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 in-house 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 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.