The current Oscar-nominated movie The Theory of Everything has its lead character Stephen Hawking laying out his vision of a single equation that explains all physical aspects of the universe. The scientist explains in lay terms the two broad areas of theoretical physics that have emerged over the last century – general relativity (as famously developed by Einstein) and quantum field theory (analysing the properties and effects of sub-atomic particles) – and the challenges of integrating both approaches in one over-arching set of theories. One approach looks at very broad aspects of the universe and space and time, while the other focuses on infinitesimally small objects as the basis for broader theories and interpretation.
In a way this rarefied scientific debate has echoes in the more prosaic world of Transaction Cost Analysis (TCA) in financial markets, where the availability of more granular data coupled with pressure from regulators has combined to drive a whole new wave of research and analysis. Typically the analysis of trading costs has focused on the big picture, identifying the implicit costs incurred in the investment process. But now a much more granular level of analysis is also both possible and required. There is a risk that for some these latest tools may be thought of as answering all the questions on trading costs and best execution. This is clearly not the case, and a combination of methods of analysis is vital.
Traditionally TCA was conducted at a relatively high level, focusing on the outcome of orders and looking at the implicit costs incurred by price movements caused by market impact or by delays in the execution process (as distinct from explicit costs such as commissions). This “implementation shortfall” can be calculated and analysed to determine where and when inefficiencies occur in the investment process. Fine tuning can lead to significantly improved investment performance within the context of an underlying process. Most leading institutions continually monitor their TCA data for trends and aim to identify opportunities to make improvements. If left unaddressed, such hidden costs of trading can and do have a major impact on investment returns and rankings in the performance tables.
‘Investment DNA’ Should Be Reflected In TCA Methodology
Every institution has an investment process, which forms a sort of investment DNA for everything it does. It is reflected in activities such as portfolio construction, stock selection, decision timing and trading strategies. Some firms are value-oriented and incur relatively low transaction costs, as they are typically trading against the consensus. Others are more event-driven and momentum-oriented; inherently they need to trade more quickly than others, incurring excess impact in order to capture as much alpha as possible before others do so. Similarly some portfolios are made up of many small positions which can be easily and cheaply traded, while others consist of fewer positions which may be highly illiquid, and cannot be readily and quickly traded without severe loss of value.
All of this should be reflected in the approach to TCA which a firm employs, and the metrics which are used to monitor efficiency in achieving optimal outcomes. There is no one-size-fits-all in this respect. There have been calls in some quarters for a standardised approach to TCA. Such thinking should be firmly resisted, given the wide range of needs and types of analysis. The high level analysis must take into account many aspects of the underlying process, since the costs will be highly linked to factors beyond the control of the trader.
Greater Market Complexity Requires Forensic Analysis
But then a whole new level of complexity was introduced to European financial markets. This reflected a number of developments over the last decade, starting with the fragmentation of trading that resulted from the first MiFID set of regulations in 2007. This led to several new trading venues emerging in Europe, reducing the market share of the traditional exchanges and making the trading landscape considerably more complex. At the same time new generations of trading systems allowed asset managers to record and analyse details of every single fill that is generated by their orders. With algorithms often slicing a large order up into very small pieces, this can literally mean thousands of separate executions for just one order. The final element of complexity – albeit a welcome response to the need for better information – has been the increased use of data tags to track and report information on these individual fills.
Together these factors have driven a rapid evolution in analytical approaches which have recently taken on added urgency as a result of the publication of the FCA’s Thematic Review on Best Execution and the final draft of the proposed MiFID II regulations. These stipulate that investors must not just monitor the venues on which their trades are being executed, but require them to describe the steps they undertake in the choice of those venues and their execution strategies to achieve best execution. While more traditional approaches to TCA tended to look at the context of the investment process and at high level trading data, the new requirements entail much more precision and forensic analysis of the tactics used at the most granular levels in terms of sizes, timing and venues of trades.
Analysis of Venues Must Be Linked to Execution Strategies
The linking of venues to execution strategies is more than coincidental, and indeed is crucial. The way in which an algo is designed to route an order is inextricably linked to the execution strategy selected. This may for instance be a fixed participation strategy, or liquidity-seeking, or aimed at trading only in the so-called dark pools or crossing networks. Each strategy will tend to execute in different venues, or in different sequences, or in different volumes at different times. Hence it is essential for the latest applications of TCA to link the analysis of venues to that of execution strategies as it drills down into these details.
With this new granularity of data, new metrics also come into play. Looking at simple average price or implementation shortfall calculations is not necessarily as relevant in determining the efficiency of one venue over another. Shorter term statistics on reversion or spread capture may be more revealing. Similarly the number and sequence of venues used can be analysed (basically the more venues used, the higher the overall cost), as can the costs or benefits of trading in lit or dark venues (with dark in general achieving better outcomes, particularly in larger sized trades).
Traders now regularly use such data to monitor the ways in which their brokers execute their orders, for instance in the differing patterns of behaviour of smart order routers or algo strategies. And using this data it is also possible to predict what is likely to be the most efficient way to execute a given type of order. As with more traditional approaches to TCA, the post-trade data can become a vital input to pre-trade decision making.
But there is a risk. If one focuses exclusively on these equivalents of subatomic particles, one misses out completely on the bigger picture. It becomes impossible to put the pieces of the broken egg back together in the analysis. Fill data and “child orders” can and should be monitored, but the parent orders of which they are part must also be part of the analysis. The need for TCA at the level of investment process has never been greater, and when coupled with the execution-level analysis it can be extremely powerful.
Standardised Definitions Rather Than Theory if Everything
Hence with these new and emerging analytical techniques, there is a need for a different understanding of TCA – even if it falls short of a single “Theory of Everything”. The two distinct levels of analysis need better description and definition, to avoid confusion or disingenuity in the ways that such analysis is described and used. The need for transparency has never been greater, given the heightened awareness by investors of trading costs. There has been some very good work done in development of standard definitions for use in TCA, not least by the FIX Trading Community . Electronic messages are passed between broker and trader confirming each execution, with the use of industry-standard FIX tags identifying such information as the venue on which the execution took place, or whether the broker acted as agent or counter-party to the trade. The FIX working group has developed a set of best practices and definitions for the key elements of TCA.
These definitions are essential to avoid ambiguity or misunderstandings. But to try to reduce TCA down to the equivalent of a single unifying equation would be simply impossible, and would miss the point of the analysis. The needs of traders, portfolio managers, CIOs, clients, compliance officers and regulators all differ enormously, as do the needs of different organisations. They are made up of the same building blocks, but the metrics most relevant for one functional group or organisation are very different from another. To focus only the big picture is no longer sufficient, but to exclusively look at the minutiae is equally misguided.
Heisenberg Effect Demonstrates Potential Benefit of TCA
Finally there is one other scientific principle which can and indeed has been successfully applied from the world of theoretical physics to TCA. The so-called Heisenberg Effect (named after scientist Werner Heisenberg, as opposed to the more recent TV incarnation) states that the act of observation changes the observed object or activity. There is much evidence to show that by monitoring TCA data the players involved do indeed change their behaviour and the results subsequently observed reflect these changes. We may not be likely to achieve a Theory of Everything any time soon in the world of TCA, but improving trading costs is perhaps a more modest and achievable goal.
Michael SparkesContributors Director, ITG Analytics, Europe
Michael Sparkes is a Director in ITG’s London office, responsible for business development of Analytical Products in Europe.
He has more than 25 years experience in the investment industry, specializing in international portfolio management, product development and transaction cost analysis.