LBMA/LPPM Precious Metals Conference
30/09/2013
Algorithmic trading facts & fantasies
J Scott Kerson
Head of Commodities AHL / Man Systematic Strategies, Man Group PLC
LBMA/LPPM Precious Metals Conference 2013
September 2013
For institutional/professional and/or qualified investor use only. Not for public distribution.
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Important notes
© Man 2013 2
What do systematic traders actually do?
Why do we use algorithmic execution?
An anecdotal history of systematic trading
Source: Man database and Wikipedia.
© Man 2013 4
What do we do?
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
LBMA/LPPM Precious Metals Conference
30/09/2013
Source: Man database
There is no guarantee of trading performance and past performance is no indication of current or future performance/results.
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Estimating the composition of ‘the deck’
The empirical response function for FASTCOMM
B a si s p o in ts P n L
Standard deviation of signal
Simulation of the Man Commodities Fund systematic overlay and WTI Crude Oil in 2008
Sources: Man database and Bloomberg.
Sample period of backtrack data: 1 January 2008 to 31 December 2008. 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.
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Is it an advantage not to read newspapers?
“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)
“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)
“Will oil prices recover after
tanking in 2008?”
(The Telegraph,
29 December 2008; Model
remains SHORT)
“New ‘super-spike’ might
mean USD 200 a barrel oil”
(MarketWatch, 7 March 2008;
Model is LONG)
“New ‘super-spike’ might
mean USD 200 a barrel oil”
What do systematic traders actually do?
Why do we use algorithmic execution?
© Man 2013 8
LBMA/LPPM Precious Metals Conference
30/09/2013
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
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.
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The rise of the machines
US Equities markets: percentage of orders generated by algorithms
Per c entage of equity volum e
Source: Man database.
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The AHL approach: pitting humans against the machines
Explicit competition between Virtual Trader and The Desk
100% to machines 100% to desk Competitive band – 50/50 split Large trades Small trades
AHL’s evidence on competitive execution
Source: Man database.
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-2 -1 0 1 2 3 4
Cocoa (NY)Soy Oil Cocoa (London)Soy Beans Coffee (Robusta)Corn Rapeseed (Winnipeg)Platinum Wheat (Chicago)Palm Oil Sugar (London)Lean Hogs Copper (NY)Rubber Wheat (KC)Live Cattle Crude Oil (WTI)Heating Oil Gasoil Cotton Crude Oil (Brent)Soy Meal Gold Coffee (Arabica)Feeder Cattle Silver Wheat (Minneapolis)Palladium Crude Oil (ICE WTI)Natural Gas Gasoline Sugar (NY)
Noisy but generally consistent performance
Welsh t-Statistic by market
Virtual Trader outperforms
Im
p
le
m
ent
a
tion s
h
o
rt
fa
ll
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%
Source: Man database.
Sample view of internal order book – intraday cash gold on 21
stAug, 2013
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Electronic competition in the dealer market
Improving price discovery
Inside
spread
LBMA/LPPM Precious Metals Conference
30/09/2013
Source: Man database.
© Man 2013 13
Competition between marketplaces
Maximising market depth
OTC Cheaper
Exchange cheaper
1 January 1994 to 30 June 2013
Source: Man database.
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