Measuring the impact of the Turn in FX markets

“Turn and face the strange”
David Bowie

In our latest paper we continue our research into measuring the impact of the ‘turn’ within the FX markets. This somewhat strange phenomenon, which manifests itself around key dates throughout the year, is generally caused by supply and demand for funding by large financial institutions, which can create dislocations in the forward curves for certain currencies. In this latest research we empirically measure the impact of the turn around a range of different dates, including year, quarter and month end, but also event days such as NFP, FOMC etc. In summary, we find that the impact is most significant for year and quarter ends, with, for example, an average magnitude at year-end of between 0.6 and 1.5 bps for EURUSD depending on the tenor of the transaction. The work has helped us prioritise where to adjust the forward curve interpolation to better estimate mid for broken dates. To receive a copy of the paper please email us at

April 30th – RTS 28 Deadline Day

“I love deadlines. I like the whooshing sound they make as they fly by”
Douglas Adams

MiFID II is here. From all accounts, January 3rd 2018 was similar to Y2K[1] day, in that everyone woke up, went to work and, generally speaking, everything carried on as usual. It is clear that the ‘soft’ launch of MiFID II has resulted in no discernible disruption from a liquidity or execution perspective, but there are a number of looming elephants in the room that were postponed e.g. the additional 6-month grace period for assignment of LEI codes. So, those that were waiting for January 3rd to come and go in the hope that they could leave MiFID II behind them, and get on with their day jobs, are going to be disappointed. 2018, and probably beyond, will continue to have a significant MiFID II focus as much remains to be done.

One of the next key dates in the implementation timetable is April 30th, by which time, institutions will need to have submitted their RTS 28 reports. RTS 28 encompasses many aspects of the best execution obligations for an institution, and represents a large data gathering, cleansing and reporting exercise. That is burdensome enough, but it is further complicated by ambiguity in what exactly needs to be reported, especially for an OTC market such as FX.

If we look at the RTS 28 Top 5 report alone, which is the only RTS 28 report where the legislation provides a specific template, then ambiguity exists even here, and can be summarised in the following areas:

a)       Venue vs Channel vs Counterparty

For the FX market, with a hybrid market structure of both quote- and order-driven activity, there is confusion over the definition of these terms. If you are executing an RFQ order, over a multi-dealer platform (e.g. FXall or FX Connect), with a panel of 5 liquidity providers then you could define the multi-dealer platform as the Channel, and the winning liquidity provider as the Counterparty. So, in this example, there is no Venue? But what if the multi-dealer platform is an MTF? Clearly, even in the simplistic case of an RFQ trade there is scope for confusion.

In the case of an algo trade, that has been initiated via a multi-dealer platform, with a bank, then additional complications arise. The bank’s smart order router will be directing the algo child fills across multiple venues, so in this case Channel, Counterparty and Venue for each child slice, at least, of the algo would appear clear. However, if the algo was spot and not linked to an underlying securities transaction, i.e. does not fall into the ‘Associated Spot’ category for MiFID II reporting purposes, then technically speaking this trade should not be included in RTS 28 reporting. But, once the algo had completed, what if forward points were then applied to the algo spot rate to roll the trade forward? The parent trade is no longer spot, and does now fall within MiFID II reporting requirements.

b)      Passive vs Aggressive

Again, for the hybrid world of FX where there is still a very large proportion of quote-driven business, how should the definition of passive or aggressive be applied? Reading the regulatory text would indicate that any trade which has paid bid-offer spread is technically an aggressive trade, whereas ‘earning’ spread would constitute a passive fill. There are conflicting views in this across the industry. For many of our clients, these fields are generally ignored for FX if they do not execute any of their business via orders or algos, or have direct market access. For orders and algos, however, data is provided by the majority of liquidity providers on whether the order was filled passively or not. This is not yet consistently available across the industry yet, or provided in a consistent format, but is becoming increasingly prevalent.

c)       Directed

For many mandates, FX transactions are ‘directed’ to a specific counterparty under the terms of the IMA. Such transactions should be split out and identified in the Top 5 report. However, many asset managers net transactions across portfolios, the net execution result of which is then allocated back across the individual accounts within the block. This can potentially result in complications whereby trades for non-directed accounts can be included in a directed block, as there was a benefit from a netting perspective, so the parent block can no longer simply be included in Top 5 directed field. This would need to be done at the level below, i.e. individual allocations or child trades, so the concept of multi-tier trade hierarchies are required.


