The modern foreign exchange market is a complex beast, providing participants with many different methods of execution. Within each execution method, there are a multitude of factors, and therefore additional decisions, to consider. For example, if you are employing a request for quote (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 clearly become quite complex pretty quickly.
High vs low touch
Before getting into details, it is worth noting that with the execution desks trading hundreds of FX transactions every day, it is not practically feasible or necessary to perform value-added pre-trade analysis on each individual trade. Instead, the positive feedback loop from the post-trade process should cover the majority of the smaller, or more liquid, tickets12. A periodic assessment of execution performance allows checks to be carried out on whether any further changes need to be made to manage and optimise the decisions for the bulk of the flow.
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 defined as the universe of larger trades, and trades in less liquid currency pairs. Guidelines for defining what constitutes a larger or less liquid trade, and what is therefore categorised as high touch3, could be included in an institution’s best execution policy.
It is not practically feasible or necessary to perform value-added pre-trade analysis on each individual trade
Types of analytics
The type of analytics that can assist in the pre-trade process generally fall into the following categories:
- Model driven
- Empirical, i.e. based on historical observed trade performance
With regards to the empirical category, there are a number of key requirements before deploying pre-trade analytics to help with algo selection:
- It is essential that any analysis is done using a level playing field, where all of the metrics are computed using exactly the same market data and methodologies
- The trade data sample needs to be large enough to be statistically significant
- Focusing on the specific performance metric that is relevant to the trading objective in question is critical
- The data set needs to be consistent and clean e.g. ability to filter out algo trades where applying a limit price has potentially ‘polluted’ performance
- The data set needs to be specific and relevant to the trade to be analysed e.g. filtered on currency pair, notional size, execution time
Where can pre-trade analytics add value?
There are a number of decisions where analytical support can be valuable, as summarised below:
Timing of trade
This is only of interest for trades with discretion around timing. Many FX trades are executed without this discretion, for example a 4 p.m. WM/Reuters 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, if your objective is to earn spread, and are therefore planning to use a very passive algo strategy, then on days with relatively low volatility and little price direction, it may be beneficial to wait and execute during times of higher liquidity.
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 the 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 Bank for International Settlements (BIS) survey. 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. 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.
Linked to the sizing of the trade, another common question when deciding to trade over a period of time is for 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 WM/Reuters fixing window, with the specific objective of minimising tracking error to the Fix, then the duration should match the window. 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.
There are hundreds of FX algo products now available. It is obviously important to select the right tool for the job, so there needs to be complete clarity on what you are trying to achieve with the algo at the outset. For example, is your trading objective simply to be passive and earn as much spread as possible, regardless of market moves, or do you want to minimise slippage to a specific price level such as arrival price? Once the objective is understood, it will provide clarity on the style of algo to choose4 .
Within this style category, analytics can then help measure and compare historical performance for the relevant trade parameters and, most importantly, for the performance metric that is appropriate for the trading objective. If you have access to a representative data set, that meets the requirements laid out earlier in the article, then this can provide a rigorous approach for selection rather than utilising a more random approach.
Going further, more advanced analytics can also help with the setting of other algo parameters, for example, how passive should you set the algo given the prevailing or forecast liquidity and volatility conditions.
Pre-trade analytics should allow a user to compare costs for different execution times over a given day
Turning to data
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 the execution result for the client.
However, trading desks are increasingly asked to do more with less, and hence everyone is very busy, 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.
4. “FX Algos – A Proposed Taxonomy” – BestX, Aug 2018