Financial Black Swans Driven by Ultrafast Machine Ecology

18 Pages Posted: 16 Feb 2012

See all articles by Neil Johnson

Neil Johnson

George Washington University

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

Date Written: 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.

Keywords: high frequency trading, complexity, ecology

Suggested Citation

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

Neil Johnson (Contact Author)

George Washington University ( email )

2121 I Street NW
Washington, DC 20052
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 ( email )

202 Burlington Road
Bedford, MA 01730
United States

University of Vermont - College of Engineering and Mathematics ( email )

United States

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