Other reporting requirements

RTS28 is not just about supplying a Top 5 report. Analysis of the execution obtained across these Channels, Counterparties or Venues is also required with a view to understanding if there is consistency across allocated volume and performance. But the definition of performance is no longer simply ‘best price’. Indeed, the MiFID II definition of best execution refers to a range of factors, including price, some of which may be relevant to some institutions in they way they execute in a hybrid FX world, some of which won’t be. Clearly, these factors need to be defined, prioritised and set in accordance with each institution’s best execution policy. Only when this has been done can any view of overall ‘performance’ be measured, aggregated and reported.

Over time it is fair to assume that these ambiguities will decrease as market consensus develops and further guidance from bodies such as ESMA is provided, especially once a review post the first reporting cycle is concluded. In the meantime, however, institutions are figuring out for themselves. At BestX, our approach has been to take outside counsel advice from Linklaters[2], which has helped provide clarity on reporting requirements in addition to the Top 5 report (e.g. the approach taken to the associated performance reports), and also to ensure that the reporting software is as flexible as possible to accommodate different interpretations and requirements.

BestX allows an institution to define exactly what execution factors are relevant for their specific business and best execution policy. This allows a customised measure of performance to be constructed across any entity, including Channel, Counterparty and Venue. This framework forms the foundation for our Regulatory Reporting module, which allows a client to fully customise and configure exactly what they would like to include in their RTS 28 Top 5 report and also generates the associated performance reports. For example, some clients may wish to generate Top 5 reports for Channel, Counterparty and Venue. Some clients have made the decision to include all spot transactions, regardless of whether the trades are associated or not. Given the delay in LEI code assignment, we also allow reports to be constructed without this official designation to at least ensure that the first round of reports in April can be generated.

It is clear that regulators are looking for evidence of a best efforts approach to satisfying the reporting requirements, so a pragmatic and flexible approach is probably a decent strategy in these early months of a post January 3rd 2018 world.


[1] For younger readers, this relates to January 1st 2000, when the world waited with bated breath to see if computers would continue to function

[2] Please contact us if you would like further information on this legal opinion (

What are the factors that drive the cost of forward FX?

“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
Dan Ariely

As part of our ongoing quest to enhance our analytics, and to continue to meet our clients requests, we have been spending considerable time over the last few months researching ideas to model the expected cost arising from the forward point component of FX transactions. Such a model would complement our existing framework for estimating expected costs for the spot component.

This research is far from straightforward. The FX Forward market is still a largely voice driven market, often with significant biases in pricing arising from supply and demand or basis factors. This results in a general lack of high quality data with which to perform rigorous modelling. At BestX, however, we do now have a unique data set of traded data that allows for such research and we hope this will provide the foundation for the production of such a model.

We have decided upon a 2 phased approach. Phase 1 will be a relatively simple, yet pragmatic, extension of our existing parametric model for expected spot costs. We plan to launch this in Q1 to meet the initial need for a fair value reference point for the costs arising from forward points. Phase 2 is a longer term project, which will take us down the road of a data-driven approach as there are indications that a parametric model will have limitations when attempting to model the foibles of the forward FX market. We are already planning for this and have started research into using machine learning methods, including nearest neighbour algorithms, to cope with the complexity of this market. As part of this research, one of the initial pieces of work was to try to understand what the key drivers for FX forward costs actually are as we are aware of the risks of utilising machine learning on big data sets without an understanding of the fundamentals. We have summarised the initial findings of this work here.

Total Transparency

Not everything that can be counted counts, and not everything that counts can be counted.
Albert Einstein

The demand for transparency within the execution process has increased significantly over recent years within the FX market. Indeed, BestX was founded to try to help meet this demand and we have adopted this theme within everything we do. We set out to build a market-leading set of analytics and ensure that all of our clients have total transparency around the workings of these models. Such analytics can only add real value if they are powered with the highest quality market data. Transparency around the market data inputs used is therefore also critical and we have invested significantly in order to build a comprehensive view of the FX market. This article explores some of the thinking behind our approach and why we believe it is important to generate the most broad, independent and representative view of the market.

In an OTC market such as FX one of the biggest challenges when trying to compute accurate execution metrics is gathering a data set which fulfils the following criteria:

-          representative
-          clean
-          independent
-          normalised
-          timely

Below are some of the common themes and challenges in building such a data set.

·       Breadth and independence of data

One of the most common topics when discussing market data and benchmarking is the breadth of sources used and the independence of such sources. Independence and the complete absence of any bias is critical in delivering a market standard for FX best execution metrics. Computing a mid based on such a broad array of liquidity providers globally is far more valuable than generating a potentially skewed mid based on a specific sector of the market. For example, if a mid were computed based on liquidity sources biased towards to non-bank high frequency traders, this would clearly be inappropriate for use in estimating costs for large institutional asset managers. BestX takes market data from over 100 liquidity providers, supplied to BestX through a number of pipes, one of which is the Thomson Reuters pipe in addition to ICE and EBS. Thomson Reuters is not the only source, and even if it was, it is not a single price as data from all of the individual liquidity providers is accessed.

