Pre-Trade Analysis – Why Bother?

“It is not what we choose that is important; it is the reason we choose it.”
Carolyn Myss

Best execution is not simply about measuring transaction costs, and other relevant metrics, after a trade has been executed. Best execution is a process, whereby informed decisions are made throughout a trade’s lifecycle in order to achieve the best possible result for the client. Clearly, a key stage in the trade lifecycle is ‘pre-trade’, which we will explore in more detail in this article.

As we have touched upon in previous articles, the modern foreign exchange market is a complex beast, providing participants with many different methods of execution. For example:

1.       Risk transfer over the phone
2.       Request for Quote (RFQ) on a multi-dealer platform
3.       Request for Stream (RFS) on either multi-dealer or single dealer platforms
4.       Algorithmic execution

Within each of these methods, there are a multitude of factors, and therefore additional decisions, to consider. For example, if you are employing RFQ, how many liquidity providers should you request quotes from and which ones? Or, if you are considering algorithmic execution, how do you select from the extensive range of products now available, and when a specific product is chosen, how should you select the parameters to use? In addition, do you want to access the market directly and have your liquidity provider place orders on your behalf, or do you want to simply execute with a counterparty as principal? If the former, are there specific venues you would like to access? The decision making process can become quite complex, analogous to deciding which chain of coffee shops to pop into on the way to work, deciding upon Starbucks and then having to select from the fatuous list of types of coffee, milk, sizes, temperature and strengths.

In our view, best practice is to not to necessarily exclude any specific execution method, although not to create a Starbucks situation of too much choice which can result in paralysis in decision making! Its ok, I’ll just have a Tetley’s instead. Each method can add value, and be the appropriate choice, for a given trade, with specific trading objectives within a particular set of market conditions. There may be occasions where a large block of risk needs to be executed quickly, and quietly, and in such cases voice risk transfer may be appropriate with the optimal liquidity provider, who can warehouse and manage such inventory. There may be other occasions where the objective is to minimise spread paid, and selecting an appropriate algo may be the optimal solution. Deciding not to have algos on your ‘menu’ of execution methods due to the added complexity and problems in selecting a specific product from a specific provider should be not be a deterrent. Such products can add significant benefits to the best execution process in terms of cost savings.

Analytics, data and technology can help simplify this process, and in particular pre-trade analytics.

Reading through MiFID II, and other initiatives such as the Global Code of Conduct, doesn’t provide a detailed specification of what is expected or required when it comes to pre-trade analysis, at least from a best execution perspective (N.B. we’re not covering here the pre-trade reporting and transparency aspects of MiFID II, we are simply focusing on how pre-trade analysis can help deliver against the definition of best execution). In the absence of anything official, we thought it might be useful to put some thoughts together on what best practice may look like, at least for FX in the first instance.

1.       Coverage

It doesn’t seem to make sense to perform value-added pre-trade analysis on every single trade. Execution desks trade hundreds of FX transactions every day and it is not practically feasible to conduct what-if analysis on every single order. This is where the positive feedback loop from the post-trade process should cover the majority of the smaller, or more liquid, tickets, as discussed in previous articles[1][2]. A periodic assessment of execution performance allows checks to be carried out on whether any further changes need to made to manage and optimise the decisions for the bulk of the flow. Having said that, if it is possible from a technology perspective, it would be valuable to have a pre-trade benchmark, such as the fair value expected cost, calculated for every trade to allow an ex-post comparison.

So, let us focus on value-added pre-trade analysis for now, defined whereby the user performs scenario, or what-if, analysis on a specific trade defines the universe as larger trades, and trades in less liquid currency pairs. Guidelines for defining what constitutes a larger or less liquid trade could be included in an institution’s best execution policy.

2.       Analysis to be performed

Timing of trade

This is obviously only of interest for trades with discretion around timing. Many FX trades are executed without this discretion, e.g. a 4pm WM Fix order or where a Portfolio Manager requires immediate execution to attain a specific current market level. However, if there is discretion, then the impact on cost can be significant. Pre-trade analytics should allow a user to compare costs for different execution times over a given day. For example, on days with relatively low volatility and little price direction it may be beneficial to wait and execute during times of higher liquidity. This issue of market risk is covered later as taking into account potential ‘opportunity cost’ is clearly critical in such decision making.

Sizing of trade

Another common theme that requires analysis is determining the ‘optimal’ size to trade. Again, there may be little discretion here, but if there is flexibility, then scenario analysis can add value given how costs fluctuate by size. The issue can be fundamentally thought of as ‘how quickly can the market digest my risk’. There is often a misconception that the FX market is so deep and liquid that such questions really shouldn’t be a consideration, often citing the BIS survey’s $5 trn of volume traded per day. However, in reality, we often see examples where relatively small tickets can sometimes create significant market impact and footprint. The FX market is generally liquid compared to other asset classes, but it is also fragmented with a lot of liquidity recycled across venues and liquidity providers. One could argue that the issue of declining risk appetite, and hence inventories, at market makers due to the regulatory environment may start to reverse given the changed administration in the US, which may help improve the conditions for executing larger sizes. However, it is clear, that care should be taken when determining the notional sizes to execute, even for liquid pairs. Pre-trade analysis on costs by size, and also information on prior executions of similar sizes to see what has worked well and what hasn’t at different times of the day, can be extremely valuable.

