The New TCA: Can It Make You Smarter?
Transaction Cost Analysis (TCA) is having a moment. Or rather a multi-year uptick in adoption, to be more precise. According to recent Greenwich Associates’ research, two-thirds of all buy-side and 88% of equity desks in the US and Europe now rely on TCA. In addition, with 60% of foreign exchange and 38% of fixed income trading desks already utilising TCA, it looks set to play an increasingly important role across all asset classes. No longer is it just a high-level monitoring or tick the box exercise, TCA has become an integral and dynamic component of best execution.
So how should TCA ideally function to provide best-in-class value in this post-MiFID II, microsecond-measuring world?
Meet regulations and meet expectations
Recent regulations have upped the ante on compliance for the buy-side. MiFID II and strengthened SEC rules specify the obligation to “take all sufficient steps to obtain, when executing orders on behalf of clients, the best possible result” rather than “all reasonable steps”- a significant tightening of language over the original MiFID directive. Similarly, a greater emphasis on the fairness of price, and higher level of detail characterize the required best execution policies and procedures, replacing the historically more subjective wording. Furthermore, with clear remedies and penalties in place for non-compliance, end clients demand increasing levels of transparency. Finally, the new rules extend not only to exchange-traded equities but also include Listed Derivatives, FX (excluding Spot) and Fixed Income.
Fortunately, solutions exist to meet the growing demand for such information. Enhanced TCA solutions can integrate across the entire order life cycle and within the OEMS framework, covering multiple asset classes, ensuring accurate measurement and monitoring of best execution, and compliance with the latest regulations. Best-in-class TCA can also provide on-demand reporting, and include intuitive visualisations to equip clients and internal stakeholders with timely information and transparency on all trades.
Balance the risks and turn challenge to opportunity
Today’s buy-side institutional traders face the daily challenge of meeting seemingly contradictory goals: they have to dissect large orders into smaller trades to mitigate market impact while limiting the potential costliness and increasing risk of longer trading horizons.
Best-in-class TCA can help make this happen. Here’s how:
Consistently measure what matters.
TCA results can now inform the feedback loop, refining the trading algorithms that make best execution possible. To provide this function, the measurement tool must deliver meaningful pre, post and real-time analytics and insights that can shape execution strategy, inform decisions and influence real-time trading tactics. It must also have the capability to measure execution performance across multiple venues and order routing types.
Traditional TCA metrics such as VWAP still play a pivotal role. However, these must work in conjunction with more granular and dynamic tools that pick apart algorithmic order routing and analyse individual venue performance under multiple conditions in relation to more defined child-order measurements- recording conditional hit rates and fill rates in microsecond increments.
Accesses all relevant data points.
To accurately and consistently calculate the performance of each trade, TCA draws on data. And it must be the right data—accurate and representative, real-time and historical.
With timely access to relevant data points, as well as the ability to toggle back and forth between big picture benchmarks and internal data sources, meaningful metrics can evaluate sell-side counterparties, venues, platforms, market timing, algorithmic strategies and more.
Functions within a seamlessly connected trading stack.
Behind every great algorithm, lies an ‘AMS.’ (Or Algorithmic Management System, as Larry Tabb so aptly dubbed.) Without this automated and integrated flow of data and analytics throughout the OEMS framework, high-performance algorithms and the measurement tools that track and create them would not exist. Such interconnection makes it possible to use post-trade data to inform pre-trade decisions and do it whilst that post-trade data still matters. It powers the fast and agile processing of analytics that can benefit trading outcomes in real-time. It also provides the possibility to leverage new technologies aimed at mitigating the ever-present market impact versus market timing risks- such as the ability to place conditional orders that simultaneously place the same liquidity across multiple venues, relying on metrics that align and gauge data at the microsecond level to trigger the optimal trade.
The next crucial differentiator is customisation. As markets grow faster and more complex, best execution demands every achievable edge, hence the do-it-yourself option is gaining traction. Previously viewed as too labour intensive and difficult to implement, the interconnection of key data sources, combined with real-time reporting and intuitive visualisations, allows the buy-side to build custom algos and put them into action.
Furthermore, the ‘AMS’ construct extends across asset classes. It’s true that data sources differ and that metrics may diverge in terms of their consistency and that the various instrument classes vary in terms of liquidity. However, the adoption of open APIs is increasing, and this enables the automation of order generation and trade execution via the interconnection of a broader range of reliable and agreed-upon data sources. As automation continues to evolve, recent studies and industry experts suggest further consolidation with single, multi-asset class trading-desk will prove optimal.
Market structure and regulatory changes have demanded a new level of TCA. Today’s best-in-class solutions integrate with all of the key data sources to automate best execution across a complex trading landscape. This evolution is part of a simpler and smarter future: one where traders can leverage the intelligence of real-time analytics throughout a fully integrated OEMS set up and across all of the eligible asset classes. One that also allows for the customisation of algorithms on an iterative basis to reflect new learning and market insights and to put them into practice as rapidly and as efficiently as possible.