When it comes to multi-asset data, how are firms treading the line between specialism and standardisation with vendors?
The majority of analytics vendors entered the market as specialists in a particular asset class. Over the years, this has gradually transformed to vendors supporting multiple asset classes at a granular level. As these firms have been enhancing their other offerings, I believe there has been more standardisation in the market where users are starting to consolidate some their analytics providers.
The optimal scenario in my opinion, would be using one vendor for all analytics requirements; however, I personally cannot see this occurring in the short/medium term for many firms. Contractual obligations, EMS links and wider business use of platforms, are some of the reasons why standardising is not as easy as it sounds.
Which technologies are most impactful currently in the multi-asset trading and data landscape?
Along with the standard OMS/EMS’ functionalities, the ability for these systems to incorporate granular analytics which aide the desk, are real differentiators. Being able to click on a live order and immediately viewing a breadth of information, is something which is valued heavily by analytics teams and traders.
API capabilities via platforms is an area that continues to grow in popularity, whether it’s through TCA/analytics products or market data portals. This type of offering allows users to amalgamate data internally and utilise for various onward processes such as model inputs or internal TCA measurements.
Lastly, I believe enhanced offerings in the high touch crossing space on the trading desks is something which traders value highly. Crossing opportunities for larger order sizes have always been popular in the market to reduce market impact and implicit transaction costs, however this has become essential given the liquidity challenges in the European markets. There are several providers in this space who are improving their functionality and portals to streamline the search for natural liquidity.
How are traders and data teams working to evolve TCA?
TCA is an ever-evolving subject matter which means traders and analytics teams must adapt or be left behind their industry peers.
As APIs have grown in popularity over the years, one of the main advantages has been seamless access to TCA measurements, market data, trading data and vendor models. Vendor TCA portals and accessing their APIs, provide an abundant source of information which can be used in several different ways to inevitably reduce transaction costs. Quantitative methods can be applied to the datasets to provide statistically significant conclusions, which can then be dispensed across the business to relevant teams.
It’s extremely important that the trading desks are also in tune with the analytics to ensure efficient onwards processes like liaising with brokers on their trading performance. Market conditions in all aspects of trading analytics are vital to normalise results and safeguard against judging the sell-side on minimal information e.g., broker A shows reversion post order execution but was the stock/index interval momentum in the same direction during and after the order?
Drilling deeper into brokers’ venue choices has also become imperative to analyse on the buy-side. The ability to scrutinise venue performance, such as toxicity, avg. fill sizes, and spread captures amongst others, has strengthened the relationship between both sides of the market.
Can the use of TCA in fixed income mirror equities?
Fixed income markets are fundamentally opaque and price transparency is a big issue for TCA. How do you effectively measure an order in a high yield bond which has only traded once in the last few weeks? Best execution regulation promotes measurement of all asset classes, irrespective of liquidity. Several TCA vendors are adapting their approach to measurement by creating proprietary pricing models which pull in various data sources such as ticks, quotes, liquidity scores and platform data, as well as creating yield curves to battle the price transparency difficulties.
Traditional equity benchmarks like IS and IVWAP are generally not relevant for voice traded fixed income securities, given the difficulty in capturing timestamps accurately, however, it’s possible to utilise with any electronic flow where this information is readily available. Use of relative benchmarks are becoming more popular, such as execution vs. far touch to represent spread capture, as well as accessing pre-trade cost estimates for performance comparisons.
Another way that fixed income TCA would differ from equities would be the analysis of spread to the bond benchmark at arrival vs. execution time. This would allow relative measurement over the order duration which can then be compared to absolute arrival results.
There are many commonalities between TCA for different asset classes, however distinctions will always exist in the methodology to account for specific nuances.