to become more common (Golub, Keane, and Poon (2012)), a key aspect of "supply information" that HFTs may have is learning quickly wether such an event is driven by news or by a glitch.…”

Section: Discussionsupporting

confidence: 89%

Catching Falling Knives: Speculating on Market Overreaction

Colliard

2012

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Jean-Edouard ColliardEuropean Central Bank; e-mail: jean-edouard.colliard@ecb.europa.eu AbstractMarket participants often invest in order to acquire information that pertains to the market itself (e.g. order flow) rather than to fundamentals. This enables them to infer more information from past trades. I show that agents trading on such information, typically high-frequency traders, decrease the likelihood of short-lived mispricings by trading against price pressure. In the long-run however, such countervailing speculation amounts to signal-jamming, slowing down price discovery. These traders insure the market against short-run crashes by "catching falling knives". Higher adverse selection and slower convergence form the "premium" paid by other market participants.Journal of Economic Literature Classification Number: D82, G0, G12, G14.Keywords: market crashes, speculation, supply information, high-frequency trading.1 Non-technical summaryMarket participants have invested a lot in the acquisition of information over the past decades, using in particular the new tools offered by computerization. There is widespread agreement that markets have become more efficient as a result, in the sense that it has become more difficult to forecast price movements using past market data. It is not clear however that this has led to greater price informativeness. Bai, Philippon, and Savov (2012) for instance show that returns have not become better predictors of earnings. This is consistent with the idea that a large fraction of information acquisition focuses on the market itself (prices, order flow) and not on fundamentals.This paper builds a model where "supply-informed traders" have private information on a non-fundamental component, namely whether the market is under pressure coming from fire sales or not. I study the behavior of these traders and their impact on price-discovery and financial stability. Accounts of famous market crashes often involve uncertainty on a non-fundamental parameter: the extent of portfolio insurance for the 1987 crash, the trigger of the price drop during the "flash crash" of May 6, 2010, how crowded the long/short equity fund category was during the "quant event" of August 2007... all seem to have played an important role during these stress episodes. Non-fundamental uncertainty generates market crashes in the model: as uninformed agents do not know the extent to which observed trades are affected...

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Section: Discussionsupporting

confidence: 89%

Catching Falling Knives: Speculating on Market Overreaction

Colliard

2012

SSRN Journal

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“…Studies such as Hendershott, Jones and Menkveld (2011), Hendershott and Riordan (2012), Brogaard, Hendershott and Riordan (2013) and Baron, Brogaard and Kirilenko (2012), attempt to disentangle the incremental effect of algorithmic trading and HFT from other changes in the equity markets by isolating market structure changes that facilitate high-frequency trading. This effort is difficult because it is not possible to directly observe whether a particular order is generated by an HFT computer algorithm, as opposed to just being any other kind of automated trade that does not rely on human intermediaries.…”

Section: Effects Of Hft On Market Liquidity and Transaction Costsmentioning

confidence: 99%

Computerized and High-Frequency Trading

Goldstein

Kumar²,

Graves³

2014

SSRN Journal

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“…Foucault et al (2015) examine latency arbitrage opportunities in currency markets, and provide evidence of a tradeoff between pricing efficiency and liquidity. In another study, Baron et al (2012) find that some kinds of HFT activities directly harm ordinary investors.…”

Section: High-frequency Trading Modelsmentioning

confidence: 98%

Latency arbitrage in fragmented markets: A strategic agent-based analysis

Wah¹,

Wellman

2017

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Abstract.We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We employ a simple model of latency arbitrage in which a single security is traded on two exchanges, with price quotes available to regular traders only after some delay. An infinitely fast arbitrageur reaps profits when the two markets diverge due to this latency in cross-market communication. Using an agent-based approach, we simulate interactions between high-frequency and zero-intelligence trading agents. From simulation data over a large space of strategy combinations, we estimate game models and compute strategic equilibria in a variety of market environments. We then evaluate allocative efficiency and market liquidity in equilibrium, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.

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The Trading Profits of High Frequency Traders

Cited by 113 publications

References 39 publications

Catching Falling Knives: Speculating on Market Overreaction

Catching Falling Knives: Speculating on Market Overreaction

Computerized and High-Frequency Trading

Latency arbitrage in fragmented markets: A strategic agent-based analysis

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