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The need for speed

By
Etienne Mercuriali
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The need for speed

Powered by new computer chips, high-frequency trading (HFT) has reached nanosecond timescales. Fierce competition has prompted a new wave of speculative orders, prompting allegations of market abuse.

HFT used to be measured in milliseconds, with traders using microwave towers to exploit latency in exchange feeds. Those days seem quaint today.

Using a platform provided by BMLL, we analysed 6 years of data starting in February 2019. Our own analysis of 2.3 billion DAX futures orders on Eurex reveals a striking trend: the share of trades modified within one millisecond has risen from 11% in 2019 to over 17% in 2024 [see box]. At Euronext, analysis of a single stock – BNP Paribas – shows an almost threefold increase in sub-100 microsecond order modifications during the same time period.

The hardware arms race and speculative triggering

The earlier generation of HFT relied on technology such as microwave networks to reduce latency between multiple trading locations. But in a competitive environment, even microseconds proved too slow. Specialised computer chips called Field-Programmable Gate Arrays (FPGAs) have now become the industry standard for cutting latency from microseconds to tens of nanoseconds.

As more firms entered the race, vendors stepped in offering custom integrated circuits and optimised networks. David Taylor, CEO of specialist HFT vendor Exegy explains: “Firms pushing into the 10-nanosecond realm are not just relying on FPGAs – they’re designing custom integrated circuits with breakthroughs in optical-to-electronic conversion and low-level networking to capture every precious nanosecond.”

This arms race has led to some creative tactics. Until 2018, Vincent Akkermans was a senior developer at Optiver and is now co-founder at TenFive AI, a company that develops advanced multi-agent AI applications. Before he left Optiver, Akkermans worked with the pioneers of “speculative execution” or “speculative triggering” in HFT, whereby FPGA chips allow orders to be initiated, and then, based on sub-microsecond signals, cancelled by leaving them incomplete before they have been processed by the exchange.

“Using FPGAs to begin processing and transmitting orders before the complete market message is received represents a paradigm shift in trading speed – pushing the limits of how fast orders can be executed,” Akkermans says.

“It’s crazy how much brain power is devoted to shaving off nanoseconds – showing that in a competitive market, even the tiniest advantage is pursued relentlessly.”

European exchanges have incorporated features to allow speculative execution by HFTs – within certain limits. “Speculative triggering refers to certain latency arbitrage strategies when market participants start to send an order before they have fully processed the incoming market data,” explains Jonas Ullmann, Frankfurt-based Eurex board member and COO.

High Frequency Trading charts

To exploit the strategy, HFTs have to occupy a grey area between raw messages and fully-fledged orders to the exchange, where nanoseconds count. “We’ve built in features such as the “discard IP” and “DSCP” field to prevent detrimental effects on market integrity and provide a clear technical framework. Messages sent to the “discard IP” will not reach the trading system gateway and matching engine, so this can be seen as a trash bin,” Ullmann tells Global Trading.

“These packets will be discarded at the Access Layer switch port and no other participant is influenced,” he added. “Packets sent to the discard IP address are not considered to be orders and are not forwarded to the exchange. Due to the nature of where the IP address is specified in the message, you need to decide very early whether to send it to the exchange or discard IP.”

Speculative execution has recently attracted controversy. In March, a Paris-based market maker, Mosaic Finance, accused Eurex of allowing the practice to explode on its venue. Mosaic claimed that Eurex was being flooded by millions of corrupted orders per second, in violation of its rules. HFTs were doing this in order to push their way to the front of the order queue, Mosaic complained.

Jonas Ullmann

Eurex strongly disputes the accusations. “There are strict limits in place,” insists Ullmann, highlighting the difference between raw messages and actual orders. “For instance, participants are allowed to send up to 30,000 ethernet frames per second and no more than 600,000 ethernet frames per minute across the network.” Mosaic’s allegation that it was able to breach these limits by sneaking corrupted data past Eurex’s own monitoring system is “incorrect,” Ullmann says. “The allegations from this individual trading participant are unfounded and all substantive concerns raised have been repeatedly reviewed by Eurex. None of the issues raised proved to have merit.”

