Despite worries in the lead up to the monumental shift, many have managed to adapt their workflows to evade issues across the ETF market, securities lending and FX alike, while adapting to affirmation and central trade matching platforms to achieve straight through processing.

However, while many buy-side have hailed the migration as considerably smooth, there are some unexpected patterns emerging in how expensive it is to trade on certain days thanks to misalignment with other regions that have not shortened their settlement cycles, and this is leading to a lack of liquidity.

Presenting the Thursday conundrum.

Given that the settlement cycle is now shorter in the US trading volumes on a Thursday have dropped off significantly thanks to funding requirements that require brokers to fund a position for an additional three days on Friday, Saturday and Sunday given the slightly longer settlement cycle in Europe, the UK, and most of Asia Pacific.

The issue has been flagged by participants in various arenas, most recently on stage at the inaugural CMX conference held by the Finance Hive last week. When quizzed on their views of how the market was handling the transition to T+1, the issue around trading on a Thursday was raised several times by buy-side speakers.

Thursday volumes were noted as “muted” thanks to what some were claiming was an extra five basis point charge on trading for orders made on that day thanks to broker funding requirements over the weekend.

Jim Goldie

“The impact on a Thursday is that brokers need to fund for another three days. Additional funding over the weekend will manifest itself through wider spreads. A few bps matter,” said Jim Goldie, EMEA head of capital markets, ETFs and indexed strategies, Invesco.

“Brokers are pricing two different levels, one for T+1 settlement and one more expensive option for T+2 settlement. We’re in a suboptimal place with global misalignment. Depending on the day of the week or the settlement cycle used it’ll be more expensive to trade.”

“It’s not just Thursdays but the day prior too thanks to the funding issue. From a basket perspective, banks have been willing to do extended settlements but they charge for that. Somewhere in the system someone is picking up the tab. These are the complexities that go away with alignment.”

Highlighted by many is the fact that the industry is yet to go through a public holiday falling a Monday or impacting the end of a week in a post-T+1 environment and this will likely exacerbate many of the patterns we are seeing emerge.

“We’re in a wait and see phase. There is the Friday the 5 [July] issue but we’ll have to wait until September for a three-day weekend. There’s a hyperfocus now but what does business as usual look like? Will the SEC [US Securities and Exchange Commission] start to implement fines?” said Callum McPherson, dealing manager at Evenlode Investment, also speaking at CMX.

Elsewhere, several banks have reportedly sent notes to clients letting them know that they intend to pull liquidity currently being provided between five and six pm on a Friday NY time given that they have seen zero executions in this window since the shift to T+1 and given that everyone is now pre-funded.

Europe and the UK must move together

With the UK and Europe on a misaligned settlement cycle to the US, some have urged the UK to move on with its own shortened settlement cycle plans now that it is no longer part of the EU.

Callum McPherson

However, as noted by buy-side speakers at CMX, this would leave UK traders at the mercy of the same misalignment-related issues as seen currently between the pan-European markets and the US.

“The UK should move in step with the EU,” said Huw Gronow, head of dealing and implementation, Newton Investment Management.

This was corroborated by Goldie: “If the UK followed strict timelines it could be there by 2026. UK market structure isn’t that complicated. But the UK Government and regulator listened to the industry. We would see the same pain points in Europe. The UK and Europe need to move together otherwise it’s just more misalignment.”

The UK put together a taskforce in 2022, releasing its first report in March of this year that confirmed that the UK should move to T+1 no later than December 2027. Its final report will be published at the end of this year.

Meanwhile in Europe, the European Securities and Markets Authority (ESMA) is set to publish its own report at the end of this year, latest in January 2025, Nina Suhaib-Wolf, director market practice and regulatory policy at ICMA confirmed.

She added that there would be a public hearing on the subject on 10 July and that it had become a question of “how not if” in Europe.

Real time settlement

When asked about the benefits of real time settlement, speakers on stage at CMX were unanimous that both the UK and European markets should focus on the move to T+1 before beginning to tackle a move to T0.

Nina Suhaib-Wolf

“It isn’t something we have time to talk about. We’ve been preparing for T+1. T0 would remove a lot of legacy systems and the custodian function. The regulatory environment would need to change. We’ll get there after alignment on T+1,” said Goldie.

“If building the system from scratch now it would be like the digital asset system with instantaneous settlement. The final step will need to be blockchain related,” added McPherson. “The advantage of real time settlement would be that the investor gets their investment back the same day.”

