[ A L G O R I T H M I C
T R A D I N G
S U R V E Y ]
“Trading in the dark has also been a hot topic
within the context of the new regulations and
its importance is clearly being recognised.”
merely be testing the waters and
shopping around for the best func-
tionality before settling on long-term
strategic relationships with select
providers in future, but it will be
interesting to see what the results in
2019’s version of the survey show.
Shifting favour
In terms of how much hedge funds
have been using algos over the past
year, there were some interesting
changes regarding how much
value was traded in this manner,
as shown in Figure 5. Last year
showed hedge funds were more
likely to predominantly use algos
for their trading strategies – over
20% of respondents said more than
80% of value traded was via algo
– or for more specialised purpos-
es with around 20-30% of value
MEASURING FUNCTIONAL CAPABILITIES
Survey respondents (both long-only and hedge funds) were asked to give
a rating for each algorithm provider on a numerical scale from 1.0 (very
weak) to 7.0 (excellent), covering 14 functional criteria. In general, 5.0 is
the ‘default’ score of respondents. In total, more than 30 providers re-
ceived responses and the leading banks obtained dozens of evaluations,
yielding thousands of data points for analysis. Only the evaluations from
clients who indicated they that they were engaged in managing long-only
firms or hedge funds have been used to compile the provider profiles and
overall market review information.
Each evaluation was weighted according to three characteristics of each
respondent: the value of assets under management; the proportion of
business done using algorithms; and the number of different providers
being used. In this way the evaluations of the largest and broadest users
of algorithms were weighted at up to three times the weight of the
smallest and least experienced respondent.
Due to the changing market conditions as a direct result of the intro-
duction of MiFID II, the researchers decided to reinstate short profiles of
the leading providers, which were not included in the 2017 edition of the
survey. Each profile outlines their share of responses, including a compar-
iso n with 2017 and the overall survey outcomes.
Finally, it should be noted that responses provided by affiliated entities
are ignored. A few other responses where the respondent could not be
properly verified were also excluded. We hope that readers find this ap-
proach both informative and useful as they assess different capabilities
in the future.
78 // TheTrade // Summer 2018
traded via algo.
This year, hedge funds seem
to have shifted their strategies
in favour of algo trading, with a
significantly higher proportion of
respondents indicating that more
value has been traded using algos.
Just under half of hedge fund
respondents said algo trading rep-
resents over 60% of value traded,
while even the more modest range
from last year increased, with over
15% of respondents saying 30-40%
of value traded was conducted via
algo.
Clearly the increasing importance
of easy-to-use and consistent algos
is driving hedge funds to use this
method for more of its trading
activity, although this may again
come back to firms adopting mul-
tiple algos from different providers
in the short-term before reducing
the volume of those relationships.
When it comes to which types of
algos hedge funds are choosing to
use within their trading strategies,
this year’s survey showed a similar
attitude to long-only firms, dis-
played in Figure 6, whereby a move
away from dark liquidity seeking
algos was the most significant
trends. In last year’s survey over
70% of hedge fund respondents
said they used these types of algos
and that proportion fell to around
58% this year, while there were
also significant decreases in firms
using participation based and im-
plementation shortfall (both basket
and single stock) algos, whereas
TWAP and VWAP algos saw
relatively consistent usage. The
increase in using algos under the
“Other” category may indicate that
firms are embracing new algo types