Tag Archives: Neil Johnson

Speed Traders Are Approaching Speed of Light – And Beyond

As you read this, a gigantic fiber optic cable is being put in place, right across the Atlantic, from the UK to  the US, so that high speed trading robots can save 5 millisecond on their communications. At the same time a new computer chip is being launched that are able to prepare trades in 740 nanosecond. Faster than you are able to blink your eye, faster than you are able to think…

“The downside of society’s continuing drive toward larger, faster, and more interconnected socio-technical systems such as global financial markets, is that future catastrophes may be less easy to forsee and manage –  as witnessed by the recent emergence of financial flash-crashes.”

Neil Johnson

With computers able to work faster than the speed of light, we are giving away the power to control our own lives. No human brain is able to think faster than 1000 nanoseconds, US researchers points out in a new paper as they reveal signs of the financial markets transforming into a machine-dominated phase that eventually will end in disaster.

Here’s a summary of what the paper says:

  • 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.”

The paper is authored by a team of physicists, engineers and industry data experts, led by Neil Johnson from University of Miami.

It is charmingly entitled:  “Financial Black Swans Driven by Ultrafast Machine Ecology” 

The scientist describes the developments as “approaching singularity.” 

It is plainly the point in time from when computers are able to calculate, react and perform faster than any human being.

And it’s a point we obviously are about to reach:

“… a new dedicated transatlantic cable 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 …”

Johnson and his colleagues ask the question of whether today’s high-frequency markets are moving toward a boundary of speed where human intervention and control is effectively impossible:

“The downside of society’s continuing drive toward larger, faster, and more interconnected socio-technical systems such as global financial markets, is that future catastrophes may be less easy to forsee and manage –  as witnessed by the recent emergence of financial flash-crashes. In traditional human-machine systems, real-time human intervention may be possible if the undesired changes occur within typical human reaction times. However,… in many areas of human activity, the quickest that someone can notice such a cue and physically react, is approximately 1000 milliseconds (1 second).”

www.betabeat.com writes:

“Notwithstanding this biophysical limitation, the strategic advantage to a financial company of having a faster system than its competitors is currently driving a billion-dollar technological arms race to reduce communication and computational operating times down toward the physical limits of the speed of light – orders of magnitude below human response times.”

Physics of Finance and blogger, Mark Buchanan, writes:  

” This just illustrates the technological arms race underway as firms try to out-compete each other to gain an edge through speed. None of the players in this market worries too much about what this arms race might mean for the longer term systemic stability of market; it’s just race ahead and hope for the best.”

So, to summarize: We now have machines that are moving big chunks of money around faster than our eyes can blink, faster than our tiny brains can comprehend, with the potential to cause crashes that are so lightening-quick they can neither be anticipated nor corrected before causing systemic trauma – because traders are still really fast at panicking.

Just great!

Here are some other highlights – enjoy:

“… our data set shows a far greater tendency for these financial fractures to occur, within a given duration time-window, as we move to smaller timescales, e.g. 100-200 ms has approximately ten times more than 900-1000 ms.”

“The presence of humans actively trading — and hence their ‘free will’ together with the myriad ways in which they can manually override algorithms — means that the effective number (i.e. α > 1). Moreover α > 1 implies m is large, hence there are more pieces of information available which suggests longer timescales…  in this α > 1 regime, the average number of agents per strategy is less than 1, hence any crowding effects due to agents coincidentally using the same strategy will be small. This lack of crowding leads our model to predict that any large price movements arising for α > 1 will be rare and take place over a longer duration.”

“Our model therefore predicts a rapidly increasing number of ultrafast black swan events as we move to smaller α and hence smaller subsecond timescales – as observed in our data.”

Download paper.

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