So long as Europe and the UK maintain a misaligned settlement structure to the US, snags in the workflows of the industry will continue to show themselves. Many institutions have done well to accommodate the change – it’s smooth but it’s not optimal. The industry is also yet to experience a major liquidity event under the new regime and this will surely put it to the test and reveal any major cracks left unidentified.

The post The Thursday T+1 trading conundrum appeared first on The TRADE.

In the absence of immediate sources, we may place the order in our internally developed retail algorithm wheel. This will agnostically place a UK retail stock with one of the specific algo retail brokers in the wheel and attempt to access retail liquidity within the selected brokers systems, who will also be accessing the retail service provider (RSP) network. Hopefully, this will gain access to retail liquidity which may not be otherwise obvious.

Using retail brokers algo products as above should be a useful tool in the search for such liquidity. However, the challenge for the buy-side is to find out which brokers are more likely to be trading in specific names, and at what times of day retail flows appear.

In an ideal world, this information would be automated into our Execution Management System (EMS), so that the retail wheel itself can either choose or recommend which broker to select to access liquidity in the most efficient manner.

Can platforms be leveraged to access liquidity more effectively?

All of the above refers to UK equity retail flow and the RSP network. We are aware that most major European countries have increasing volume of retail flow, which is currently difficult for institutions to access, or indeed know how to access. This may be due to a number of reasons, for example some brokers may choose to keep retail and institutional flows separate.

As retail volume and especially value increases, it would be useful if these flows were accessible to institutions. Furthermore, for effective and efficient access, it would be useful if this could be traded electronically.

As the brokers in question are possibly smaller regional or national brokers, they may lack the economies of scale or technological capabilities to create similar algo products used elsewhere. Hence this continues to be a challenge to effectively access these retail flows.

The post Adrian Bradshaw: Retail liquidity – the current state of play appeared first on The TRADE.

Formerly a compliance box ticking exercise, firms are now leveraging TCA as a tool throughout the execution process to achieve better results. Today, trades are not solely measured by outcome in isolation, but instead, the focus has shifted towards understanding the holistic impact of every transaction on portfolio performance, risk management, and overall market dynamics. TCA offers a sophisticated methodology designed to scrutinise an entire trade journey, from inception to completion, unravelling the hidden costs and opportunities along the way.

Evolution

From its nascent beginnings as a rudimentary tool for post-trade evaluation, TCA has evolved into a more sophisticated device, in some cases utilising advanced analytics and real-time data to assess and bolster trade execution. However, reservations about its usage still exist among users.

“There’s still this mistrust of TCA [among traders] because who’s to say whether I’ve done a good job or not? There are so many different components that answer that question and you can’t capture it all in data,” argues Paul Squires, head of trading, EMEA and APAC equities at Invesco.

“There’s often that initial resistance but we’re moving to a place where traders are now, for the most part, much more engaged with it. Traders look at data and deploy it in a much more meaningful way in terms of their day-to-day job, rather than a monthly compliance check that you need to complete – TCA is becoming something that is much more embedded in trading decisions.”

TCA is evolving towards something more proactive, utilising predictive analytics that enable market participants to anticipate and mitigate execution risks, optimise trading strategies, and help to generate alpha. The data is increasingly being used as more than a simple measurement, but instead is being applied to make better informed trading decisions.

“Some of this has been regulatory driven, of course, but there remains the quest – and rightly so – to utilise TCA for actionable insight and additional alpha, with the users getting smarter and smarter in how they approach this,” says Michael Richter, global head of trading analytics at S&P Global Market Intelligence. “[…] There seems to be more synchronisation around the analysis internally than there ever was.”

Instead of simply being a report on outcomes, TCA is now using real-time data to provide organisations with methods to achieve better outcomes. Back testing with historical data is also being used to identify where a trading decision could’ve been changed to reduce costs while still achieving trading objectives. And these reports are being shared at investor level.

“We’ve gone from providing TCA because it’s a need and a requirement for a regulatory purpose, to actually looking at the TCA to drive future trading decisions and to drive an improvement in the overall outcomes of trading using the data as the insight for that process,” states Victoria Bryan, vice president, lead data analyst for capital markets at Northern Trust.

Value

When it comes to TCA, we’re talking about measuring a theoretical and implicit cost of trading which affects performance. By being able to measure that and then – in the best case – improve on that, a real measurable value linked to TCA is established.

“The value of TCA is you’re spending time thinking about your investment process, how to clean and capture that data, how to communicate that data back to end users in a way to help them understand markets better, understand their counterparties better, and understand their trading workflows better,” says Kevin O’Connor, head of analytics and workflow technology at Virtu Financial.

