THETRADETECHFX DAILY from the floor
How are use cases for multi-asset TCA changing on the buy-side ? What is driving this demand ? Over the past decade , technological advancements and increasing financial regulations have been the primary drivers behind the evolution of trading analytics and transaction cost analysis ( TCA ). These components are the backbone of the PGGM trading analytics desk . Our mission is to optimise the transaction chain , provide comprehensive insights into trading processes and order flows within financial markets , and ensure accountability in order execution . We firmly believe that measurable insights from transaction data enhance both understanding and control over the trading process , leading to more efficient order execution and improved outcomes for our client . Initially , we focused on best execution reporting to comply with Mifid II requirements . However , we have evolved into leveraging data-driven TCA processes and are currently building a multi-asset data platform . This platform aims to offer our clients deeper insights into the efficiency and cost-effectiveness of their trades , provide a complete feedback loop ( including pre- and post-trade analysis ), and support the decision-making process for portfolio managers .
What are the pros and cons of building in-house versus using a third-party provider ? Using a third-party provider often results in solutions that are designed to meet the needs of the average user . In contrast , developing inhouse allows us to customise every feature specifically to our business requirements . This approach enables us to combine and utilise information from multiple data sources without the constraints often imposed by external vendors . By integrating this data into a comprehensive platform , we can generate insights that would be unattainable from a single source , yielding more detailed information on brokers , algorithms , and trading venues . These insights can then be transformed into actionable business intelligence , providing us with a robust dataset for our models . However , a significant drawback of our in-house approach is the difficulty in benchmarking our performance against industry peers . Many TCA data vendors offer anonymised peer comparisons , which can be valuable . Given that our trade execution processes are highly customised , the value of such peer comparisons is limited . Therefore , we
Achieving TCA maturity on the trading desk
NOORTJE DRAPER , trading analytics lead at PGGM Investments , sits down with The TRADE to explore multi-asset TCA use cases , the pros and cons of building in-house , and the shift from monitoring to actively optimising trading strategies using pre-trade data .
are exploring alternative benchmarking methods to compare our trades .
How can TCA use be further optimised / automated on the trading desk ? Our desk has reached a level of maturity that allows us to meet all reporting and regulatory requirements . In addition , we provide traders and portfolio managers with easy access to their trading data , offering them a realistic view of their performance , beyond standard metrics like turnover . We are now shifting from merely monitoring to actively optimising , starting with equities and fixed income , and extending to FX next year . This transition includes providing explicit pre-trade insights and integrated trade signals , ensuring that portfolio managers and traders have all the relevant information at the time of decision-making . With these insights , we can further specialise our trading strategies and in the future probably automate more standardised trades . Additionally , we are exploring the development of in-house trading algorithms tailored specifically to our trading needs .
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