[ T H O U G H T L E A D E R S H I P | B E R E N B E R G ]
Berenberg ’ s TIA , The Next Frontier in Execution Analysis
Over recent years , buy- and sell-side collaboration has driven algorithmic development , resulting in innovative ways of provisioning liquidity in a rapidly evolving landscape . The recent shift towards more dynamic , learningbased algorithmic models have positively contributed to the buy-side achieving trading objectives which ultimately have yielded better outcomes for all participants .
And yet , despite the crystallisation of Mifid II and the eventual settling of Brexit , the buy-side ’ s continued task to achieve the optimal rate of trading as they profile orders and balance the trade-off between liquidity and impact , seems ever more daunting . Whilst the buy-side remains vigilant in understanding where it is best to make future algorithmic innovations , the sell-side is keen to explore whether large-scale investments are economically viable . The question seemingly plaguing both participants at current is whether these innovations have evaluation models ( Pre / Post trade TCA ) capable of measuring their incremental value .
In response to profound market microstructure transformations ,
“ The Times They Are A-Changin .” Jason Rand , global head of electronic trading and distribution at Berenberg , sets out how democratising access to quantitative execution analytics and data visualisations can lead to better trading outcomes for participants , irrespective of size .
buy-side execution preference has shifted dramatically from schedule-based algorithms , typically VWAP and participation-based strategies , to Implementation Shortfall and Liquidity Seeking algorithms .
However , this fundamental trading shift has not been accompanied by an evolution in benchmarking methodologies that accurately assess the quality of these algorithms or their performance within a wheel framework . Most buy-side firms are utilising platforms that measure only raw IS without adjusting for exogenous factors such as expected trading costs ( pre- and intra-trade ), market regimes , or inherent alpha present in the order flow . This creates intrinsic bias in the evaluation process , and produces misleading results whilst making it difficult to determine what execution “ quality ” actually is .
Rand believes it is time to establish a more standardised and forensic analytical framework to help the buy-side make real structural changes to their execution decision-making . “ Whether it ’ s the construction of broker algo wheels , classification of orders , or measurement of discretionary buy-side trader performance ( through trader amends ), our aim in building TIA ( Trading Intelligence Analytics ) is to democratise advanced trading analytics using dynamic data visualisation which will empower the buy-side to make more consistent and informed decisions .”
12 // TheTRADE // Q4 2022