TCA is only as valuable as the output, and it needs to be incredibly accurate. The interpretation of TCA requires people to know exactly what it is that they’re looking at and ensure it is reliable and robust.

Adding to that, performance can be improved overtime by using post-trade analysis and plugging that back in at a pre-trade level, which can then potentially shift decision making and trading processes.

“As a TCA vendor, we can provide best-in-class proprietary metrics, but it all comes down to how the firms are using the data. TCA and trading analytics are at their most powerful when the output is used in a way to actually tell a clear story,” emphasises Richter.

“TCA was built in the first instance to provide users with benchmark measurement of their trading activity and to provide actionable insight into their trading, the compliance use case was always secondary.”

As much as TCA can provide valuable insights to make data analytics more actionable for traders, Invesco’s Squires reveals a much more practical level in which TCA can be used, particularly in a managerial position.

“If you put data in front of a trader and say, without any agenda, without any bias, your trading data looks a little bit better/worse this month than last – whether those results are authentic and representative or not, what you will get is a response,” he says.

“The benefit of TCA is not from forensically analysing the data – and there is an argument that we have become perhaps a bit too forensic about it – but simply the fact that it creates a discussion with your traders about their performance and why they have behaved in certain ways.”

Usage depending on asset classes

TCA varies across asset classes due to differences in market structure, liquidity and execution dynamics, requiring tailored methodologies and metrics for more meaningful insights and accurate evaluation.

More mature asset classes such as equities tend to be able to extract the most value from TCA, but increasingly other asset classes are becoming more advanced in their use of data and analytics in parallel with increased electronification and on-venue trading in some markets.

Equity TCA is the most robust out of all the asset classes because it has the most market data associated with it, argues Northern Trust’s Bryan. “The other asset classes are more challenging because there is less data available for the comparison,” she says.

Generally speaking, it can be argued that if you’re trading algorithmically or trading on- exchange, you can be pretty granular and precise with the analysis that you’re doing. Something that doesn’t extend to all corners of the markets.

Mark Montgomery

“In fixed income, you’ve got things that are exchange listed, you’ve got very liquid bonds that trade as freely as equities and they’re somewhat easier to measure. However, 90% of bonds don’t trade from issuance to maturity. So how do you even know what the right benchmark could be for something that doesn’t trade if you were forced into that execution?” questions Mark Montgomery, head of strategy and business development at big xyt.

“The more regulated the market and the more exchange driven it is, the more effective I believe the TCA can be.”

Over the years, TCA within asset classes such as fixed income has grown into a more mainstream product, being used by investment institutions worldwide.

However, “each asset class has to be taken on its own merits, as they have unique market microstructures and nuances which have to be taken into account,” argues Richter. “We see much more of a compliance use case in these asset classes as opposed to the preferred actionable insight approach.”

Similarly, foreign exchange (FX) TCA can be argued to be less developed when compared to equities. However, it has grown from simply defining what was being measured, to now determining if there is something that can be applied differently to improve trading outcomes.

“The principles are generally the same across asset classes – measure first, decide if there’s some way to compare it to an alternative and decide if it’s worthwhile to change the process to actually lower costs,” says O’Connor.

Making trade analytics more actionable

With growing amounts of available data, it brings into question how this is applied. Having data is only useful if it is then used to better inform trading decisions. TCA culminates knowledge of past outcomes and pushes it towards making a recommendation for how one can trade a specific order when a similar scenario comes about.

“There are some benchmarking elements which are based on normal market conditions or our own historic trading data,” says Invesco’s Squires. “Then there’s a sense of what’s actually happening in the market at the particular moment, where trader instinct can override the data-driven, backward-looking recommendation. The point being that the signal in an EMS to make that recommendation, is looking at historic TCA. TCA is really gauging whether the previous way you traded it worked well or not.”

Building on this, TCA is able to provide insights into who has been traded with, how it was traded, and the costs associated with that trade, while also providing potential new techniques to tackle that trade in the future.

“It’s actionable in the sense that I’ve analysed data, drawn conclusions from that and then used it to perhaps make a change in how I’m segmenting orders, how I’m routing orders and who I’m giving those orders to,” says O’Connor.

Ultimately, it comes down to how firms are using the data. “Gone are the days of people just looking at implementation shortfall and VWAP,” emphasises Richter. “There is a need to understand direct market impact, reversions, liquidity vs aggressiveness, adverse tick activity etc. Using these smarter metrics where necessary, provides a much more valuable analyses.”

To make trade analytics more actionable, we see increasing collaboration between traders and data scientists and/or research analysts. The core benefit of the these typically separate teams communicating with one another is helping shift data into something that is more substantial and useful in the grand scheme of trading.