Akkermans is inclined to accept Eurex’s side of this argument. “It would surprise me that if lots of invalid network traffic were sent that the exchange wouldn’t notice, although I cannot say with any certainty – I’m not a network engineer. Additionally, the discussions I heard about the speculative execution were always grounded in the desire to only send valid messages. They were well aware that it would be possible to make the message invalid at the final moment, but also that this would contravene the rules and shouldn’t be done. I do believe the culture was such that this was a genuine intent,” he says.

A spokesperson from Optiver declined to comment on whether the firm still conducted speculative execution, and whether it deployed the strategy at Eurex.

The value of technology

Even so, the controversy highlights the competitiveness of the new environment.
Milan Dvorak, CEO of Magmio, an FPGA software provider, agrees that even a moderate jump in speed can bring rewards.

“By moving decision logic from software onto the network via FPGA technology, traders can reduce latency from microseconds to tens of nanoseconds, giving them a competitive edge even if they’re not the fastest in absolute terms,” Dvorak says. “There’s a market for every latency bracket. Even if you’re not the absolute fastest, having a robust strategy means you can still profit from the opportunities available at your speed level.”

Milan Dvorak

At the same time, these technologies require deep expertise. Programming an FPGA takes specialised skills, especially if you want to tweak the hardware’s logic.

According to Milan Kratka, CTO of broker Trading Block who started at Citadel in the early 1990s, “I was one of Citadel’s first quant – and that early experience taught me that in trading, speed isn’t just about quoting; it’s about being the first to respond to a market event.”

He tells Global Trading that while many traders know they must quote quickly, the real edge appears when markets suddenly shift. If you can pull or modify your quote a split-second faster, you either avoid a bad fill or seize a profitable one. Or, as he puts it: “Programming an FPGA is a rigorous process – but once optimised, it delivers decision speeds measured in nanoseconds, offering a decisive edge in today’s fast-paced markets.”

However, HFT vendor McKay Brothers warns about a level of hype over the technology. “We use FPGAs where deterministic behaviour and extremely low jitter are critical,” according to CEO Stephane Tyc. “But they’re not always necessary. An FPGA might buy you nanoseconds; good transport can buy you milliseconds. Both matter, depending on the use case.”

McKay says its approach has broadened beyond latency-obsessed clients. “Many of our recent products are built purely in software,” Tyc adds, noting that “flexibility now rivals raw speed. This isn’t a religious debate for us, it’s a question of fit-for-purpose design.”

Stephane Tyc

Hardware and Integration

There is also competition to provide specialised FPGA hardware. Chipmaker AMD has emerged as a leading provider of FPGA-based hardware, offering chips that support everything from sub-10 nanosecond trades to high-volume analytics.

AMD sales director Alastair Richardson envisions chiplet architectures enabling more modular designs: “By using a chiplet process – breaking a processor into smaller pieces – instead of the traditional large piece of silicon, monolithic method, we can build complex processors, with more capabilities and more efficiency. This includes things like 3D stacking of chips or pushing the boundaries of core counts in a single processor.”

In practice, large trading shops might combine an FPGA for the ultra-fast “tick-to-trade” loop with additional GPUs or CPUs for broader risk modelling and artificial intelligence-based prediction. The net effect is a hybrid computing approach that merges microsecond-level decision-making with bigger-picture analytics – something unattainable until recently.

Fragmented Markets and Regulatory Nuances

In the US, the Regulation National Market System (Reg NMS) has forced firms to route to the best price across multiple venues, making speed crucial for updating quotes instantly and picking off mispriced orders. Europe mirrors this fragmentation under the Market in Financial Instrument Directives (MiFID I, and II), albeit with more complexity around data fees and pan-European connectivity. Firms must link to multiple exchanges to remain competitive, each link adding cost.

According to Exegy’s Taylor: “Market fragmentation in Europe is a significant challenge, connecting to all relevant markets can be two to three times more expensive than in the US.”

Despite these hurdles, a European consolidated tape is on the horizon, potentially lowering entry barriers.

Taylor says: “The current phase of the European Consolidated Tape is just a consolidated trade report – it isn’t directly tradable yet. However, many in the industry are bullish that it will eventually evolve into a tradable tape. This would lower the cost of achieving pan-market visibility and drive more innovation.”

Fast but secure: Protecting trading DNA

Given the stakes at play, safeguarding proprietary strategies, and intellectual property is crucial for trading firms. Vendors like Exegy and Magmio emphasise frameworks that let clients deploy code without exposing it to the vendor.

