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07/02/2012 John Cartlidge, ICAART-2012, Portugal 1
TOO FAST – TOO FURIOUS: Faster Financial-Market Trading Agents Can Give Less Efficient Markets
John Cartlidge, C. Szostek, M. De Luca and D. Cliff
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Outline • Lightening-fast background of…
…automated trading agents in financial markets (HFTs) …impact of agents on financial markets (specifically crashes) …academic literature on experimental human vs. agents financial markets
• The Problem – Does agent speed affect the dynamics of human-agent financial markets?
• Methodology – Human-agent experiments in a virtual commodities market
• Results & Conclusions – Faster agents can produce less efficient markets
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Financial-Market Trading Agents Continually Grab the Headlines…
But why is everybody [suddenly] so interested?
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‘twas ever thus…
Traders were human [and male] and bought and sold face-to-face on ‘open- outcry’ trading floors
Copyright: © Henry Grant Collection/Museum of London
Traders on the London Stock Exchange trading floor, 1968
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And then, on October 27th 1986, the markets were changed forever…
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Huge deregulation of Britain’s financial markets ushered in a move in London from open outcry to screen based electronic trading
‘Big Bang’
Philip Martin. Free Clip Art: philipmartin.info
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‘loadsamoney’
Charlie Sheen in ‘Wall Street’, 1987 Copyright: © 20th Century Fox
Anonymous computer-based trading platforms enabled greater transaction speed, better price discovery and increased liquidity
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Death of the Trader?
Copyright: © 2006, IBM
[by 2015] “survey respondents, on average, plan to shrink their number of traders from forty to four across most product groups” [IBM 2006]
…with hindsight, this was probably an underestimate
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Rise of the Robots
Life size robot sculptures, 1997 Copyright: © Clayton Bailey
In Europe & USA major exchanges are reporting that 30%-75% of all transactions currently involve automated [robot] traders
[Foresight 2011]
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• Current trend for financial agents is to increase speed by hardwiring algorithms using FPGAs
Wired for Speed
• Profits generally go to the first firm to act
• HFTs hold very short term positions, arbitraging across markets
• Speed of light is a limiting factor. A 60ms round trip from New York to London can leave data looking ‘stale’
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How has the introduction of trading agents affected the dynamics of financial markets?
To answer this, let’s take a quick stroll through the history of market crashes…
Question…
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1929, Wall Street Crash
35% of market value lost in 3 weeks
[Source Wikipedia] http://en.wikipedia.org/wiki/File:1929_wall_street_crash_graph.svg
‘Black Thursday’ Oct 24th, Dow Jones Industrial Average opens at 305.85 By Nov 13th, Dow had fallen to 199
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Oct 19th 1987, ‘Black Monday’
[US Federal Reserve] http://www.federalreserve.gov/Pubs/feds/2007/200713/200713pap.pdf
Largest one day percentage decline in Dow Jones history (22%)
Electronic trading systems and computer-generated trading still in their infancy
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May 6th 2010, ‘Flash Crash’
Within 20 minutes, Dow plummets 9% and then largely recovers the loss
New York Times
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May 6th 2010, ‘Flash Crash’
In 14 seconds more than 27,000 e-Mini S&P futures contracts were bought and sold
But only 200 aggregate net purchases
The ‘hot potato’ effect
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Some stock, like Accenture, plummeted to 1 cent…
May 6th 2010, ‘Flash Crash’
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May 6th 2010, ‘Flash Crash’
At this value, Sotheby’s would have a net worth greater than the entire Chinese economy!
…while others traded at $100,000
NYSE cancelled all trades executed between 14:40-15:00 that were more than 60% away from last print at 14:40
This resulted in arbitrary winners … and losers
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and then, one year later…
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Traders work in the oil options pit on the floor of the New York Mercantile Exchange
REUTERS/Brendan McDermid
Brent Crude lost 13% Copper slid 5% Cotton fell 8%
5th May 2011
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Observations…
• The move to computer-based trading and the introduction of automated trading agents has altered market dynamics
– E.g., market ‘crashes’ now occur more rapidly and more regularly • How to better understand the effects of agents on market dynamics?
– This question is being asked by academia, industry and government alike…
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Understanding Market Dynamics
• By observation of real-world market activity
• By controlled experimentation in laboratory settings with traders interacting in artificial markets • We can assess market ‘quality’ by measuring the equilibrium
finding behaviour, allocative efficiency and profit dispersion • We introduce these concepts next…
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Supply & Demand 101
Supply: as price increases more people are willing to sell
Demand: as price decreases more people are willing to buy
The market is in equilibrium when supply equals demand (Q0, P0)
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Equilibration Behaviour
r.m.s. difference between each transaction price, pi, and P0, expressed as a percentage of equilibrium price
The equilibrium-finding behaviour of a market (Smith’s α)
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Allocate Efficiency
that is, the profit a trader could have made if all market participants would have traded units at the theoretical market equilibrium price, P0
Profit earned by traders as a proportion of maximum theoretical profit
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Profit Dispersion
The deviation of actual profits of traders from the maximum theoretical profit of traders
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Economics, Experiments & Agents • Human vs. Human Markets (1960s onwards)
– Experimental Economics: Vernon Smith established field early 1960s, received Nobel Prize in 2002
• Agent vs. Agent Markets (1990s onwards) – ACE: Agent-Based Computational Economics; AMEC: Agent-Mediated Economic
Commerce; TAC/CAT: Trading Agent Competition / Market Design Competition; MBC: Market-based Control
• Human vs. Agent Markets (2000 onwards) – Very little work in this area…
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Humans vs. Agents 1
• IBM’s Research Labs (2001) – First ever scientific study of the interactions
between human and agent traders in experimental CDA markets
– Showed ZIP and MGD consistently out-perform human traders
– Outcome generated worldwide press coverage
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Humans vs. Agents 2
• Grossklags & Schmidt (2003/2006) – Used own simple agents to explore the
effect of knowledge of the presence of agents on the behaviour of humans
– Found a significant “knowledge effect”: market dynamics were altered just by telling the humans that there were agents in the market
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Humans vs. Agents 3
• De Luca & Cliff 2011 – Replication of IBM’s setup – GDX outperformed ZIP on all metrics – AA outperformed GDX – Concluded that AA is best-performing
published CDA trading strategy