Victoria Bryan

“If you look at equity TCA and you look at all of the data points that are available, it can be overwhelming to look at and you’re not really sure how to get those actionable insights out of that analysis. If you have an analyst or data scientist, a team of people that are able to disseminate that information, what they can do is they can find what those post-trade insights are,” says Bryan. “By putting it into some sort of pre-trade

philosophy or mechanism, you’re able to test if those actionable insights were valuable and then that gives you more data to support getting closer and closer to the best results over time.”

Collaboration is key

Increased collaboration between traders and data scientists is proving beneficial in helping improve future execution strategies. It is worth noting, however, that a huge level of trust needs to exist between the two.

Collaboration needs to be impartial. If traders don’t have the confidence in the underlying data, or the people presenting that data, it is not going to be as valuable. In the same breath, it has to be understood that the role of the trader is still paramount.

“Once people realise that you can’t exclusively do this from a data science view, you need to pair it with someone who is living and breathing in this environment, that’s when you start to really get some of these experiments that are successful, where you actually start to see cost savings or optimised trading,” argues Erin Stanton, global head of portfolio and trading analytic client support at Virtu Financial.

“We’re starting to think about how we can summarise down the metrics to two or three really easy to consume pieces of information and present those to the trader as a co-pilot scenario. It’s not autopilot – I’m not bypassing the trader – I’m instead enabling the trader with better information than they had previously.”

Collaboration is essential, however, trader intuition is still incredibly important given the varying nature of financial markets on a daily basis.

“The trading research team can do much better analysis on smaller orders, partly because liquidity profiles are easier to understand and are more predictable. As soon as you get an order that’s more than about 30% of average daily volume – i.e. spans more than a single day – a lot of those metrics go out the window,” argues Squires.

Kevin O’Connor

“We let the research team run with their testing on systematic orders but we definitely apply a liquidity profile above which we need to be more circumspect about pure quantitative analysis and that’s where traders can apply their own experience, instinct and dynamic knowledge of the market and reading of the market.”

With increased trust and an understanding of the roles played by the trader and data scientist, once the value of TCA is established, the data can help free up time for traders to focus on more important, higher value trades.

“It frees up the trading desk to carry out their primary function of trading knowing that the performance numbers and TCA data are being scrutinised by an expert who will highlight patterns and trends which feedback to the trading desk and ultimately add significant alpha to the way orders are traded,” says Richter. “It’s not so much a luxury anymore but more of a necessity to have this type of resource.”

Moving forward

As with anything related to technology, improvements can still be made, with the same applying for TCA. Although improvements within this function can appear to be incremental, it is still worth acknowledging that progress is being made – be it through more actionable data insights or improved usage of TCA in asset classes apart from equities.

“Thinking about things like consolidated tape as an industry, that’s being worked on in fixed income and progress is being made – that’s the type of thing that would improve the value of a fixed income TCA report and the output of that report,” suggests Bryan.

“Trying to get to the state that we’re at with equities with the other asset classes may be somewhat of an impossible dream, but if people aim for that to be the golden standard, the closer we get to that, the more valuable TCA is going to be for the other asset classes.”

As TCA progresses and improves, the hope is that trade analytics can become more actionable, resulting in improved trading outcomes for its users. The advancement shouldn’t be viewed as an attack on a traders’ abilities, but instead something that aids processes.

Erin Stanton

“One thing that I think people forget is traders have ears, having the communication through our eyes and mouths is one thing, but what you hear around on a trading desk is harder to bring to bear,” says Montgomery. “The interpretation of market movement at the moment in time and the ability to filter out the noise as well is important.”

As previously noted, collaboration between traders and data scientists is essential as it has the ability to merge domain expertise with quantitative rigor, ensuring that insights derived from data actually align with the reality of current markets.

“To turn even comprehensive data analysis into a decision about how you trade differently is really difficult – but our aspiration – as you can end up being a bit too swamped with data and unable to draw any clear conclusions. Where this can evolve is drawing on reliable, pre-trade and proprietary data in your EMS which is smart enough to make a trading recommendation based on an expected trading cost vs an execution outcome that is ‘guaranteed’. It should be easier than it currently is – the concept is simple – but getting trustworthy data is really, really challenging. That’s where we aim to get to,” concludes Squires.

The post Conscious usage of TCA: Making trade analytics more actionable appeared first on The TRADE.

