EXECUTION ANALYSIS IN THE WORLD OF TCA.
Ian Domowitz, ITG Inc.
What is the difference between execution analysis and TCA?
So, what changes based on the use of execution-level data as opposed to just orders?
…if the lens through which you see data evolves with the data themselves.
|Data are neutral. Consequences of changes in data can be enormous. In that spirit, I will try to sort out what this Q&A means.|
Enhanced granularity of trading data delivers transparency. Dashboards are popular for algorithms and routing. Post-trade analysis follows suit. Whereas order-level TCA contains average order sizes, durations, cost, market conditions, and so forth, this information now is available at the level of individual algorithms. New metrics appear, such as price reversion and fill size by strategy.
There is more. Distributions of fills by market cap, by start time, by number of placements… need we go on? Absolutely… distributions of algo usage by market conditions, fill sizes, duration, and number of venues. There is a blizzard of data once granularity has reached the level of individual fills.
But this is all just TCA, or more aptly, transaction cost reporting. It is “observational analysis” conducted with different data. In contrast, the trend in order-level TCA has been to navigate a smaller blizzard to isolate actionable information. In execution level analysis, we almost achieve a count of distributions of outcomes equal to the number of data points in order-level TCA. That exercise has left granular TCA unchanged from its aggregate cousin: the setting of performance results in the context of market conditions and trader activity.
The most popular report requested in order-level TCA is an outlier report. It may be as simple as the five best and worst outcomes. It may be as complicated as the breach of user-defined bounds in particular market conditions.
The most popular report requested in execution analysis is also an outlier report. We have more possibilities for the definition of “outlier.” Examples include prices at the millisecond level and reversion statistics.
The aggregate and granular concepts are the same. Beyond extra metrics, there is no change in moving from order-level to fill-level analysis. There are some pitfalls, however.
Granularity and noise
In my experience, every outlier has a story, which could not be captured in the assessment of the trade. Studies of trader physiological and psychological behaviour suggest that these stories be interpreted with caution. After the fact, trader opinions may be disconnected from market conditions and uncertainty.1 There is physiological truth in the axiom, ‘you are what you trade, not what you say’.
One person’s stories are another’s noise. While granular data may amplify resolution, like pixels on a screen, they increase uncertainty. Peering closely at a pixelated face delivers more about the nose than desired, while missing the shape of the chin completely.
How many outliers ought one to expect, viewing them as noise? A pragmatic standard is five percent of the data. That’s 50 outliers for 100 orders resulting in 1000 executions. The trick will be to establish methods which find patterns in that many outliers. Such methods could turn a poor data situation into something more useful.
Best price and best execution
There is no commonly accepted definition of best execution. This uncertainty has driven some market participants to think about best execution as best price. Execution analysis has a tendency to promote this belief, perhaps because prices are the only things truly moving at millisecond intervals.
Best price at the fill level is not best execution. Best execution entails the best outcome under the circumstances surrounding the implementation of an investment decision, i.e., surrounding the order. Consider the following example.
If one followed a passive participation strategy, getting the best price at every fill, this order would cost 75 basis points (bps). The strategy used resulted in a cost of 60 bps.
There are no obvious outliers in the implementation of this investment decision. We can, nevertheless, do better based on execution analysis, albeit with the right lens.
Strategies as outliers
The lens through which execution analysis differentiates itself must focus on trading strategy. Granularity of data then adds value, and strategy is the core contributor to the final result. Strategy analysis is actionable. Conditional on the investment decision, best strategy choice leads to best execution.
Strategy is the target of a lens which evolves with the data. Continuing the example of Figure 1, any price path can be differentiated through trading strategies.
A more aggressive strategy drives price, incurring three times the trader’s price impact of the realised strategy. Nevertheless, the effect of momentum in the broader market is minimised, and competing orders are circumvented. Understanding the role of strategy and making the right choice in this context drops the cost of the order by over a third, to 40 bps. The circumstances behind the choice become the focus.
The spread between a bad strategy (passive participation here) and a good one is 35 bps, money worth fighting for. Strategies may be decomposed into effects due to market momentum, competing orders, and the trader’s own activity. These factors may be analysed in terms of market conditions and relative liquidity. Guidelines can be set and hypotheses tested.
The question of outliers can be settled in an entirely different fashion and without a blizzard of numbers. The trading strategy itself is the object of analysis. In the case considered here, passive participation is an outlier. In this example, it is obvious as to why. But generally, it is the “why” which elevates observational reporting to analysis.
Execution analysis sometimes goes under the rubric of execution consulting. In that guise, it has been an educational tool for brokers, applied to their own algorithms. The discussion above is relevant in a broker-neutral context. Are algorithms truly commoditised, and differentiated only by trader implementation? What are the usage patterns? How do strategy types react to market conditions?2
Venue analysis is a 25-year old theme, revived as part of execution analysis. A venue report is part of any execution study, but care must be taken. Consideration of trading strategy is an essential component in assessing venue performance. One cannot contrast two market structures, or assess individual venue quality, without controlling for the strategy used.3
Order-level TCA feeds applications such as portfolio optimisation, fund capacity analysis, liquidation studies, and fund NAV determination. Execution analysis exposes the core issue in day-to-day operations, trading strategy, and paves the way for real-time decision support. In the end, it is the suite of applications which will distinguish execution analysis from order-level TCA, along with questions which evolve with the data themselves.
- See, for example, John Coates, The Hour Between Dog and Wolf: How Risk Taking Transforms Us, Body and Mind, Random House, 2012.
- For example, Algorithmic Trading Usage Patterns and Their Costs, Ian Domowitz and Henry Yegerman, Journal of Trading, Summer 2011.
- Garbage In, Garbage Out: An Optical Tour of the Role of Strategy in Venue Analysis, Ian Domowitz, Kristi Reitnauer and Colleen Ruane, Journal of Trading, Fall 2015.