David Taylor

Exegy’s Taylor says: “We empower our clients to innovate without compromising their secrets – whether through a configurable software interface or a sandboxed FPGA where their proprietary code remains completely private.”

Milan Dvorak of Magmio echoes this: “We deliver our solution as a framework without ever seeing the actual strategy. Our clients maintain full control over their code, ensuring their competitive edge remains completely confidential,”

According to former Optiver developer Vincent Akkermanns, high frequency trading firms embed structural risk considerations in their systems and their operations. Developing teams are close to traders:

“The usual routine was to get in early, listen in on the traders’ briefing, and then get to work on the market links and trading software for which I was responsible. I liaised with traders, assistants, and mid-office and compliance to get requirements. Our team sat on the trading floor, so it was easy pretty noisy, but also easy to get a sense of what was going on.”

For him, “The entire system is engineered so that every role – from the trading floor to the back office – contributes to a high level of technological sophistication, ensuring the market functions more efficiently.”

Tyc agrees: “That’s been a quiet success story over the past decade. The firms operating in these environments today have developed robust internal controls, risk frameworks, and strong engineering ethics. McKay itself is not a trading firm, but we work closely with some of the most sophisticated market participants in the world, and we see firsthand the care they take to build systems that are both high-performing and well-governed.

One of the key concepts that has emerged as a result of low latency investment is determinism. Deterministic systems – those that behave in highly predictable ways – can support more precise risk management, but like anything, too much of a good thing has trade-offs. We’ve seen exchanges that pushed determinism to extremes and inadvertently created unintended market structure effects. A small amount of jitter can help level the playing field; too much, and you get chaotic, inefficient markets. Some venues that could be extremely deterministic have chosen to introduce intentional randomness to prevent predictability from becoming an exploit.

It’s a balancing act – much like the question of private fills printing before public trades. There’s no universal answer. What matters most is transparency: clearly disclosing how systems behave and how access is granted. If disclosure is fair and access is equitable, the market can and will adapt. We don’t need to prescribe a singular model, if the rules are visible and access is open, competitive pressure will do the rest.”

When a single glitch can devastate even established firms, like was the case at Knight Capital. HFT are rightfully obsessed not just with speed but also reliability – a balancing act that shapes internal processes and controls.

Lawsuits and regulatory filings reveal how fiercely competitive the sector is. Consider the lawsuit by Skywave Networks against leading HFT players Virtu and Jump Trading that alleged fraud in the use of experimental shortwave licences (the defendants dispute the allegations). Or the complaint by McKay Brothers against Nasdaq, which forced the exchange to stop offering preferential low latency access to high-paying clients. All market participants are racing to compete.

Can the market keep up?

Proponents argue that high-speed competition narrows spreads, boosting liquidity and benefiting all investors. Critics label it an expensive arms race, with diminishing returns and potential negative externalities when markets become “too fast.” Regulators in the US and Europe have taken incremental steps – such as imposing licensing for HFTs, demanding kill switches, or endorsing “speed bumps” in certain venues – but nothing has fully reversed the move to ever faster speed.

The future may see more frequent usage of AI. Although neural networks can be too heavy for the tightest loop, they can assist in slightly upstream decision-making or in scanning large data sets for predictive signals. Meanwhile, talk of quantum computing hovers at the edge of possibility, but immediate breakthroughs remain speculative.

Alastair Richardson

AMD’s Richardson hints that more integrated hardware solutions – combining FPGAs, GPUs, specialised network adapters, and even novel high-performance computing (HPC) techniques – will likely define the next wave.

“We’re now witnessing a new latency race that involves AI, with some tasks still best served by a dedicated FPGA or ASIC, while others lean on CPU’s, GPUs or NPU’s to do more complex AI inference.”

If anything can slow the chase, it might be the laws of physics. Market data cannot traverse distances faster than the speed of light, leaving only network design and extremely clever hardware optimisations to squeeze out incremental gains. Still, as the last decade has shown, major leaps – from microwave relays to shortwave transatlantic signals – can arise when profit potential is high enough.

Exegy’s Taylor points to the stratification forming in this space: “While the top-tier HFT players operating at nine nanoseconds are few and stable, the dynamic next layer – trading at deep sub-millisecond speeds across multiple assets – is where fierce competition and innovation are truly unfolding.”

