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Financial Black Swans Driven by Ultrafast Machine Ecology


Neil Johnson


University of Miami

Guannan Zhao


University of Miami

Eric Hunsader


affiliation not provided to SSRN

Jing Meng


University of Miami

Amith Ravindar


University of Miami

Spencer Carran


affiliation not provided to SSRN

Brian Tivnan


The MITRE Corporation; University of Vermont - College of Engineering and Mathematics

February 12, 2012


Abstract:     
Society’s drive toward ever faster socio-technical systems, means that there is an urgent need to understand the threat from ‘black swan’ extreme events that might emerge. 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. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly. 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.

Number of Pages in PDF File: 18

Keywords: high frequency trading, complexity, ecology

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Date posted: February 16, 2012  

Suggested Citation

Johnson, Neil, Zhao, Guannan, Hunsader, Eric, Meng, Jing, Ravindar, Amith, Carran, Spencer and Tivnan, Brian, Financial Black Swans Driven by Ultrafast Machine Ecology (February 12, 2012). Available at SSRN: http://ssrn.com/abstract=2003874 or http://dx.doi.org/10.2139/ssrn.2003874

Contact Information

Neil Johnson (Contact Author)
University of Miami ( email )
Coral Gables, FL 33124
United States
Guannan Zhao
University of Miami
Coral Gables, FL 33124
United States
Eric Hunsader
affiliation not provided to SSRN
Jing Meng
University of Miami
Coral Gables, FL 33124
United States
Amith Ravindar
University of Miami
Coral Gables, FL 33124
United States
Spencer Carran
affiliation not provided to SSRN
Brian Tivnan
The MITRE Corporation
202 Burlington Road
Bedford, MA 01730
United States
University of Vermont - College of Engineering and Mathematics ( email )
United States
Feedback to SSRN (Beta)


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