The TRADE 58 | Page 95

[ 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