[ A L G O R I T H M I C
T R A D I N G
S U R V E Y ]
Bank of America Merrill Lynch
B
ank of America Merrill Lynch (BAML) recorded
a significant increase in the level of percentage of
responses it received from hedge funds, over dou-
ble the percentage it received in last year’s survey,
ranking joint-sixth in terms of response levels this
year. Just over half of hedge fund respondents for
BAML managed over $10 billion of assets, although
there was a relatively even split of respondents across
AuM brackets. BAML recorded increased year-on-year
scores in nine of the 15 categories under review in this
year’s survey, with an average score of 5.70 (up 0.28
from 2018), fractionally below the survey-wide aver-
age score of 5.72. The most significant year-on-year
improvements were in the speed (up 0.63), dark pool
access (up 0.48) and cost (up 0.73) categories, while
the bank’s best area of performance was in customer
support, where it recorded a score of 6.15, which rep-
resented a year-on-year improvement of 0.89. BAML
also received a score of 6.10 in the anonymity category.
Meanwhile there were decreased year-on-year scores
from hedge fund respondents in the customisation
(down 0.45), ease of use (down 0.30) and execution
consulting (down 0.13) categories.
BANK OF AMERICA MERRILL LYNCH RATINGS FOR ALGORITHM PERFORMANCE
Increase trader
productivity Reduce market
impact Execution
consistency Cost Speed Anonymity Price
improvement Customisation
5.80 5.31 5.66 5.74 5.80 6.10 5.44 5.17
Ease of use Customer support Execution
consulting Dark pool access Flexibiltiy and sophistication
of smart order routing Algo monitoring
capabilities Data on venue/order
routing logic or analysis Average
score
5.44 6.15 5.70 5.67 5.90 6.05 5.61 5.70
KEY STATS
59%
6.15 5.17 Highest score
(customer support ) Lowest score
(customisation) +0.89 -0.45 53%
Best year-on-year score
(customer support) Worst year-on-year score
(customisation) Most used
algo performance
measurement method:
Implementation shortfall TCA
86 // TheTrade // Summer 2019
Most popular non-equity asset
traded via algo by respondents:
Exchange-traded funds