DEFINING THE GOAL.

In an ever more complex trading world, Mike Googe, product head of post trade analytics at Bloomberg explains why the quality of results in transaction cost analysis for multi-asset operations depends on the right questions being asked.

Improving trading and investment performance
is a common goal for any desk, irrespective of
the asset class being traded. Just as important
is demonstrating compliance to regulations and best execution mandates for all asset classes.
In equities, Transaction Cost Analysis (TCA) is established as a significant contributor towards this goal but now a clear demand has emerged to extend TCA in FX, Fixed Income and Derivatives beyond simple best execution reporting to deliver actionable insight.

Bloomberg engages with thousands of clients across the globe daily, and an increasing theme of discussion is trading analytics and in particular multi-asset TCA. In response to this, we held an industry event in London earlier this year to debate the subject. Traders, compliance officers and investment managers engaged in multi-asset operations were surveyed throughout the event, starting with two key questions:

• ‘In which asset classes do you currently practice TCA?’

A significant 31% of respondents are already conducting some form of multi-asset TCA

• ‘What is the most important reason for conducting post-trade TCA?’

61% cited compliance and best execution as the most important reason, but broker/algo evaluation is a significant requirement along with trader evaluation and client reporting.

What was clear from the response and the discussions on the day was:

• A significant amount of multi-asset TCA is 
already being conducted but there is a clear 
lack of consensus regarding best practice.

• Best execution monitoring remains the primary goal but is coupled with a growing demand for 
a more sophisticated approach that delivers 
decision support in a timely and integrated way.

• Demand is growing for a credible, transparent 
and more robust multi-asset TCA capability.

So what are the kinds of questions clients are trying to answer through implementing a multi- asset TCA strategy? What are the key drivers and the unique challenges to deliver TCA for other asset classes and what can be learned from the equity experience as multi-asset TCA evolves?


Cost de-composition

Understanding the overall cost of implementing
an investment idea is important, but the critical requirement is to de-compose the costs and
look at attribution along the entire order lifecycle. Isolating and measuring the key contributors to performance allows good practice to be identified and reinforced, and corrective steps to be taken where weaknesses are found. For example, comparing a price snapshot at the time a currency exposure is generated or when a portfolio manager decides to invest, with the price at the time the resultant order is created, can give insight to the opportunity cost of time delay. Capturing that discrete moment is particularly tough for FX trading when coupled with the natural netting of currency exposures from the underlying trades. A practical solution to this problem might be to consider using the opening price on arrival day as the trigger 
point for exposures. (In itself an arbitrary point,
but by convention 5pm NY.) The aggregation of these results by account or portfolio manager
can begin to build a picture of momentum bias and identify where natural delays are creating a negative impact. That, in turn, enables traders to anticipate strategy selection better and also guide on aggression levels. Another common use case for our clients would be to compare the snap price from the order arrival until the first route to isolate the opportunity cost attributable to trader timing. The value added by electing when to trade can then be demonstrated.

De-composition can provide even deeper analysis to help in a more subtle way. For example, one can analyse potential ‘leakage’ cost by comparing price at the time an RFQ goes out to the price when quotes are returned. Additionally, one could look at a reversion based on the price at a defined interval – e.g. 1 or 5 minutes after trading, to build a profile of latent impact. When combined with fill attributes, e.g. natural agency
or principal execution, will build a profile of which bonds or currency pairs are more likely to exhibit negative impact and which dealers manage the unwind of their resultant exposures better to protect the market for follow on business. All of these results can deliver insight to optimise the trading and investment process.

Insight for improved investment and trading performance

Armed with a de-composed view of costs, multiple forms of analysis can be aggregated to understand true opportunity and impact costs. By isolating
and grouping specific factors, it is possible to profile trades and contrast positive and negative performance. To illustrate this, a typical approach
is to compare costs according to order ‘difficulty’. In equities, clients will often analyse orders in aggregate using a combination of factors – order size/ADV is common, for example, because trading 1% of a day’s volume is typically easier to execute than 100% or greater. Volatility is measured to determine whether the market is consistent or skittish, and momentum is used to gauge whether general market direction is favourable or adverse to your intent. This is more challenging in OTC- centric markets like FX and Fixed Income due to incomplete volume data. However an approach being adopted by the industry today is to group together orders within ranges of size according to the underlying attributes of an order. For example G10 majors could be grouped within defined levels of trading difficulty based on the value: less than US$3m orders are ‘easy’, US$3m –US$15m are ‘medium’ and >US$15m are ‘difficult’ for example. A different definition could be created for restricted currencies or for different groups of bonds, according to their credit ratings. Building aggregations of ‘similar’ orders allows a team to analyse performance in context.