Most technology vendors are pushing in this direction, Tyc tells us that McKay has been working with exchanges like Euronext to allow firms to reroute connectivity with no hardware changes, “a seismic shift,” as Tyc puts it, in how infrastructure is accessed.
He adds, “That plug-and-play simplicity destroys the barrier to entry,” Tyc says.

Even if the absolute fastest tier remains exclusive, broad adoption of low latency technologies – from microwaves to FPGAs – has high-speed tactics permeating all corners of trading. And that reality, though it continues to spark debate, is almost certain to persist. As innovations, from AI integration to chiplet architectures, make once-exotic technology more widely accessible, the race to zero won’t be stopping anytime soon. Speed, in all its minute increments, has become part of the market’s DNA.

 

Behind closed doors: Slander and secrets

By
Lucy Carter
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Venues

The use of alternative trading systems (ATSs) has increased drastically over recent years, now accounting for more than half of US equity trading volume. Lately, these platforms have begun to offer a new service, allowing brokers to create closed trading pools for their buy-side clients. These private rooms – also called hosted rooms – allow brokers to internalise their flows without having to create their own dark pools and go through the burdensome technical and regulatory workload this would provoke.

These venues also minimise the market impact of larger trades, with prices remaining steadier than they would on a lit venue.

It all sounds appealing – until you hear whispers that these rooms are not as private as they seem, and that market makers are being let in on the sly to snap up order flow.

Reports of this nature have been swirling for a number of years now, popping up at conference panels and in quiet conversations – although there is hesitancy among individuals to be too vocal about their concerns. For some, this has given private rooms a sordid edge, the platforms looked down on as illegitimate intruders.

Global Trading spoke to buy-side participants to get to the root of the rumours.

Pool party

Mehmet Kinak, global head of equity trading at T Rowe Price, explained how private rooms are filling a gap in the market. “If you run an ATS, I can interact with your liquidity in that dark pool. If you run a single dealer platform, I can interact with you there. But if you are a smaller market maker that has neither of those options, we have nowhere to meet up bilaterally. The easiest solution for us to be able to interact bilaterally is to find each other in a hosted pool,” he said.

Market makers are attracted to private rooms because of their perennial fear of adverse selection in public venues by participants possessing market-moving information. “When trading on exchange, flow is an aggregate of many different market participants with both benign (non-directional) flow and directional flow,” according to Jeremy Smart, Head of Distribution at XTX Markets. “As a result, market makers will provide liquidity that reflects the average of the flow profile. Because of this, the benign flow does not attract the liquidity that it could achieve.

Mehmet Kinak

“If a client has really good flow, it makes sense to go to a hosted room to get some of the best, highest quality liquidity,” Smart adds. “They allow for interactions on a more bespoke basis, without the need to connect to 10 different liquidity providers and for 10 different FIX connections.

“It’s more direct, more disclosed, and can facilitate really strong direct relationships.”

“Because you know who the other parties are, you can show even larger sizes and or even better pricing,” explained Vlad Khandros, CEO of OneChronos Capital Markets, referencing the company’s Nexus platform. “That can increase the liquidity in the pool and provide better pricing. If people know who they’re trading with, they can further lean in.”

There’s a reduced risk of information leakage when private rooms are used, an appealing prospect for buy-side traders desperate to avoid competitors frontrunning them. A limited number of counterparties cuts down the chance of information revealed, deliberately or inadvertently, being used against room participants, allowing them to be bolder in their actions.

“Clients have the ability to completely control how the room works, the prioritisation of who gets the orders first before random allocation – however they want the logic to work, they can set it up,” Smart added.

Jeremy Smart

“Whether you’re a retail broker or a long-only buy-side trader or a systematic hedge fund, it doesn’t matter. Whatever type of client you are, you’re in complete control of who is in the pool, the attributes of the pool and ultimately therefore the liquidity you can access.”

The private room concept is not exclusive to ATSs, according to Kinak. “Single-dealer platforms like Virtu and Citadel are running similar services,” he said. “They wouldn’t call themselves hosted rooms because they have their own venues, but they are essentially doing the same thing, just operating the venue itself.”

Those names have cast a shadow over the space, with rumours that the market makers are being let into hosted rooms and nabbing flow without the knowledge of other participants.

