An ecological perspective on the future of computer trading

Farmer, J. Doyne; Skouras, Spyros

doi:10.1080/14697688.2012.757636

Cited by 51 publications

(40 citation statements)

References 61 publications

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“…Society's drive toward ever faster socio-technical systems [1][2][3] , means that there is an urgent need to understand the threat from 'black swan' extreme events that might emerge [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash 4 .…”

Section: Introductionmentioning

confidence: 99%

“…The downside of society's continuing drive toward larger, faster, and more interconnected socio-technical systems such as global financial markets 1,3 , is that future catastrophes may be less easy to forsee and manage --as witnessed by the recent emergence of financial flash-crashes 1,3,4 . In traditional human-machine systems, real-time human intervention may be possible if the undesired changes occur within typical human reaction times.…”

mentioning

confidence: 99%

“…For example, a new dedicated transatlantic cable 22 is being built just to shave 5 milliseconds off transatlantic communication times between US and UK traders, while a new purpose-built chip iX-eCute is being launched which prepares trades in 740 nanoseconds (1 nanosecond is 10 !9 seconds) 23 . Financial market price movements are determined by the selforganized activity of a global collective of trading agents [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , including both humans and machine algorithms. Like many other complex systems, financial markets have no real-time controller, hence mitigation relies on carefully choosing the trading regulations.…”

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confidence: 99%

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Financial Black Swans Driven by Ultrafast Machine Ecology

Johnson

Zhao

Hunsader³

et al. 2012

SSRN Journal

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Society's drive toward ever faster socio-technical systems [1][2][3] , means that there is an urgent need to understand the threat from 'black swan' extreme events that might emerge [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash 4 . However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly 20,21 . Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system's internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast 'fractures' and the slow 'breaking' of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Financial Black Swans Driven by Ultrafast Machine Ecology

Johnson

Zhao

Hunsader³

et al. 2012

SSRN Journal

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“…Liquidity provision may be even more impeded during times of high volatility, when it is particularly expensive to post limit orders. Furthermore, this measure may also change the dynamics of the market by attracting more aggressive HFT (Farmer and Skouras, 2013). Lastly, market quality may be diminished due to higher transaction costs for the end users and lower price efficiency.…”

Section: Minimum Resting Timesmentioning

confidence: 99%

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

Leal¹,

Napoletano

2019

Journal of Economic Behavior & Organization

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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 AbstractWe investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low-and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of flash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes flash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-off between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off.

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“…Robot traders have been around for years, but used only for private gain. More recently, their use for high-frequency trading has been the cause of intense debate on whether their effects are beneficial or harmful to the functioning of financial markets, and how regulation should cope with the rapid pace of their technological innovation (see the reviews by Foucault, 2012;Kirilenko andLo, 2013, andFarmer andSkouras, 2013, for an ecological perspective). Here, instead, we propose their systematic use for the benefit of public policy making.…”

Section: Introductionmentioning

confidence: 99%

Cowboying Stock Market Herds with Robot Traders

2016

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One explanation for large stock market fluctuations is its tendency to herd behavior. We put forward an agent-based model where instabilities are the result of liquidity imbalances amplified by local interactions through imitation, and calibrate the model to match some key statistics of actual daily returns. We show that an "aggregate market-maker" type of liquidity injection is not successful in stabilizing prices due to the complex nature of the stock market.To offset liquidity shortages, we propose the use of locally triggered contrarian rules, and show that these mechanisms are effective in preventing extreme returns in our artificial stock market.JEL codes: C63, G02.

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An ecological perspective on the future of computer trading

Cited by 51 publications

References 61 publications

Financial Black Swans Driven by Ultrafast Machine Ecology

Financial Black Swans Driven by Ultrafast Machine Ecology

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

Cowboying Stock Market Herds with Robot Traders

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