Let’s say orders for a certain desk are grouped according to the time of day they are traded. A desk might decide to split the trading day in to
one hour ‘buckets’, allowing similar orders to be compared according to the time executed, which can highlight the optimal time of day to trade. Combining both aspects – difficulty level and optimal timing – can provide additional clarity – for example trading spot USD/KRW for “easy” or “medium” orders might not be significantly affected by the time of day but for ‘hard’ orders it may prove better to execute during live hours in Korea. This insight can quantify and prove something that may already be suspected by intuition, and will provide pre-trade decision support the next time a similar order arrives.

Pre-trade decision support

Our clients want to answer questions like “Who
is the best dealer to use for this order in hand? Which is the best strategy, time of day and what order instructions should I give?” Experience
tells most traders who their ‘go to’ guys are for certain types of orders and TCA can reinforce that judgement as illustrated above, but the next step in leveraging TCA is to incorporate this information into the decision making process at the point of consumption. For example, the results of TCA can be used to help identify the most appropriate targets for RFQ. By incorporating those results
into the RFQ ticket a list of target dealers can be delivered based on factors such as hit ratio i.e. how often you trade relative to the amount of RFQ sent, performance when trading by measuring slippage to the prevailing ‘best’ price, and also performance when not trading, or the aggregate slippage of all rejected quotes compared to the taken quote, to gain perspective on how aggressive their quotes are even when they don’t get the trade. When put in the context of the order profile, this provides a complete decision/action cycle that calibrates as subsequent trades are analysed.

Regulation & compliance

Multi-asset operations increasingly need to conduct effective trade surveillance. To do
this, compliance departments must monitor conformance to regulations while simultaneously detecting and investigating outliers as trades are executed. This is true in all asset classes now, and will become more important as MiFIR makes its passage through the EU. In addition, Dodd-Frank and the movement toward more organised trading facilities such as SEFs and increased pre and post-trade transparency make the case even more compelling for implementing TCA for non-equities. For example, testing for possible suspicious trades can be achieved using TCA. There are a variety of possibilities, comparing to a day’s high and low can indicate suspicious trades, or by comparing the achieved average execution price to a close ‘n’ days later and isolating trades that exhibit a positive performance greater than a certain threshold can indicate a possible insider trade. These can then be investigated to determine if any further action is required. To investigate effectively requires market context and content that can be used to recreate market conditions at the time of trading, for example viewing any contemporaneous news items or spikes in quantity or readership of news related to the asset in question at or after the time of trade.

Context and content

Using TCA to provide insights on possible trends can lead to a step change in behaviour and is
an important first step for every firm. In addition, the context in which a result is crystallised is critical to conducting proper trade analysis – that is, the ability to see relative as well as absolute performance. For example a trader or dealer working an order with full discretion should likely be viewed differently to one who has received instructions to ‘get me done NOW’. In addition being able to view performance against relative measures becomes increasingly important. The use of pre-trade cost estimates can provide a ‘budget’ of likely cost against which you can compare achieved results. In addition, using an aggregate of all observed performance within a universe allows relative performance to be measured in the context of peers. Both are elements which are beginning to emerge in a multi-asset context. Wider context means analysing results in light of macro events or news. Knowing that an economic release came out during the order which negatively affected the price would provide shading to the performance. Similarly, news of a credit rating review that emerged subsequent to trading and drove the bond positively in a trader’s favour might indicate the need for further investigation of potentially suspicious trades. Analysing costs in context provides a deeper and more usable result.

What is driving the demand for a more sophisticated multi-asset TCA?

Change is a constant we must all face, however the broad drivers towards a more sophisticated approach to multi-asset TCA could be characterised as regulation, market evolution and competition. From a regulatory standpoint, I’ve mentioned the growing compliance burden with initiatives such as STR in the UK, but there is a
lot of debate on MiFIR right now and on pre/post trade transparency in the USA, which makes future regulation and resultant market structure uncertain. Finding a consensus around organised dealing for bonds, requirements for firm and continuous pricing, etc, are proving to be a challenge. Most parties can agree however, that whatever the changes are to come, they will certainly yield a different market structure from any we’ve seen
in the past. Other market factors, such as the reduction in liquidity in secondary markets, are also contributing to a desire to better understand costs and reduce market impact. Due to commission unbundling, trading is now seen as a profit centre in its own right, and so the ability to demonstrate performance becomes increasingly important. We saw this in equities through MiFID and RegNMS which created fragmentation challenges. However, markets evolve and adapt and in this case the response was an ‘execution services arms race’ with the development of increasingly diverse and sophisticated electronic trading services – from the array of algos through DMA, smart order routing technology and dark pools etc. With this choice in execution and fragmented liquidity, conducting an effective TCA became far more challenging, almost impossible at a basic level without some level of automation. The days of checking against the close or Day VWAP were gone and people started to really get serious about using TCA as a core part of their investment and trading process. Preparing for how these potential changes might impact other asset classes must now be considered. In my opinion, these all drive towards how to better demonstrate competitiveness. Returns are scrutinised, so it is becoming increasingly important for both buy- and sellside
to demonstrate a control of trading costs. For the buyside, the focus is still on ‘stock picking’ in the broad sense, but the marginal gains achieved through efficient trading cost management is now a clear differentiator when courting new clients and trying to retain funds. For the sellside offering new and innovative execution services being able to demonstrate execution quality is key.