Uninvited guests

Providers, of course, are keen to dispute the claims, defending the security of their systems and the resilience of their compliance.

“You can’t just invite someone randomly into one of our rooms without the sponsor of that room knowing,” Khandros said. “Each side opts in and can know who’s on the other side of the fill. There’s complete control and transparency. We’ve built it with the customer in mind. Giving our customers the option of that full control, we think, is a good thing.”

Vlad Khandros

Among other market participants, those with less skin in the game, the sentiment is similar. Smart is direct: “The rumours are complete fantasy. There’s no lack of transparency there – the client is in full control.”

“These ideas are being pushed by those who want interactions to happen on their terms,” he argued, noting a historical trend of suspicion towards any changes in market structure.

“We’ve heard some negativity from people who wanted to launch a platform and didn’t, or who aren’t being invited to these rooms,” a source familiar with the issue corroborated.

“Conspiracy theories are always running rampant in our industry,” Kinak agreed. “Traders are generally sceptical around how things work. They’re paranoid.”

However, he acknowledges that there is a kernel of truth in some of these concerns.

“It’s true that private rooms are not as transparent as the lit market. Segmentation, by definition, is going to limit people’s participation. But that’s one of the main reasons we use ATSs,” Kinak said.

“I try to dispel the negative connotations around private rooms,” he added. “Lots of people think mysterious things are going on in them, but they’re just a solution to help with segmentation.”

Worries about who else is in a private room are not without precedent.

“When the national market system (NMS) regulation was introduced in the US, there were a couple of scandals about players allowing people into their dark pools without disclosure,” one market participant recalled. “Sometimes it was market makers, sometimes principal flow from their own shops. They were fined and suffered reputational damage.”

“I can’t imagine anyone doing something like that now. It’s so baked in that you have to be clear about how your platform operates,” they mused – but fears of a potential recurrence are high.

“People are concerned whether they can genuinely know who the counterpart is in that hosted room. And the thing is, you don’t know,” Kinak observed. “You have to believe that the venue operating the hosted room is doing things appropriately and legally.”

While that might make the prospect of a private room seem more risky, it’s no different from the situation in any ATS or dark pool. “I find it funny that there’s this narrative of deception in private rooms, because it can happen anywhere. You just have to trust the operator,” Kinak observed. “If I go to the largest ATS, request to only interact with a particular group of market participants, and they tell me they’ve codified it that way, I just have to believe them. There’s no way to verify what they’re telling me.”

Clearer waters

As such, uncertainties around private rooms are fuelling a wider conversation – ATS regulation as a whole. Currently, in the US, ATSs are obliged to report all trades and orders to FINRA under the CAT regulation. CAT was designed to prevent manipulative trading strategies and tactics, Mark Davies, CEO and co-founder of S3, explained, seeking to provide a clearer, more transparent picture of the market after the 2010 flash crash.

The number of shares executed must be disclosed, but not where or who with. As such, the broker actually doing the trade can remain anonymous – and may be far more present in a certain stock or market than they appear to be. This has been used as a reason to illegitimise private rooms, driving concerns that if market giants are sneaking into private rooms, they could be eating up much more of the pie than it seems.

Mark Davies

However, this is part of broader ATS regulation rather than the small private room subsection – regulation that many players have less issue with when it comes to dark pools.

Platforms are also subject to Rule 605 and 606. The former, an order execution quality requirement, is receiving an ATS-specific update at the end of 2025. “Previously a firm like UBS didn’t have to separate out their ATS flow from their main broker dealer flow or their STP flow. Now that has to be reported independently, and there are quite a few more statistics than there used to be,” Davies explained.

Rule 606 obliges brokers to disclose where orders are routed to. This is a hazy area for ATSs, Davies said. “An ATS is generally considered a terminal venue for 606 purposes, which means it executes and therefore they don’t have any onward routing that needs to be reported.

“However, a lot of the ATSs have routers that they run in tandem. If an order is sent to an ATS or private room as an execution venue via a router, then the firm would have to disclose that. The ATS, in turn, would not have to disclose any onward routing.”

When ATS trades are reported, it is not specified where the execution took place. It may have been completed in a dark pool, via trajectory crossing or in a private room – no one, aside from those taking part in the trade, can say for sure. This is one of the transparency hangups people have with hosted rooms and ATSs as a whole, arguing that the non-granular reporting practices are wilfully opaque.