 Execution method

As alluded to in the introduction, there are now many methods of execution available. We have seen a significant increase in the use of algos across both institutional and corporate clients, which in itself creates the problem of product selection. Such products can provide benefits in the form of cost savings, when viewed on an overall performance basis net of fees. However, there are risks, such as the obvious one that the market simply moves against you whilst the algo is working. This market risk is part and parcel of working any order, so some form of quantification of the possible cost of this is useful in a pre-trade environment to allow an informed decision to be made. Risk transfer may be preferable if the market conditions are unfavourable for working your order via an algo. Having the market move away from you may be simply down to bad luck and the random walk of the FX market, but not always. If your order is being worked in a way that is generating signalling risk[3] then there may be market participants trading ahead of your order, resulting in less favourable execution.  This may happen for many reasons, including through poor product design, simplistic smart order routing, inappropriate sizing, incorrect product selection for the time of day and currency pair. Having metrics available in a pre-trade environment that, for example, quantify market footprint and signalling risk for similar trades in the past can help in the selection of execution method and product to mitigate such risks.

Defining duration

 A common question when deciding to trade over a period of time is “how long”? Especially, if the trade does not have a specific objective of tracking a particular benchmark. For example, when trading an algo over the WMR fixing window, with the specific objective of minimising tracking error to the Fix, then the duration should match the window. Or, if a passive equity portfolio is rebalancing and the objective is to achieve as close to an average rate over the same window of time that the equity exchanges are open, then the duration of the FX trade should match. However, if there is discretion over setting the duration, then pre-trade analysis can add value as there are conflicting forces at play. If you trade too quickly, you may create unsatisfactory market impact whilst minimising the time that the market has to potentially to move against you, defined as opportunity cost. Equally, if you trade too slowly, then you may minimise market impact but you may run significant market risk, especially in a high volatility environment, potentially resulting in adverse opportunity cost. Figure 1 below illustrates the conflict.

Netting

Net or not to net, that is the question. Unfortunately, not an easy question to answer. There is no simple yes or no, it really does depend on a number of factors, including available liquidity and therefore spread cost, together with prevailing market volatility. As above, there are once again competing forces at play. If liquidity is good, and volatility is relatively high, then it may make sense not to wait too long for offsetting orders to benefit from netting as the opportunity cost from waiting could more than outweigh the potential cost savings from crossing spreads less frequently. If, however, volatility is relatively low, and liquidity is poor, then it may make sense to wait to net orders as in this scenario the opportunity cost may be less than the spread savings. This gross simplification is portrayed graphically in Figure 2 below.

So, in essence, the answer is, ‘it depends’. It would therefore be valuable to have some form of netting analysis incorporated within the pre-trade stage of the process to help evaluate this on a case by case basis.

3.       Results storage

So, you’ve done all the analysis and executed the trade. Now what? In our view, best practice should be that such analysis is saved and stored for the specific trade. When you go back into your post-trade analysis, how valuable would it be to have the trades tagged with the associated pre-trade analysis you performed? This then allows a comparison of performance on a post-trade basis with the pre-trade analysis, e.g. did choosing that particular algo perform as expected? This feedback loop is valuable as it allows the decisions to be assessed and then adjusted in the future to improve the result even further. Spending the time to perform pre-trade analysis is not about ‘ticking a box’, it should be time well spent to help add additional value to the execution process.

Conclusion

Pre-trade is a core component of the best execution process. The increasing focus on best execution from a regulatory perspective has propelled pre-trade into a more mandatory status, rather than a ‘nice to have if we have the time’, although one could argue it was never a ‘nice to have’ given the value it can bring to execution result for the client. However, everyone is busy, very busy, all of the time, so incorporating pre-trade in a more systematic fashion requires technology to automate as much as possible. Trades should be prioritised such that only those where significant value can be added are focused on. And you should learn from past performance. Not necessarily in a machine-learning perspective, but simply have at your fingertips previous experience summarised in a form that allows quick, informed decisions to be made. Improving execution systematically requires the use of ‘smart data’, not just ‘big data’.

[1] “Feedback loops and marginal gains – using TCA to save costs and improve returns”, Pete Eggleston, BestX, Oct 16

[2] “Applying the Pareto Principle to Best Execution”, Pete Eggleston, BestX, Feb 17

[3] Signalling Risk – is it a concern in FX markets?, Pete Eggleston, BestX, July 2016