As markets become increasingly complex and interconnected, the need for speed, precision, and automation has become paramount. From the early days when algorithms were basic rule-based systems executing predefined strategies, to the present era of machine learning and artificial intelligence-driven models, the evolution of algorithmic trading is central to the adaptability of financial markets. 

“Algorithmic development has and always will evolve to achieve the best performance possible versus the client benchmark,” says Alex Harman, head of EMEA electronic and program trading at Goldman Sachs. “That would involve minimising footprint via enhancing order placement and internalisation, having extensive liquidity capture via the SOR and a framework of customisable algorithms built upon a fast, scalable algorithmic platform.”

Simplification and automation have been key focus areas on the buy-side. The goal is to enable high touch traders to adapt dynamically to market conditions while still ensuring that algo strategies remain simple so that those strategies can be correctly measured and compared. 

“On our systematic side, we have adaptive algorithms that are identified in our EMS and routed to multiple venues to achieve the optimal outcome,” notes Samuel Henderson, EMEA equities head trader at Invesco.

“This adaptive automation allows us to manage hundreds of orders quickly and efficiently at below pre-trade costs and most importantly without adverse selection. As we expand our database of measurable historic trades, our machine learning insights continue to enhance the decision making of our algos.”

Evolving client demand has driven innovation in algo trading. More traditional strategies, such as VWAP for example, have begun to incorporate machine learning and predictive techniques to remain relevant. Increasingly, clients are looking for more advanced methods of liquidity seeking, in particular in harder-to-trade stocks during liquidity events, such as the close and monthly expiries.

“Trading is always a trade-off between price impacts and opportunity risks,” says Ben Springett, head of electronic and program trading, EMEA, at Jefferies. “The longer I take, the more opportunity/risk I’m exposed to. We see a migration of strategies toward higher urgency liquidity seeking; we see people moving away from VWAP; and we see less people willing to wait for the closing auction.”

During periods of high volatility, some quant funds as well as funds which typically use long duration or schedule-based strategies such as VWAP or TWAP, will see a shift in urgency to go into more arrival like benchmarks such as liquidity seeking algos. 

“Clients that continue using schedule algos tend to shorten the order duration and in addition, they look to customise participation in the closing auction,” notes Harman. 

Volatility

During periods of increased volatility, algo strategies come to the forefront even though their usage isn’t necessarily changed entirely. Instead, it becomes a by-product of a change in objective of the buy-side trader. A greater sense of immediacy becomes apparent for traders in these periods, resulting in a shift in algo strategies.

Typically, as volatility increases, liquidity decreases, resulting in an increase in impact. In such periods, it can be observed that traders move from automated algo trading to high touch and portfolio trading, relying on more blocks. 

“As market volatility increases, we find clients tend to specify more algo parameters on an order level i.e., ‘offsets to benchmark’, ‘would levels’ and ‘smart scaling’,” notes Chris McConville, global head of execution services and trading at Kepler Cheuvreux Execution Services (KCx). “We also see an increase in customised algo usage. In more recent situations where market volatility has increased, we saw an increase in demand for agency blocks.”

Information leakage

When utilising algorithms, information leakage becomes paramount, especially when breaking up orders and dealing with multiple banks. Various techniques exist to help combat the issue, including splitting larger parent orders into smaller child orders to disguise the full intent of a trade, to both the market and a single broker. 

“Trading electronically can prevent word of mouth leakage – but similarly information leakage can be created by using the wrong algorithm or venue in the wrong way,” emphasises Invesco’s Henderson.  

Unpredictability also takes precedent. Firms need to be unpredictable when they respond to price changes, unpredictable in terms of size that they’re submitting to markets, while also maintaining an unpredictable stance in terms of their presence in the market. 

Reducing the predictability of the algo order placement – the child orders in the market – can also help reduce information leakage. In essence, preventing information leakage in algorithmic trading hinges on the intelligent design and execution of algorithms and SORs, notes McConville.

“Strategies like randomising order sizes, managing market entry times, utilising multiple and non-displayed trading venues, and deploying conditional orders are vital,” he says. “These approaches not only protect a trader’s strategy but also enhance the efficacy of their trades in a complex, multi-bank environment.”

Limitations of algo trading

As with any technological advancement, algorithmic trading has its limitations. Although these have narrowed, these strategies can still be improved. One of the biggest limitations is the lack of understanding related to the context behind the orders that are being placed. 

“Even the most sophisticated algorithm cannot know that the portfolio manager has been waiting three days to find liquidity, a potential catalyst is approaching, or that a bullish research note was published earlier that day,” highlights Phil Risley, head of trading and product development at Redburn Atlantic. “The algo will take the statistically correct approach, and adjust for a range of real-time signals, but that may not necessarily be optimal for that stock, for that PM, on that day.”

