The movie The Theory of Everything, which recently won a leading actor Oscar, 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 these latest tools may be thought by some to be able to answer 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 higher impact costs 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.