So what are the challenges to delivering an effective multi-asset TCA?

• Pricing data:

For exchange-traded instruments such as equities and futures, getting pricing data is simple with a continuous stream of tick data. However, for bonds and currencies, no single view of price and trading data exists because liquidity is fragmented between electronic platforms and single dealers. Even so, data is more available and of higher quality the further up the liquidity profile you go and the previously mentioned initiatives to greater transparency should lead to improvement in data quality in the future.

For example, trading G10 majors on spot or
US Treasury 10 year notes doesn’t represent a challenge as the available data to price against delivers a good proxy for an effective market best bid/offer. However pricing for a broken date in an obscure currency pair or for a distressed corporate bond with esoteric coupon or redemption profile presents a much more significant challenge.
This is the case in equities to an extent as well. Getting pricing for IBM or Vodafone is simple,
but less so for a micro-cap stock in an emerging market. Market participants commonly deduce that the quality of the data will drive the quality of the analysis.

However, it is worth remembering my earlier point about context of costs and that measuring the ‘easy’ in the same way as the ‘difficult’ doesn’t make sense. Whilst pricing data might constrain
a complete analysis, it is still possible to take a pragmatic approach and gain valuable analysis on a good portion of the value traded.

When we consider that transparency
initiatives in the market are likely to improve this situation, it appears there is a good basis for achieving meaningful analysis. For this to work, of course, the equivalent trade data needs to be of equal quality.

• Trade data:

With de-composition and context such a large part of achieving a quality analysis, having accurate and plentiful timestamps that match the events within the order lifecycle is key. This is a significant challenge when trading isn’t electronic, as is the case for a significant proportion of FX and Fixed Income flow.

While the increase in adoption of electronic trading will benefit TCA quality, the lack of adoption does not negate the ability to gather meaningful insight. Benchmark selection is of key importance for interval or implementation shortfall based measures but benchmarking against points in time (such as open/close, reversions, time off-sets etc) can still be effective.

In addition benchmarking to fixings can also
be achieved. A common benchmark in equities, VWAP (Volume-Weighted Average Price) is of less use in OTC markets because of the incomplete volume data previously mentioned. To that end, using another fair value proxy such as TWAP (Time-Weighted Average Price) has seen wider adoption. Implementation would typically be on an all-day basis to overcome the timestamp issue.

Another key aspect of a complete trade
data record is capturing other attributes such
as instructions, limits/stops etc. Being able to capture the softer aspects of the order, including any amendments to them throughout the life is also critical.

Finally we have to consider the specific requirements of conducting TCA on items like swaps, or rolls in futures. Keeping these trades separate is essential to remove the potential for material skew when looking at the single legs. Being able to isolate them enables a more refined analysis to take place, for example by removing the volume attributed to futures rolls from the aggregate participation rate for a contract. From a technology point of view this is a challenge of the OMS/EMS data set and the integration process.

• User experience:

The challenge for multi-asset TCA is to reconcile the need for a common environment and procedures in order to present a normalised view of costs across a firm, whilst at the same time accommodating the differences in methodologies between asset classes. For firms conducting multi-asset business there is little value to having separate TCA solutions for each asset class, as it requires separate skills to use, produces different reports, and most importantly cannot produce the holistic view of performance to provide a single report for clients.

To encompass all trading activity and enable
a normalised view is of utmost importance as
the ultimate benefit of a single platform for multi- asset TCA is that it allows greater insight to be captured. For example by looking at aggregate performance across assets within groupings
like ‘easy’ or ‘difficult’, trading method (e.g. electronic versus RFQ) or when comparing traders, brokers or portfolio managers who have cross asset responsibilities, it is possible to compare performance cross-asset which can promote the sharing of best practice. It is also a challenge to ensure that consumers of TCA can draw useful conclusions from the analysis. You can measure, de-compose, attribute etc, and once you have
the results, a user will invariably ask the question ‘so what?’

Conclusion

Multi-asset TCA is a reality and is already delivering answers and insight to those that participate. The market landscape looks set to allow an increase
in sophistication and thus the value that TCA can deliver should increase. The key is to determine a very specific TCA policy which focuses on the questions you are trying to answer, what entities you want to measure and to determine the correct benchmark or benchmarks required. It takes time to develop history upon which to gain context and trends, so a TCA policy should be dynamic, evolving with the firm or trading desk. Given the status of current thinking, this framework – both the types of analysis and the data used – can be used to answer some of the important questions posed above: ‘when to trade?’, ‘who to trade with?’ and so on. ■

©Best Execution 2013