The TRADE 2018 APAC Algorithmic Trading Survey | Page 7

[ A PA C
A L G O R I T H M I C
T R A D I N G
S U R V E Y ]
Credit Suisse
C
redit Suisse recorded a number of impressive
scores from respondents in this year’s survey,
comfortably beating the survey-wide average. The
firm recorded scores higher than 6.00 in half of the
12 algo functionalities under review, with its best
results coming in the speed (6.47), improving trader
productivity (6.31) and customisation (6.23) categories.
Alongside this, Credit Suisse also significantly outper-
formed the survey average for reducing market impact
(0.37), crossing (0.33) and execution consistency (0.41)
categories. There were some areas where the firm
failed to outperform, although this was most often by a
difference of less than 0.1.
Just over two-thirds of Credit Suisse respondents
said they are managing more than $50 billion in
assets, while the rest were primarily from the smaller
end of the AuM brackets (managing less than $1
billion). The same proportion of firms are using algos
to execute more than 60% of both their overall flow
and its value.
Improve
trader
productivity Reduce
market
impact Execution
consistency Cost Speed Anonymity Price
Customisation
improvement Ease of use Crossing Execution
consulting Customer
support
6.31 6.19 6.28 5.57 6.47 6.07 5.96 5.96 5.85 5.86 5.70
6.23
Goldman Sachs
G
oldman Sachs will have every reason to be disap-
pointed with the results of this year’s APAC algo
survey, wherein the firm consistently underperformed
against the overall average and the specific areas
under review. Respondents for Goldman Sachs judged
its algo capabilities to be lacking in key areas, at least
in comparison with its peers, with particularly low
scores in the cost (4.80), crossing (5.04) and customi-
sation categories (5.07). The firm’s best scores were in
the improving trader productivity (5.64) and ease of
use (5.48) categories, while it recorded substantially
lower scores than the survey average for cost (-0.89),
execution consistency (-0.75), speed (-0.73) and cus-
tomer support (-0.69).
Just under half of respondents for Goldman Sachs
were from the top AuM bracket (more than $50
billion), with a significant portion coming from the
lowest end of the scale (up to $0.25 billion). Just under
one-third of respondents said they had increased
usage of Goldman Sachs algos year-on-year.
Improve
trader
productivity Reduce
market
impact Execution
consistency Cost Speed Anonymity Price
Customisation
improvement Ease of use Crossing Execution
consulting Customer
support
5.64 5.31 5.12 4.80 5.14 5.20 5.19 5.48 5.04 5.30 5.11
5.07
Issue 58 // TheTradeNews.com // 95