An Agent Based Model of the E-Mini S&amp;P 500 and the Flash Crash

Paddrik, Mark E.; Hayes, Roy; Todd, Andrew; Yang, Steve Y.; Scherer, William T.; Beling, Peter A.

doi:10.2139/ssrn.1932152

Cited by 23 publications

(17 citation statements)

References 14 publications

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“…Second, in each trading session t, HF agents trade near the best ask (P ask t ) and bid (P bid t ) prices available in the LOB [12]. This assumption is consistent with empirical evidence on HF agents' behavior which suggest that most of their orders are placed very close to the last best prices [19].…”

Section: High-frequency Traderssupporting

confidence: 73%

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Rock around the clock: An agent-based model of low- and high-frequency trading

et al. 2015

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Abstract. We build an agent-based model to study how the interplay between low-and highfrequency trading affects asset price dynamics. Our main goal is to investigate whether highfrequency trading exacerbates market volatility and generates flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of highfrequency traders, i.e., their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.

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Section: High-frequency Traderssupporting

confidence: 73%

“…However, only few attempts have been made to account for the interplay between HF and LF traders [7,12,13]. We improve upon this literature along several dimensions.…”

Section: Introductionmentioning

confidence: 99%

Rock around the clock: An agent-based model of low- and high-frequency trading

et al. 2015

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“…The parameters used in the simulations are listed in Table 1. The estimation of these parameters is inspired by the papers of Mandes (2014), Paddrik et al (2012) and Pellizzari and Westerhoff (2009).…”

Section: Simulations and Discussionmentioning

confidence: 99%

Time to Slow Down for High‐Frequency Trading? Lessons from Artificial Markets

Veryzhenko

Arena

Harb³

et al. 2017

Intell Sys Acc Fin Mgmt

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“…Paddrik et al investigated the flash crash incident using a zero-intelligence agent-based market simulation and successfully validated against the price and volatility movement during the Flash Crash [17]. In this study, we connect the two simulations through introducing some informed high frequency traders who rely on social media information.…”

Section: E Simulation Of the 2013 Associated Press Hoax Incidentmentioning

confidence: 99%

Twitter financial community modeling using agent based simulation

Yang

Liu

2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-With the empirical evidence that Twitter influences the financial market, there is a need for a bottom-up approach focusing on individual Twitter users and their message propagation among a selected Twitter community with regard to the financial market. This paper presents an agent-based simulation framework to model the Twitter network growth and message propagation mechanism in the Twitter financial community. Using the data collected through the Twitter API, the model generates a dynamic community network with message propagation rates by different agent types. The model successfully validates against the empirical characteristics of the Twitter financial community in terms of network demographics and aggregated message propagation pattern. Simulation of the 2013 Associated Press hoax incident demonstrates that removing critical nodes of the network (users with top centrality) dampens the message propagation process linearly and critical node of the highest betweenness centrality has the optimal effect in reducing the spread of the malicious message to lesser ratio of the community.

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An Agent Based Model of the E-Mini S&P 500 and the Flash Crash

Cited by 23 publications

References 14 publications

Rock around the clock: An agent-based model of low- and high-frequency trading

Rock around the clock: An agent-based model of low- and high-frequency trading

Time to Slow Down for High‐Frequency Trading? Lessons from Artificial Markets

Twitter financial community modeling using agent based simulation

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