TOO FAST – TOO FURIOUS: Faster Financial-Market ... cszjpc/slides/cartlidge... TOO FAST – TOO FURIOUS:

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