With all the furore around private rooms, it could easily be assumed that they are taking a significant chunk of the flow. That’s the opposite of the case, according to the IntelligentCross spokesperson. “It’s a single digit percentage of our overall volume,” she said, while Kinak cited a less than 10% figure for the amount of ATS flow going through private rooms.

“It’s not significant, but they’re willing to be transparent about it,” he said.

Under the SEC’s ATS-N filing requirements, providers do not have to answer questions about private rooms they provide. The number of rooms on offer, the counterparties involved, fees, and volumes can remain under wraps.

“Transparency around their size, their meaningfulness, is valuable,” Kinak said. “If people can see that an ATS has 2% of total market share, and that 2% of that share is in hosted rooms, they’d realise that there’s no big story. On the other hand, if half of that market share is made up of private room volumes, that changes the narrative. People generally want to concentrate their orders in venues where liquidity is accessible.”

Whether ATSs and private rooms should be more regulated and transparent is a contentious issue. Some argue that more transparency is always a good thing. “I absolutely support additional transparency and regulation around how private rooms are operated,” Kinak asserted, “and I don’t think anyone would be opposed to that. Both the venue operator and the person hosting the specific room would be happy to provide transparency, because they’re not doing anything sinister. They’re hosting the room so they can interact with certain people or a specific segment of the market.”

Others, though, don’t see it the same way, arguing that greater disclosure demands would go against the point of the format. “We can’t necessarily see where flow is coming from,” one person familiar with the subject said, “but that’s by design – we’re not supposed to know. The breakdowns within individual pools are in the hands of the subscribers, not us. People are looking for data on this, on things like retail versus institutional participation, but that’s not consistent or available.”

This is not an opacity issue, the IntelligentCross spokesperson asserts. “Hosted rooms are another tool in the trader’s toolbox in their efforts to access liquidity but not move the market.”

Smart does not see the need for anything to change. “ATSs are perfectly within their rights to create segmented pools of liquidity. Hosted rooms exist in plain sight, in a regulated environment,” he said.

Building trust

Although the amped-up rumours about private rooms are unfounded, they point to a wider demand for transparency – and a lack of trust in the industry. While some are calling for the introduction of new or more comprehensive regulation across alternative trading systems, it is likely that any changes will be put on ice for now.

“Realistically, I don’t think we’re going to see a lot of new regulations in the next four years,” Davies commented. “We’re more likely to see a reduction, particularly in restrictive regulation.”

As market participants continue to seek out alternatives to the lit market, new ways to avoid information leakage and access to the best liquidity available to them, the emergence and popularity of systems like private rooms will inevitably grow. Whether the industry will learn from past mistakes, keep a close eye on platform regulation and play nice is less certain.

It’s a thorny issue, but one thing’s for sure: don’t believe every rumour you hear on the street.

Powered by research

By
Nick Dunbar
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Jean-Philippe Bouchaud

Jean-Philippe Bouchaud

The world’s foremost experts in trading and market microstructure and their youthful research team have turned Capital Fund Management (CFM) into a hedge fund giant whilst employing almost no traders. Nick Dunbar spoke with CFM’s co-founder Jean-Philippe Bouchaud.

Walking through Paris’s Left Bank to the offices of Capital Fund Management, it is easy to forget one is visiting a financial institution. Students from nearby Sciences-Po throng the streets, not investment bankers, and a similar relaxed, youthful vibe pervades CFM’s offices. That is exactly how CFM co-founder and chairman Jean-Philippe Bouchaud likes it.

“Junior people have direct access to the founders of the firm,” he says. “The hierarchy is very flat, I don’t know how many firms work like this. But I think it’s very common in universities that you go and see a Nobel Prize winner that’s working in your department. He sits in his office, opens his door and discusses with you.”

The difference is that this ‘university department’ is a US$17 billion hedge fund running a portfolio of diverse, systematic strategies. With eight times leverage, holding thousands of different stocks, futures and options, with a 20-40 day median lifetime, CFM trades hundreds of billions in volume per year.

But the real surprise is that the firm employs almost no traders, relying for the most part on algos designed by its 100-strong team of researchers and 200 developers. Other than financing, CFM uses no broker algos for trade execution. It is just as well that Bouchaud, and co-founder Marc Potters, are considered to be the world’s leading experts in the theory of trading and market microstructure.

