Futures transaction cost analysis: Evolving to meet new challenges

This article was written by Mike Googe, Product Manager & Global Head of Transaction Cost Analysis at Bloomberg. 

Futures volumes are at historic high levels driven partially by the growth in passive investment strategies like ETFs. Traders use futures to balance cash exposure whilst trading individual lines of a basket to manage that exposure. With high liquidity and cost effectiveness, futures are attractive for this purpose. This growth has prompted greater scrutiny of execution costs as investors seek evidence that all is being done to protect returns.

Futures trading is more immediate than equities or bonds and analytics have typically focused on execution strategy. Calculating costs on the basis of an arrival price or VWAP formed over the interval of the execution has been a basic requirement but more sophisticated analytic needs are emerging. So how is Transaction Cost Analysis (TCA) in futures adapting to support this demand?

Benchmark sophistication

Listed markets have the luxury of complete sets of price and volume data for benchmark calculation. When looking to compare and assess execution strategies an emerging theme has been to focus on the performance of each individual fill. This analysis compares every fill to benchmarks that answer two questions important to traders:

• Did we capture any spread? (i.e. did we execute lower than the offer when buying or vice versa)
• Did the level of aggression prompt adverse price movement leading to increased market impact?

These measures aggregated to the order can provide key indicators of execution quality and inform future trading strategy selection.

Volatility is key in derivative pricing and influences the bid/ask spread. Whilst typical arrival price methodology looks at the mid of the bid/ask spread at the given time stamp, there is increased appetite for looking at a far touch benchmark (which looks at the offer side where buying and vice versa for selling). Similar in principle to the spread capture, this is emerging as a benchmark of choice when looking at the order or route level to capture the impact of the prevailing bid/ask spread at the time and not handicap the analysis with half the spread.

One benchmark proving popular especially in light of passive strategies is the Market On Cash Close (MOCC). Not an official order type, this measure is popular because it snaps the price of the future at the time that the underlying securities cease trading. This allows traders to bifurcate volume and price when underlying markets are in operation from when they are not.

Relative comparison

These represent absolute benchmarks.  Whilst traders instinctively assess good and poor outcomes the pressure from external stakeholders to ‘prove it’ has prompted the need for a relative benchmark to provide a frame of reference for comparison.

The first to emerge are peer benchmarks. These measures have been successful in equities analysis for years and gaining significant traction in Fixed Income and FX markets but new for Futures. Peer benchmarks are community sourced based on aggregating results from the community but should not be thought of as comparison of participants. It is a comparison of flow profiles based on order and market condition characteristics. The approach is for observations to be compared at a meaningful level of aggregation to provide value; not so abstract as to be of little value but not so detailed as to introduce scarcity to the data at best and potential leakage at worst. Sensible approaches to minimum contributors and number of observations will typically alleviate these issues. Another challenge for a futures peer calculation is their transient nature. Any given contract exists for only a limited period, typically 3 months, so how to make a meaningful comparison with such a short time frame? The answer is again in the aggregations used. Using an individual contract would be precise but be limited in data and only passing value, so focusing on the contract and not the specific series delivers the benefit of protracted data.

Rolls and roll periods extend the challenge. Their contingent nature creates imbalance of supply and demand as the roll date approaches which can skew results. Using appropriate aggregations again helps here to isolate rolls from single leg and aggregating peer numbers to a quarter would typically smooth out this effect.

Gaining insight

The next step is to organize the data to gain maximum value. Typically participants seek two outcomes. Efficient outlier detection and insights for decision support. Increasing volumes demand automated outlier detection to identify trades failing execution quality thresholds. These thresholds differ depending on a variety of factors, order difficulty, market momentum etc. However the most popular question that arises is ‘where to set thresholds?’ Experience and judgement guide this but having an independent frame of reference to guide decisions is critical and again peer data can help. For selected combinations of groupings / benchmarks, suggested threshold values based on positive and negative percentiles of any distribution curve formed by these combinations can identify any order falling into the top or bottom ‘n’ percent of similar flow amongst the peer community.

This flow profiling approach also provides decision support insights. Capturing and clustering results according to order related factors (e.g. Size/ADV, order type, etc.) and market conditions (e.g. volatility or momentum, etc.) allows fair comparison of performance and illustrates conditions and characteristics that influence trading performance. Increasing automation using tools like RBLD, incorporate these insights and used with peer benchmarks, help traders take the guess work out of less complicated flow allowing more time to focus on more impactful orders. Finally there is growing interest to consider order creation times and trader routing times to seek insights about timing and delays impacts and considering all traded elements (including FX exposures trades) for a holistic trading cost analysis.

Conclusion

The growth in futures trading is driving the evolution of matching analytics. Competition is driving the adoption of new measurements and approaches. Far from simply extending existing TCA approaches the futures markets have clear challenges to overcome to capture the nuances of this asset class. However the underlying data affords participants a real opportunity to develop high quality analysis which can only lead to better outcomes.