·       Generating benchmarks based on client specific liquidity providers

This is an interesting point and one which we debate frequently. Aside from the fact that regulations such as PRIPPS stipulate gathering data from as representative set of sources as possible, we believe that for the institutional market it is important to portray a view of the total market. To simply compute costs based on a client’s specific liquidity sources is self-reinforcing and could be argued is not satisfying best execution as perhaps there are other sources out there that a client could access but currently doesn’t? In addition, there is a growing demand for one level playing field to compute costs across, that could be used to meet demands for, for example, peer analysis. If the market data set is tailored for each client in this universe then we would always be comparing apples and oranges.

At BestX, we do also provide the ability for clients to submit their quote data, which we will use as additional benchmarks if so desired, as some best execution policies require this. However, we provide these metrics in addition to the spread to mid costs based on the full market-wide data set.

·       Internal pools

We would argue that, even if it were available, would data from liquidity provider’s internal pools add any value when trying to assess price discovery and generating a market mid? The price forming data and flow is available via the lit electronic marketplace, where liquidity providers risk manage the ‘exhaust’ of their inventory. The activity of internal pools is interesting, although would not add value in determining the market mid at any one point in time, e.g. having offsetting trades match and internalise wouldn’t necessarily change where the external market is trading.

There is clearly significant value within the overall best execution outcome through internalisation, and we measure this via other factors to demonstrate this value (e.g. through post-trade market revaluation and impact metrics).

·       Timeliness of data

There is a lot of focus on market data sources and independence, and rightly so. In addition, however, there is also a requirement to ensure that data is timely, especially in the FX market. Using stale data, for example, snapped at 1 or 5 min intervals or worse, can obviously potentially generate erroneous cost and slippage metrics. It is imperative to be gathering data on a millisecond frequency and in real-time to allow for immediate transaction analysis if required.

·       The FX Tape and other potential sources

The recent announcement of the launch of a tape for the FX market is an interesting development. Clearly, this is an initial step and there are many questions still around exactly what will be available, at what cost and with what lag. It may be that it could provide BestX, and all other providers, with an additional ‘official’ source of traded price data, although for it to be truly representative it will require all of the large liquidity providers to participate fully. This would, obviously, be extremely valuable and could be used in addition to the broad market data set we already consume and aggregate.

Equally we will be following the evolution of what trade data becomes available via the APAs once MiFID II goes live. It is unclear at this stage exactly what will be available and how timely the data will be, but it could provide an additional source. The trade data that became available following Dodd-Frank disappointed to some extent as it wasn’t rich enough to use for rigorous analytical purposes, so we are reserving judgement on the potential data riches that may flourish from MiFID II until we can actually see it.

·       Credit

We don’t generate pools of liquidity adjusted for different credit quality or capacity. The philosophy is to generate a representative picture of the institutional market that can be broadly applied to compare and contrast performance and cost metrics.  Additional benchmarks can be customised on a bespoke basis to service specific liquidity pools if required.

OTC markets make the provision of representative, accurate TCA metrics difficult. FX doesn’t have a National Best Bid and Offer (NBBO), there isn’t a source of public prints and there is little consistency across the industry in terms of what data is made available. The current situation may obviously change over the next few years, for example, via the FX tape or a shift to an exchange based market structure, but it seems unlikely to happen in the medium term. We have taken the pragmatic, and rigorous, approach to gathering as much high quality data that we can and use it in a thoughtful way across a suite of analytics. One of the core tenets of BestX is the delivery of an analytics product that is totally free from any conflict or bias. Independence and total transparency is therefore critical, both in terms of the analytics and input market data.

A New Beginning for Fair & Transparent FX Markets

A New Beginning for Fair & Transparent FX Markets

The release of the final Global Code of Conduct (“Code”) on 25 May 2017 is a watershed moment for the foreign exchange (FX) market.  The FX market, which is a global decentralized market for the trading of currencies, is the largest market in the world in terms of trading volume, with turnover of more than $5 trillion a day.  The Code was developed by the Foreign Exchange Working Group (“FXWG”) working under the auspices of the Markets Committee of the Bank for International Settlements (“BIS”).  The Code was also created in partnership with a diverse Market Participants Group (MPG) from the private sector.  A Global Foreign Exchange Committee, formed of public and private sector representatives, including central banks, will promote and maintain the principles. 

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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.


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.


