A sophisticated FX platform makes life easier for clients by taking over the burden of data collection and analytics production. And it can do so for a lot less because of development and maintenance costs being shared across the entire user base, rather than financed by a single, independent provider. But data alone isn’t the answer unless clients have the capability to distil and decipher it for their own benefit.
Although FinTech firms are popping up with regularity to offer some part of the solution, the few that can provide a combination of both data and analysis look set to be victorious. Instead of just big data, the key differentiator to controlling execution costs and preserving Alpha is being able to apply attribution to that data. It’s not just about what you spent, but about knowing where you spent it.
Transaction Cost Analysis becomes every cost analysis
So, what has changed? The early demand from buy-side clients focused on cost control, with the emergence of Transaction Cost Analysis (TCA) providing a benchmark for trading costs that primarily ticked the regulatory due diligence box.
However, what that data provided was a snapshot of a specific time at the point of execution, which only really had information on liquidity and spread. Yet there are many more factors that go into the total cost of execution. Alongside spread, there is skew, slippage, time to market, and speed of execution, all of which have a bearing on the potential loss of Alpha.
What the most sophisticated platforms can now offer clients is not just TCA, but attribution to determine where those costs were incurred. For example, by waiting until later in the day, a client may execute in a market that has better liquidity, allowing the providers to show a tighter spread. But how much Alpha was potentially destroyed by the delay between the time of receiving the order and actually executing it? The ability to have that deeper layer of analysis, and the insight such granularity provides, creates an exponential increase in the value of that data.
Beyond that, composite historical data from streaming prices can also help clients decide how to execute particular trades. Should a portfolio manager choose to exit Japan in favour of Australia, the FX trader can study data on spreads and slippage to determine whether the best course of action is simply to sell JPY versus AUD in a single trade, or to break the trade into its components and deal over the USD, potentially getting greater competition on pricing, better liquidity, and tighter spreads. More sophisticated hedge funds may take it a step further and choose to analyse the merits of delaying one side of the trade in order to create Alpha—all from the availability of attributable data to support the decision.
Liquidity providers can benchmark their own performance versus the market
Data on the actual liquidity provided can also give clients another valuable tool to improve overall execution costs. Analytics will reveal their strengths and weaknesses and isolate where they can be most effective. The data will uncover what type of market conditions are best suited to their trading needs; in which currency pair they are most competent, and whether their access to liquidity allows for efficient exit of trades.
Buy-side clients may also choose to share their own data to improve their relationship with a liquidity provider. Without needing to reveal other counterparties, a client can show collated data to Bank A as to where and why it missed out on business to Bank B or C, either because of less liquidity, wider spreads, skewed prices, slippage, or any combination thereof. By helping to identify where a liquidity provider needs to improve its service, the client stands to benefit from better execution on future trades.
Of course, the liquidity providers also have access to their own data that they can analyse. They can compare their individual pricing against the broader market to evaluate their own performance versus the market at a given time. The smarter platforms even have their own composite pricing data, so that either the client or the liquidity provider can benchmark their performance against an independent source, not just at the time of execution, but at any point on any given day.
Over time, banks can also create their own benchmark level of performance and then compare current data against historical figures to see whether standards are being maintained. Longer-term data can also reveal patterns and trends that may show changing market dynamics a liquidity provider would need to be aware of and react to accordingly.
Streaming NDFs boost liquidity and aid automation
A lack of liquidity, and therefore pricing transparency, has long been a complaint of the buy-side when using Non-Deliverable Forwards (NDFs), making them one of the most expensive FX trades, not only in nominal terms of the cost of transactions, but also in potential loss of Alpha stemming from poor execution.
Even today, hardly any platforms can provide a decent body of reliable data—other than a basic screenshot of the liquidity at the time of execution—from which to derive an accurate Transaction Cost Analysis.
Those few that have developed streaming NDF pricing have a distinct advantage in being able to offer much more than just a tighter spread due to better liquidity. Reducing implementation shortfall, or slippage, is another key consideration for the overall cost of a transaction.
The ability to collect and analyse data from streamed pricing provides the immediate benefit of being able to determine the best method of execution—from how much it will cost to trade a specific amount at a given point in the day, to which liquidity provider is offering the best pricing at that time. These pre-trade decisions help facilitate greater automation that, in turn, reduces operational risk, which can have a significant impact on the potential to achieve provable best execution.
However, a pricing platform also needs to be supported by a robust Execution Management System (EMS) to process a more complex NDF trade than a simple spot transaction. That includes features such as staging incoming orders pre-trade and straight through processing (STP) after the trade.
NDF trading currently only accounts for around 4% of the total FX trading, making it a marginal activity for many providers. However, with Emerging Market economies gradually, but steadily, increasing their share of global GDP against Developed Markets, that percentage is only going to rise, with the G20 already becoming a more important body than the more tight-knit G7.
With the FX market boasting turnover of $8.7 trillion a day, based on the latest Bank of International Settlements (BIS) survey, even a trajectory to just 10% of that total would be a substantial increase in volume. Future entrants to streaming NDF pricing and data may discover that late is too late, and their more nimble and sophisticated competitors have already captured the lion’s share of the market, pushing the barrier to entry ever higher for the newcomers.
Data analysis will be able to highlight the effectiveness of policy decisions
In addition, while the latecomers are struggling to catch up, the early movers are not exactly standing still. The best platforms are already developing software to be able to aggregate anonymous data to improve a client’s ability to gain valuable insight and analysis.
By adding the ability for a client to annotate their data, they can effectively pinpoint a moment in time when a specific change was made. From there, the post-change data can be compared with the pre-change historical data to see whether they achieved the desired results. Much in the same way a digital marketing campaign can be tweaked to reflect various responses, clients will have an almost real-time conclusion about the effectiveness of their decisions.
Another advance that’s just around the corner is the introduction of a so-called Verdict system. Clients soon will be able to place a set of parameters on their trades to ensure every transaction meets their internal due diligence criteria. Clients will be able to determine their own priority of factors such as spread and speed of execution to adhere to their internal cost controls and receive a warning when one or more aspect is outside of those boundaries. Ascertaining this information before executing the trade is far more valuable than a post-mortem on where things went wrong.
This system has the additional benefit of potentially improving communication between clients and liquidity providers. Armed with this data, a client can inform its liquidity provider what aspect of the transaction caused the client not to trade, whether that was because pricing was too slow, the spread was too wide, or for some other market-related reason.
Sitting in the middle is the best of both worlds
The use of data has already become an essential part of FX trading and its importance will only grow with time. While many FinTech companies offer data as a service, it is very much on the Costco model of “more is better,” yet still only a restricted cross-section from the time of execution. In addition, many providers are more reliant on the sell-side that provides the liquidity, rather than the buy-side that needs it, so much of their data goes back to the people that provided it.
A sophisticated multi-dealer FX platform is in the unique position of sitting in the middle, able to collate data from both sides, with the sum of the parts being greater than the whole. Clients have already realized that analysing this data can save them money. The next step will be when they embrace the trading algorithms that run off this data and start using it to identify and seize opportunities to generate Alpha as well.