Algorithms are not one size fits all and another limitation to these strategies is that their appropriateness varies. Just because the tool was the most appropriate one day, does not mean it will necessarily be best tool on the next. Traders must consistently assess the usefulness of algo strategies and amend them appropriately to ensure they provide the best outcome. 

“There may be a change in market conditions, such that an algorithmic strategy was perfect yesterday, when there’s a significant amount of midpoint liquidity, there’s periodic auction, there’s conditional blocks and so on, and today that might not be the case,” notes Jefferies’ Springett. 

“Having an awareness of the real-time conditions that you’re trading into; helps you navigate the limitations of algorithmic trading.”

Another key limitation is liquidity. There is no magical way to create liquidity and algorithms may not be appropriate in every scenario. 

“Pre-trade metrics can give an idea of estimated cost and liquidity – which should help the trader choose the best way to start an order, be it via an algorithm, a high-touch desk, directly using broker capital on risk or any combination of channels,” explains Henderson. 

Most algos are based on a schedule and that schedule can be interrupted by events. This means that if volume is out of character, the algo must guess, which can ultimately lead to negative results. 

“Any algo is bound by the parameters the trader sets, unless the algo is customised. This is where understanding your algo tools is key. And, if you use more than one algo provider, you must make sure you know the differences between them,” stresses BNY Mellon Pershing’s equity trading desk manager, Matt Short.

Trader intuition

As with any technological advancement, the trader’s role shifts as it looks to adapt and improve workflow. Trader intuition is crucial, given that traders can see past historical data and utilise lived experiences to make the best decisions in unusual scenarios, which algorithms may not be able to detect.  

“Buy-side traders have an awareness of the stocks they’re trading, whether or not they’re sectorised, or arranged by portfolio management group, or whatever it might be – they have that underlying experience where they’ve seen a range of different conditions,” notes Jefferies’ Springett. “They’ve identified what can be successful and unsuccessful in those different conditions. And almost on a second nature basis, know what the right tool is for the job at any given point in time.”

Advancements such as artificial intelligence have proven to be beneficial to traders, however, these have been viewed as aids as opposed to replacements of the human trader. The same can be said for algorithmic trading strategies.

The buy-side have better internal tooling via their EMS or data provided by counterparty banks to help ensure they know the best time to use a certain strategy. Thanks to the continued evolution of electronic and algorithmic trading, buy-side traders are now inundated with growing data sets and pre-trade analytics to help determine what to do with a specific order. 

KCx’s McConville highlights that “while the efficiency and analytical prowess of algorithms are undeniable, the role of trader intuition in selecting the right algo remains indispensable.”

A benefit of algorithmic trading is its lack of human bias. Humans inherently have bias, be it conscious or unconscious– something that algorithms can avoid, making them more useful in certain trading scenarios. 

“If you are seeking to identify genuine differences that exist between different things, then having an automated process of managing the distribution of orders across those is critical. It’s impossible for a human being to remove all of their bias from any process,” adds Springett. 

Customisation

Central to much market debate in recent years as algorithms have developed is how much a firm should customise their strategies. Customisation comes with pros and cons, depending on what the algo is being tailored for. 

“When considering algo wheels, it is rare that an out-of-the-box strategy is going to be a perfect fit, so true customisations – specifically designed to take account of both the benchmark and the characteristics of the order flow – are more common,” notes Redburn Atlantic’s Risley. 

Although providing many benefits, customisation can also bring about more complexity, which could lead to increased risk of unintended consequences. To avoid these, robust tests, testing capabilities, QA testing, and change and release procedures are required to ensure that customisation does not impose unintentional consequences. The client’s desired customisation might also achieve different outcomes across brokers.

“While we try to build algos that work really well out of the box, we have several clients who each have different needs and requirements,” notes Goldman Sachs’ Harman. “What one client needs from VWAP or liquidity seeking algos doesn’t necessarily match what the next client will want in terms of performance, venues or urgency.”

Customisation can also increase cost and come with added pressures, including ensuring staff – especially new joiners – are trained up on each change to the algorithm. The buy-side have been vocal at industry events about the danger customisation poses to delaying updates to algorithms. When a new version of an algorithm is released, those firms who have customised it are often left until last to upgrade. 

“It is important to stay disciplined when developing [customised algo] solutions, in terms of documentation, increased testing, or even simply ensuring your client understands what the custom actually does in real life,” emphasises KCx’ McConville. “One thing is for certain, being efficient with customised solutions means you really need to understand agility, to avoid a drag on your resources.”