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

Applying the Pareto Principle to Best Execution

Applying the Pareto Principle to Best Execution

BestX launched its first product last September, delivering a comprehensive set of analytics and reporting for post-trade best execution in FX. The software was designed to satisfy internal and external best ex requirements, including regulatory reporting requirements, often referred to colloquially as ‘box ticking’. Perhaps unfairly given this is a vital component of the fiduciary responsibility of asset managers to asset owners, and more broadly, of increasing importance to all FX market participants given the Global Code of Conduct and other initiatives. However, this article seeks to explore the value that the software can bring over and above the core ‘box ticking’ requirements

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Red Flags and Outlier Detection

Red Flags and Outlier Detection

Although there is still considerable debate on exactly what ‘best execution’ means in the FX markets, one component that has become clear is that any best execution policy should include a process to identify, monitor and record outliers. The question now arises – how should I define what is an outlier? As with most things, as soon as you start getting into the details it becomes clear that this is not necessarily straightforward and involves a number of factors. In this article, we explore these factors and suggest some approaches for what we are seeing at BestX evolve as best practice.

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Feedback loops and marginal gains – using TCA to save costs and improve returns

Feedback loops and marginal gains – using TCA to save costs and improve returns

Continuous performance improvements, whereby all aspects of a process are examined with precision, are the hallmark of many leading teams and businesses. Seeking out such marginal gains, as exemplified by Sir Dave Brailsford with the GB cycling team, or the Formula 1 team of Mercedes McLaren, has now become commonplace, and in this article we explore how such approaches can be applied to a continuous refinement of best execution.

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Best Execution for Asset Managers Under and Beyond MiFID II – A User’s Guide

Best Execution for Asset Managers Under and Beyond MiFID II –  A User’s Guide

This report breaks down the texts of the relevant FCA and MiFID II regulations, with a particular focus on the practical implications for the fixed income and FX markets. It also provides insight into how the MiFID II best execution requirements are of relevance even to those products  and companies outside the technical scope of the legislation, and in many ways set the new standard for how best execution should be monitored and assessed. Lastly, the importance of new technologies and rigorous data analysis in this new era of best execution compliance is emphasized.

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Factors to consider when implementing a TCA framework

Factors to consider when implementing a TCA framework

There are many different terms and methods used to describe and analyse the costs and associated performance of execution. There is an element of choosing the right tools for the job, and some market participants may require a less extensive range of metrics to measure costs and performance. However, there are some fundamental elements that form the foundations for any meaningful analysis and in this article we explore these core components.

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Signalling Risk – is it a concern in FX markets?

Signalling Risk – is it a concern in FX markets?

What actually is signalling risk and is it something I should be worried about? This article will seek to answer both of these questions. As a topic, signalling risk has become more widely talked about in the FX markets following the move to a more order driven market, and the increased adoption of the use of algos. There are benefits to using execution methods such as algos , although there are also associated potential drawbacks. Signalling risk is one of these drawbacks.

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Best Execution - Do Algos have a role to play ?

Best Execution - Do Algos have a role to play ?

The use of execution algorithms in the currency markets has increased significantly over recent years and it would appear that this trend is set to continue for the foreseeable future. In this article we explore the benefits of using algorithms, but we also review the potential pitfalls that users should be aware of if they are to incorporate the use of algos in their execution process.

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Global Code of Conduct for FX Explained

Global Code of Conduct for FX Explained

The Bank for International Settlements (BIS) Foreign Exchange Working Group (FXWG) published the first phase of the Global Code of Conduct for the Foreign Exchange Market (Global Code) today.  It also published principles for adherence to the new standards, entitled FX Global Code: Public Update on Adherence. Final publication of the complete FX Global Code is targeted for May 2017. In this article, we attempt to highlight and explain some of the key principles behind this publication.

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Best Execution Under and Beyond MiFID II – a User’s Guide post Brexit

Best Execution Under and Beyond MiFID II – a User’s Guide post Brexit

The new best execution requirements under MiFID II will require banks, asset managers, and other MiFID investment firms to put into place substantial procedural and technical changes across a wide range of asset classes. This article seeks to break down the texts of the relevant regulations, with a particular focus on the practical implications for the fixed income and FX markets and the importance of new technologies and rigorous data analysis in this new era of best execution compliance.

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Using Execution Benchmarks - Why ?

Using Execution Benchmarks - Why ?

Benchmarking execution within FX has become standard practice. Benchmarks provide the reference points against which to measure performance. Using benchmarks is a no-brainer, however, the selection of appropriate benchmarks, and ensuring they are computed using a standard methodology using consistent market data, is far from a no-brainer. In this article we explore some of the larger landmines to look out for, and offer some suggestions for an approach for benchmark selection.

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