Optimizing Trading with Transaction Cost Analysis
As trading has become increasingly complex and markets have evolved, transaction cost analysis (TCA) has shifted from a compliance function primarily for equities trading to one that actively drives front-office decision-making across all asset classes. Today, multi-asset TCA is an essential tool for both buy-side and sell-side firms in optimizing trade execution, managing risk and meeting regulatory requirements.
Recognizing the criticality of TCA, TT acquired Abel Noser Solutions, a pioneer in the space for 50 years, in 2023. Then last summer, we expanded the offering beyond equities, options, fixed income and FX to also cover futures. Now TCA, once an unknown acronym in the listed derivatives space, regularly comes up in conversations with our clients.
More Than a Compliance Checkbox
Historically, TCA gained traction due mainly to regulatory policies, such as MiFID II, which mandated best execution as an obligation for firms. However, over the past 15 years, TCA has increased its relevance and application outside simple regulatory compliance. We talked about this at our TT Connect: Unlocking Profit with Data & Analytics event last September.
One of our panelists, Sharon Bains-Kler, Director – Futures and Options & OTC Clearing at Bank of America, said: “Regulation was the main catalyst in the past, but now TCA is about execution quality, saving on the implicit cost of trading and ensuring that you are getting optimal execution across your trades.”
TCA enables firms to capture, analyze and minimize implicit trading costs—such as slippage, market impact and timing inefficiencies. Together, these costs can significantly erode the profitability of trades, which is driving the need for increasingly advanced applications of TCA.
Another panelist, Diarmuid O’Keeffe of Irish Life Investment Managers, said: “We are starting to get more sophisticated with TCA. Previously, we primarily used it for daily reports; now, we’re exploring trade optimization and how it integrates into our TCA workflow.”
The integration of post-trade data in pre-trade decision-making provides a key advantage for firms. Asset managers increasingly build broker league tables, which incorporate various metrics such as market liquidity and execution performance.
These metrics play a crucial role in selecting counterparties for each trade, allowing traders to rank brokers and assess performance based on both qualitative and quantitative factors, as well as ensuring that trading decisions are continuously optimized in response to market conditions.
Recognizing this demand, TT recently launched TT Broker Scorecard, a first-of-its-kind monthly report that ranks global and regional equity brokers by liquidity and execution quality. The data included in the report helps buy-side market participants easily identify and vet brokerage firms that trade in specific market segments, then pinpoint broker liquidity and estimate cost before trade execution. Equally, sell-side firms can identify both their competitive strengths and areas for improvement, and promote themselves in the market.
Inputs to TCA calculations are becoming more sophisticated, and TCA is now being calculated across a wider range of factors, such as market color and liquidity, to enhance traditional metrics. Ultimately, anything that is measurable and trackable can be added to the overall calculation.
TCA Across Asset Classes
Although TCA is widely used in equities and futures markets, its adoption across other asset classes such as fixed income remains challenging—but this is growing.
Baines noted that applying TCA to other asset classes requires a tailored approach for each. “TCA usage in futures is almost as sophisticated as in equities, but the unique characteristics of listed derivatives makes it difficult to apply the same tools across the board,” she said.
In the fixed income market, the fragmented nature of liquidity and the challenges in reconciling different market microstructures are key obstacles to broader TCA adoption. Despite these challenges, the market is evolving, and increased trading electronification is likely to drive broader TCA adoption.
Looking to the Future: AI and Automation
Another key trend is automation, which is playing an increasingly important role in TCA as more institutions deploy automated tools to route smaller trades while allowing human traders to focus on larger, more complex orders. At the same time, more complex trades, such as portfolio trades, are being incorporated into TCA.
Firms are also adopting intraday TCA, which enables them to monitor slippage and adjust orders in real time. In highly liquid markets like futures, for instance, being able to switch to a more aggressive trading strategy mid-session can help reduce momentum costs and optimize execution.
Looking ahead, the use of machine learning and artificial intelligence (AI) in TCA is expected to become more prominent. The integration of AI tools will allow for the development of more advanced feedback loops, where real-time data can influence trade decisions dynamically. This could create a more sophisticated trading environment where TCA not only informs past and future trades but also adjusts strategies during trades in progress as a fully automated process.
TCA is rapidly evolving into a comprehensive tool that not only enhances trade optimization and improves compliance, but also provides crucial insights into trading strategy efficiency. As financial markets become more complex, the need for more sophisticated TCA tools—capable of handling multi-asset class analysis, real-time data integration and advanced automation—will only grow.
One of our key operating principles at TT is that data and analytics are the cornerstone of profitable and cost-effective trading strategies. From TT Futures TCA to TT Broker Scorecard, we’re continuing to build new solutions that can help market participants to optimize their trade execution and ultimately maximize profitability. Keep an eye out for more to come.