Central to agility is data. Like with anything linked to automation, data plays a crucial role in ensuring the success of any advancement. Algorithms require reliable sources of data around venue performance, smart order routing, liquidity profiles or opportunity costs to ensure they are beneficial to traders.

That data needs to be updated frequently to ensure the effectiveness of the solution, given that real-time data forms a key part of algorithmic behaviour. 

“It is important to understand that the execution landscape is continually changing and that historical data may not reflect today’s reality, but even more crucial is the recognition that the goal of any form of analysis is to develop insights allowing you to improve results,” notes Risley.

Looking forward

Although having its limitations and with areas of growth still existing, algorithmic trading continues to show promise as a trading strategy to help prioritise time and shift attention to more pressing orders. 

Algorithms are getting smarter and buy-side desks are equipped with more data and analytics to help with algo strategies alongside their tool kits becoming more sophisticated. Although limitations do exist within algo strategies, it appears as though these are narrowing. Algorithms are smarter than ever, but there’s still more work to be done.

The TRADE has actively been tracking developments in algorithmic trading over the past 17 years, by carrying out its annual Algorithmic Trading Survey. First launched in 2008, the survey now receives over 1,500 provider ratings from traders across the globe. To share your views please participate in the survey here.

The post Algorithmic trading: Smarter than ever? appeared first on The TRADE.

I predict we will see increased flows into international equities and active management coming back into vogue amidst a rise in earnings and macro volatility. As money moves back into international equities the demand and competition for liquidity will increase and so, how we access it will become more of a focus. Traders will need different tools as well as the counterparties to navigate. 

We have seen new entrants into the block space in 2023, and I see further appetite in 2024 as buy-side traders seek safer and larger block liquidity – I foresee “superblock” venues and an increase in capital provision. Lastly, as active management becomes more relevant again, so too the impact a buy-side trader can have on the investment process; gone are the days of execution only dealers. 

Daniel Morris, chief market strategist, BNP Paribas Asset Management 

Volatility has become the new normal, bringing opportunities for active managers, and 2024 is likely to see a continuation of the profound and often unanticipated change in the global economy and financial markets that typified 2023. Central banks are continuing to tighten monetary policy to tame inflation, although the global economy has taken higher rates in its stride and the much-anticipated global recession has yet to materialise, albeit that prices are rising at a significantly less rapid pace than a year earlier. Markets will continue to focus on the outlook for growth and inflation, and the implications for the valuations of those assets that are the most cyclical and/or interest rate sensitive.    

Although tighter financial conditions may negatively impact economic growth and corporate profitability in the shorter term, markets are not losing sight of more positive longer-term influences. The widespread application of artificial intelligence will drive innovation and creative destruction in many areas, including healthcare, education, logistics and mobility, as well as being key to semiconductor demand in the coming years. Nonetheless, amid the optimism around the transformative impact of AI, it will be important to be mindful of risk factors such as ESG concerns, regulation and fluctuating investor sentiment.

Thomas Griffiths, head of product, Cassini   

In 2024, central banks will be closely monitoring inflation trends and considering the effects of possible interest rate changes on both inflation and economic growth as inflation shows signs of subsiding in major economies. The direction of rate adjustments, whether increasing or decreasing, will be a crucial area of global attention. 

In Q3-Q4 of 2023, there was an emphasis on the cost of collateral and the strategies firms use to efficiently utilise their asset portfolios for collateral purposes. This trend highlights how elevated interest rates have compelled companies to rethink their asset management strategies, including how they handle fees paid to third-party managers for collateral processes. These funding challenges have underscored the significance of cost-effective derivative trading and the need for an efficient margin and collateral management throughout the trading lifecycle, a focus expected to persist into 2024.   

Regulatory emphasis on the need for centralised and transparent margin requirements is expected to continue. This will likely encompass mandatory clearing, such as the forthcoming regulation on treasury repos, stress testing of margins as recently advised by the Bank of England, and efforts to optimise margins through arrangements like cross-margining, exemplified by the FICC/CME treasury enhancements.

Mark Rogerson, EMEA head of interest rate products, CME Group 

Between 2019 and 2023, the proportion of risk exchange in over-the-counter European derivatives referencing the euro short-term rate (€STR) has jumped from 6% to 32%. This has been a voluntary adoption, not driven by a regulatory mandate. I think there are multiple drivers of this; importantly the level of familiarity with overnight rates has increased across the globe following transitions away from Libor, and separately many customers want to have the same type of benchmark rate on all their rates products meaning ESTR vs SOFR is now almost universally the first choice for cross currency and forward foreign exchange.

