The TRADE 84 - Q2 2025 | Page 64

[ A L G O R I T H M I C T R A D I N G S U R V E Y ]
for the recent decline, however. First, overall adoption of dark liquidity could be declining due to regulatory changes that limit the use of dark pools and emphasise the importance of transparency. Secondly, continued enhancements in efficiency of lit markets could be reducing the need to tap into dark pools. Lastly, the potential for information leakage in the dark markets which could benefit more sophisticated counterparties could be a discouraging factor for seeking liquidity in the dark pools. In relation to more longerterm trends, implementation shortfall( basket) has more than doubled over the last five years( up from 10.91 % in 2021 to 24.67 %), reflecting growing automation and multi-asset execution capabilities on desks. Other types of algorithms have also risen steadily, indicating potential use of custom or niche strategies among some hedge funds.
Figure 5: Algorithm usage by value traded(% of responses)
Percentage of respondents
2025
2024
2023
Not answered
7.33
6.67
2.67
0- 5 %
4.00
5.33
14.67
5- 10 %
6.00
4.67
5.33
10- 20 %
2.67
6.67
14.67
20- 30 %
4.00
6.67
6.67
30- 40 %
6.67
4.00
5.33
40- 50 %
6.67
7.33
5.33
50- 60 %
11.33
12.67
13.33
60- 70 %
9.33
6.67
9.33
70- 80 %
9.33
10.67
9.33
> 80 %
32.67
28.67
13.33
Figure 6: Types of algorithms used(% of responses)
Algo type
2025
2024
2023
VWAP
74.00
74.67
70.24
Dark liquidity seeking
68.00
74.00
84.52
% Volume( participation)
66.67
62.00
69.05
Target Close / Auction Algos
48.67
54.67
45.24
Implementation shortfall( single stock)
46.67
44.00
45.24
TWAP
42.00
45.33
27.38
Implementation shortfall( basket)
24.67
20.67
15.48
Other
8.00
5.33
7.14
Methodology Buy-side survey respondents were asked to give a rating for each algorithm provider on a numerical scale from 1.0( very weak) through to 7.0( excellent), covering 15 functional criteria. In general, 5.0( good) is the‘ default’ score of respondents. In total, 2189 provider ratings were received from a record number of 614 individual respondents, across 34 algo providers, yielding thousands of data points for analysis. Only the evaluations from clients who indicated that they were engaged in managing hedge funds or using hedge fund strategies 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. Finally, it should be noted that some responses provided by affiliated entities were ignored. A few other responses where the respondent could not be properly verified were also excluded. We hope that readers find this approach both informative and useful as they assess different capabilities in the future.
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