[ A L G O R I T H M I C
T R A D I N G
Hedge fund 2019
Hedge fund 2018
Hedge fund 2017
Figure 3: Average number of providers used by AUM
2.00
2.40
2.33
Not answered
1.67
Up to $0.25 billion
2.00
1.84
3.00
1.50
$0.25 - 0.5 billion
2.20
1.80
$0.5 - 1 billion
4.50
1.56
3.55
$1 - 10 billion
4.79
3.93
4.58
$10 - 50 billion
4.50
4.33
More than $50 billion
3.68
0.00
1.00
this year’s survey both received
respectable scores – 5.72 for data
on venue/order routing logic or
analysis, and 5.63 for algo moni-
toring capabilities, slightly above
the scores recorded from long-only
respondents.
Similarly to the responses from
long-only buy-side firms, the
scoring in this year’s survey sug-
gests that efficiency has become
the key watchword for algo end
users. While cost will always be
a factor for trading operations,
improved perceptions around
trader performance, price
improvement, the ability to
customise algos for different
trading strategies and the
ease with which they can be
used indicate that algo pro-
S U R V E Y ]
2.00
3.00
4.00
4.80
5.20
5.00
6.00
viders are stepping up their efforts
to optimise automated trading for
their clients.
Hedge fund firms continue to
adopt and use algos for much the
same reasons as they have histor-
ically done, according to Figure
2, regardless of the regulatory
landscape. The importance of
consistent execution performance
(9.00%), an increase in trader
productivity (10.52%), reduction in
market impact (10.45%) and ease
of use (11.10%) were once again
selected as the main reasons for
hedge funds to use
algorithms within
their trading opera-
tions, although price
improvement has
also steadily become a
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