Central bank activity has meant that the granularity provided by overnight rate products is more desirable, while the demand for a rate that is grounded in observable transactions has made ESTR increasingly the choice of participants. For all these reasons, I expect the voluntary adoption of ESTR based products to continue into 2024 and beyond.

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Goldman Sachs veteran, Christopher Daur, left the investment bank to head up buy-side sales and relationships at AccessFintech. As part of the role, Daur will be responsible for strategy, product, and sales. He joined AccessFintech after most recently serving at Goldman Sachs in several roles across the bank’s various asset classes and global markets operations, most recently as a managing director focused on post-trade strategy. Prior to joining Goldman, he spent five years at JP Morgan in a range of operations roles.

Head of Europe for UK-based asset manager Federated Hermes, James Rutherford, has stepped down from his role to pursue a technology venture. Rutherford departs the investment management industry after 35 years to take on a more active role in SmartFrame Technologies, a venture which he has been chairman of for six years. He spent 19 years at Fidelity Investments as a European portfolio manager until 2006. Following his stint at Fidelity, Rutherford departed to co-found Sourcecap international, an investment management company for European equities which was later acquired by Hermes International. Following the acquisition, Rutherford spent the last 14 years as head of Europe for Federated Hermes.

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“It’s very easy for the trading desk to just look at straightforward TCA and implementation shortfall. We need to work out what we actually need TCA to do for us as opposed to just how it impacts the trading desk,” said Adrian Bradshaw, senior equity dealer at Invesco.

Best execution analysis is a given when it comes to TCA, however, firms need to take the steps to focus on where the incremental edge of true insight-generating analytics is actually found, as noted by the panellists.

“Insight-generating analytics is how you take TCA from basic analysis to deriving information that can be used to make improvements going forwards, such as where you did well and where you didn’t quite do so well. These are pieces of feedback that I can give to you so that you can improve in the future,” said Victoria Bryan, senior data analyst, capital markets at Northern Trust.

“What people care about at each part of the trade for insights is different and understanding which insights are important at each different part of the trade is important.”

“It’s very easy to say it’s just data,” added Bradshaw. “But we have to make sure we get the right data from the right sources – that’s we need to concentrate on alongside asking data providers what we could utilise the data for.”

TCA provides users with useful data but as some panellists noted, it is ultimately up to them how best to utilise these analytics.

“As a TCA vendor, we can provide best-in-class proprietary metrics, but it all comes down to how the buy-side are using the data. TCA and trading analytics are used at its most powerful when the output is used in a way to actually tell a clear story,” said Michael Richter, executive director, TCA at S&P Global Market Intelligence.

“Did post-trade meet expectations for the trade? If that’s a yes, then great. If not, then you have to look at the factors that influenced that. Keeping the analysis on point with what you want to achieve is the best way you can use TCA data.”

Many firms have started to seek better communication between data scientists and the rest of the firm to ensure TCA is being optimised efficiently.

“The big thing that we have noticed changing is that data scientists are now sitting on the buy-side desks as well alongside brokers and market makers,” noted Mark Montgomery, head of strategy and business development at big xyt.

For TCA to truly deliver true insight-generating analytics it is important that data scientists are working closely with trading desks to ensure there is a full understanding of the data being presented. Panellists highlighted the importance of education around TCA, because without out an understanding of the metrics, they ultimately become useless.

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In the second of two episodes The TRADE sits down with participants from Ninety One, Schroders, Invesco, SIX and Liquidnet to explore the global regulatory debate around dark trading.

Non-displayed trading has become the poster child for regulatory divergence between Europe and the UK post-Brexit. While, in the US, the Securities and Exchanges Commission (SEC) is also undergoing its biggest shake up in equities trading rules to bolster best execution, with many of the changes designed to shine a light on its now bulging dark markets.

This episode explores the shifting regulatory agenda around dark trading in Europe, the US and Asia, as well as how dark trading could be developed further to be more innovative.

If you missed episode one you can find it here.

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The wait is over. The TRADE’s dark trading documentary series, in partnership with Liquidnet, is finally here.

In the first of two episodes The TRADE sits down with participants from Ninety One, Schroders, Invesco, SIX and Liquidnet to unpack the genesis of dark trading.

With non-displayed trading now playing a central role in regulatory change across the globe this episode aims to explore just how we got where we are today: exploring how and why dark trading was brought to the market, key users and use cases, how it was received by participants and how the infamous term “dark” was first coined.

Don’t miss Episode two: due to go live on Monday 20 March!

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