The TRADETech Daily 2021 Preview edition | Page 26

providers continue to represent an important diversification strategy for the largest longonly managers , downward pressure on research budgets across the industry have tapered enthusiasm over the past 12 months to add more and more providers .

Nonetheless , despite the year-on-year direction of travel , larger managers are still associated with a higher number of providers than smaller managers . This is still down to the requirements associated with managing a larger multi-asset class portfolio . Looking beyond equity algorithms , the rise of algo use in the foreign exchange ( FX ) asset class has grown over the years for spot trading and , recently , has begun to extend to FX derivatives such as non-deliverable forwards . New regulations , such as the uncleared margin rules , are driving FX derivatives into the clearinghouse and fostering more electronic trading . The development of new algos is a natural extension of this phenomenon . Thus , it may be that these managers are holding the number of algo providers somewhat consistent while diversifying the types of algos used by asset class and strategy .

Analysing the number of providers chosen by long-only managers without segmenting results into AUM categories reveals some curious findings ( Figure 4 ). Like last year , 2021 ’ s survey suggests that long-only managers either go hard or go home when it comes to committing resources to providers . The proportion of participants indicating that one provider is sufficient has grown from around 19.86 % in 2020 to just shy of 28.66 % in 2021 . This is in part driven by the growing familiarity and relationships between long-only managers and their providers .

The proportion of firms relying on five or more algo providers has fallen from 41.13 % in 2020 to 32.54 % in this year ’ s survey . The reason for this phenomenon is that cost pressures have pushed managers to consolidate and streamline their relationships , even though clearly a significant number of providers still see the diversification benefits of leveraging a handful of different algo providers .

The distribution of algo usage by value traded has changed considerably over the past year ( Figure 5 ). The proportion of participants trading 80 % or more of their portfolio via algo trading almost doubled from 10.98 % in 2020 to 20.75 % in 2021 . At over a fifth of all long-only respondents to this year ’ s survey , this group of managers trading 80 % + of their portfolio algorithmically now represents the largest proportion of survey participants . Additionally , the year-on-year increase for this group is the largest of any bracket . Long-only funds allocating 20 % to 30 % of their portfolio value into algos grew to 12.19 %, up from 7.65 % in 2020 in the second largest increase of any bracket . At the lower end of the spectrum , 6.82 % of participants trade between 5 % and 10 % of their portfolio ’ s value using an algorithm ( versus 8.43 % a year ago ). The most significant year-on-year decrease was seen in

Ease of use 12.04 11.08 Consistency of execution performance 10.19 10.51 Increase trader productivity 10.32 10.45 Reduce market impact 10.45 10.29 Greater anonymity 8.96 9.93 Flexibility and sophistication of SOR 7.24 8.02 Algo monitoring capabilities 5.30 7.20 Lower commission rates 8.69 6.83 Better prices ( price improvement ) 6.68 6.65 Higher speed lower latency 7.64 6.56 Customisation capabilities 6.21 5.74 Data on venue / order routing logic or analysis 3.84 5.07 Results match pre-trade estimates 2.45 1.67

Figure 3 : Average number of providers used by AUM ( USD billions )

the 50 % to 60 % segment , which declined from 22.16 % of long-only funds trading via algos last year to 9.65 % in this year ’ s survey .

At first glance it seems that more firms are pushing a larger proportion of their book into algorithms and this is partly a consequence of the developments in technology and software that has enabled algo trading in equities as well as other asset classes . The percentages of funds trading via algos have risen since last year in the three largest categories : 60 % to 70 %, 70 % to 80 % and above 80 %. All the same , the fact remains that the percentage of respondents trading 50 % or more of their portfolio algorithmically has remained essentially the same : 49.41 % versus 49.22 % last year . This is a reminder that for all the benefits of algorithmic trading , the industry still values the role of human discretion and it remains the case that many instruments and asset classes do not lend themselves easily to algo trading .

Long-only managers were asked to select the types of algorithms they used from providers ( Figure 6 ). Like last year , in 2021 the highest proportion of surveyed long-only funds chose dark liquidity seeking algos ( 59.78 %). Further proposals by European regulators to further

2021 2020 Up to 0.25 2.13 2.14

0.25-0.5 2.50 1.83

0.5-1 2.64 2.00

1 to 10 2.94 3.33 10 to 50 3.47 4.25 More than 50 3.89 4.02

clamp-down on dark venue trading have led to resistance from many asset managers who have responded to public consultations to remind policymakers of the value that dark venue trading provides large buy-side managers . This is partly behind the continued high adoption of these kind of algos in recent years , though it is noticeable that the percentage choosing dark liquidity seeking algos has fallen by 13.16 % from 72.94 % in 2020 . Clearly over the past year long-only firms have become mindful of a broader range of considerations when it comes to their choice of algo .

The percentage-of-participation algos rose to 56.96 % from 49.02 % last year , indicating strong preference for the ability to participate in volume at a user-defined rate . Two algos that have been in existence for years , volume weighted average price ( VWAP ) and time weighted average price ( TWAP ), both saw year-on-year increases in its adoption in this year ’ s survey coming in at 59.51 % and 25.75 % respectively . At a time of extreme market uncertainty and unpredictability , it is expected that participants turn to tried and tested methods of algo trading to navigate execution venues .

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