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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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 2

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 3

    Financial-Market Trading Agents Continually Grab the Headlines…

    But why is everybody [suddenly] so interested?

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 4

    ‘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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 5

    And then, on October 27th 1986, the markets were changed forever…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 6

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 7

    ‘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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 8

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 9

    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]

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 10

    •  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’

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 11

    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…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 12

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 13

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 14

    May 6th 2010, ‘Flash Crash’

    Within 20 minutes, Dow plummets 9% and then largely recovers the loss

    New York Times

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 15

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 16

    Some stock, like Accenture, plummeted to 1 cent…

    May 6th 2010, ‘Flash Crash’

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 17

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 18

    and then, one year later…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 19

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 20

    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…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 21

    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…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 22

    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)

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 23

    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)

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 24

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 25

    Profit Dispersion

    The deviation of actual profits of traders from the maximum theoretical profit of traders

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 26

    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…

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 27

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 28

    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

  • 07/02/2012 John Cartlidge, ICAART-2012, Portugal 29

    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