Algorithmic trading facts & fantasies

Transcription

1 Algorithmic trading facts & fantasies J Scott Kerson Head of Commodities AHL / Man Systematic Strategies, Man Group PLC LBMA/LPPM Precious Metals Conference 2013 For institutional/professional and/or qualified investor use only. Not for public distribution. September 2013 Important notes This material is communicated by Man Investments Limited and AHL Partners LLP (the Entities'), which are both registered in England and Wales at Riverbank House, 2 Swan Lane, London, EC4R 3AD. Both are authorised and regulated in the UK by the Financial Conduct Authority. Switzerland: To the extent this material is communicated in Switzerland, this material is communicated by Man Investments (CH) AG, which is regulated and authorised by the Swiss Financial Market Supervisory Authority FINMA. Germany: To the extent this material is communicated in Germany, the communicating entity is Man (Europe) AG, which is authorised and regulated by the Liechtenstein Financial Market Authority (FMA). Hong Kong: To the extent this material is communicated in Hong Kong, this material is communicated by Man Investments (Hong Kong) Ltd and has not been reviewed by the Securities and Futures Commission in Hong Kong. This material can only be distributed to institutional/professional clients who are within one of the professional investor exemptions contained in the Securities and Futures Ordinance and must not be relied upon by any other person. This material is for informational purposes only and should not be relied upon for any other purpose. This material includes facts, views and opinions of global economic markets deemed of interest. This material is proprietary information of the Entities and its affiliates and may not be reproduced or otherwise disseminated in whole or in part without prior written consent from the Entities. The Entities believe its data and text services to be reliable, but accuracy is not warranted or guaranteed. We do not assume any liability in the case of incorrectly reported or incomplete information. Information contained herein is provided from the Man database except where otherwise stated. Please be aware that investment products involve investment risks, including the possible loss of the principal amount invested. Alternative investments can involve significant risks and the value of an investment may go down as well as up. Returns may increase or decrease as a result of currency fluctuations. The data used herein constitutes the latest data available at the time of production. CH/13/0746-P Man Session 2 Kerson 1

2 What do systematic traders actually do? Why do we use algorithmic execution? What do we do? An anecdotal history of systematic trading 1954: IBM introduces the 704, the world s first mass-produced computer with floating point hardware, index registers, and core memory. 1956: Mathematics PhD student Edward O. Thorp observes that a deck of cards has memory the conditional distribution of the next card depends on the previous cards dealt. He uses the IBM 704 to bootstrap a systematic strategy for Black Jack. He doubles his money in his first weekend. 1962: Thorpe publishes his system in Beat The Dealer, becomes a NY Times best-seller, and forces every casino in the world to alter the rules of the game and ban card counting virtually overnight. 1998: After being barred from the casinos, Thorpe applies his research to the stock market, and reports a 20% compound annual gain in his personal portfolio for more than 28 years. Man Source: Man database and Wikipedia. Session 2 Kerson 2

3 Estimating the composition of the deck The empirical response function for FASTCOMM Basis points PnL Standard deviation of signal Man There is no guarantee of trading performance and past performance is no indication of current or future performance/results. Source: Man database Is it an advantage not to read newspapers? Simulation of the Man Commodities Fund systematic overlay and WTI Crude Oil in New super-spike might mean USD 200 a barrel oil (MarketWatch, 7 March 2008; Model is LONG) An ominous warning that the rapid rise in oil prices has only just begun (The Independent, 11 June 2008; Model remains LONG, but starts to CUT) Will oil prices recover after tanking in 2008? (The Telegraph, 29 December 2008; Model remains SHORT) 6 Sample period of backtrack data: 1 January 2008 to 31 December In this backtrack, which corresponds to a simulation of the Man Commodities Fund USD institutional share class. There is no guarantee of trading performance and past performance is no indication of current or future performance/results. Sources: Man database and Bloomberg. Session 2 Kerson 3

4 What do systematic traders actually do? Why do we use algorithmic execution? Algorithmic execution Who s going to win? Man Session 2 Kerson 4

5 The rise of the machines Coming soon to a market near you At least 30% of all US equity market volumes are traded by machines, with some estimates suggesting 60% or more In electronic commodities futures execution, we estimate that algo share has doubled in the last two years, from 20% to 40% High-touch or human-to-human rapidly going to zero for liquid futures US Equities markets: percentage of orders generated by algorithms Percentage of equity volume Man Sources: BloombergBusinessweek, 6 June 2013: How the Robots Lost: High-Frequency Trading's Rise and Fall. Marco Avellaneda, 2011: Algorithmic and High-frequency trading: an overview. The AHL approach: pitting humans against the machines Explicit competition between Virtual Trader and The Desk Large trades Small trades 100% to desk Competitive band 50/50 split 100% to machines Distribution of tickets (COMEX Gold) Average ticket: 15 lots Man Session 2 Kerson 5

6 AHL s evidence on competitive execution Noisy but generally consistent performance Virtual Trader outperforms Implementation shortfall Sugar (NY) Gasoline Natural Gas Crude Oil (ICE WTI) Palladium Wheat (Minneapolis) Silver Feeder Cattle Coffee (Arabica) Gold Soy Meal Crude Oil (Brent) Cotton Gasoil Heating Oil Crude Oil (WTI) Live Cattle Wheat (KC) Rubber Copper (NY) Lean Hogs Sugar (London) Palm Oil Wheat (Chicago) Platinum Rapeseed (Winnipeg) Corn Coffee (Robusta) Soy Beans Cocoa (London) Soy Oil Cocoa (NY) Welsh t-statistic by market Man Electronic competition in the dealer market Improving price discovery Streaming prices from multiple counterparties, often updating several times/second Multi-broker model minimises inside bid-offer spread Direct price feeds reduce information leakage and execution risk from stale information Internal order book enables proprietary execution algorithms overall slippage reduced by around 30% Sample view of internal order book intraday cash gold on 21 st Aug, 2013 Inside spread Outside spread Man Session 2 Kerson 6

7 Competition between marketplaces Maximising market depth Exchange cheaper OTC Cheaper Man The final result Lower costs mean higher returns 1 January 1994 to 30 June 2013 Realised slippage (base year index = 100) Slippage yield 0 Man Session 2 Kerson 7