“For us, market structure is really important, because unlike other firms who go through brokers who execute their trades, all the execution is done in-house” Bouchaud says. “So we’re extremely interested in market microstructure.”

Before speaking to Bouchaud, it helps to have read his 2017 monograph ‘Trades, Quotes & Prices’ (co-authored with Julius Bonart, Jonathan Donier and Martin Gould). Packed with equations, the book embodies CFM’s guiding philosophy, which opposes the ‘efficient market’ economics dogma, according to which prices reflect new information.

“We believe that most market moves are really due to impact of others,” counters Bouchaud. Years of painstaking analysis have turned this into a money machine. “We have a saying, which is that ‘the impact of others is our alpha and vice versa’.”

Rather than the traditional buyside approach of deciding on portfolio allocation first and execution afterwards, CFM starts with the execution problem first, because its profits are so sensitive to it.

“There are really two main sources of costs,” explains Bouchaud. “One is spread. And in this case, you can try to be smart about when you trade.” With CFM’s access to exchange central limit order books, this means shadowing the market makers, and building algos that exploit patterns in their behaviour.

“Typically, there’s a game, which is pretty high frequency, although, of course, we’re not high frequency traders, we’re not market makers,” he adds.

“We’re directional in our trades, and our trades tend to be autocorrelated in time, for hours, days, sometimes weeks. But it’s still important to be high frequency, to compete with high frequency traders and see when they’re putting a spread that’s too tight or too large, and try to get this very high frequency information about when to trade: if you want to wait a few seconds for the trade to go back down, or spread to go back down, or for the price to go down, if you want to buy and so on.”

Jean-Philippe Bouchaud

But CFM has no interest in becoming a market maker itself. “They need to be first in the queue, so if they are executed, they can actually earn the spread,” notes Bouchaud. “We are not trying to earn the spread. We’re trying to save costs, which is a very different concept, and therefore, spending millions to build an infrastructure to save a little bit of cost is not worth it.”

The impact of market impact

Perhaps the core of Bouchaud’s academic research is in the elusive concept of market impact, which is a huge concern for CFM. “The second major part of our cost is impact cost, and that’s much more subtle, because this is something that’s entirely statistical,” he says. “Fifty per cent of the time you buy and the price goes down. But of course, it doesn’t mean that you don’t have impact.”

“A lot of the time brokers are given a trade, and they’re told, ‘Well, if the price goes too high, you stop trading. You don’t execute everything I want’,” Bouchaud explains. “We’re not doing that at all. We decide on the size of a trade, and we’re going to trade no matter what. Even if the price goes up, it doesn’t matter, because we know that on the other side of our portfolio there’s an asset for which the price will go down the same day, or later on, anyway.”

“In portfolios like ours, which are extremely large and that tend to turn over reasonably frequently, over a year or 10 years, we’re going to turn over quite a lot on a given stock. But also, we’re typically trading 2,000-5,000 stocks, so we’re really interested in statistical averages.”

This high volume gives CFM a unique vantage point not enjoyed by typical buyside firms. “When you average over a large number of trades, you see that on average, when you buy, you put the price up. And it’s a small effect. It’s a fraction of the volatility that happens during the same time scale during which you execute your trade. That’s why you don’t see it on a case-by-case basis. This is the impact.”

“It’s a very subtle cost, but it’s huge, so even if this is very small compared to vol, it is a substantial fraction of the cost for us, and it’s a cost that scales,” Bouchaud says, explaining how it curtails CFM’s profits. “It’s our main issue in terms of dollars extracted from the market.”

The explanation hinges upon Bouchaud’s academic research, where he is famous for his theoretical underpinning of the famous square-root law of impact versus trade size.

“The spread cost scales linearly with your volume,” he continues. “The more you trade, the more you pay proportionately, whereas impact cost scales itself as the square root of your size. So what you pay is your size times the square root of your size, but what you expect to gain is just your size.”

“This thing is bad because at some point you start losing money and that limits the capacity of funds.”

Nothing at CFM would be complete without a scientific experiment to back it up. “We’ve done a random trading experiment,” Bouchaud says. “And we see that on the time scales where we can measure impact, it doesn’t matter whether our trades are random – whether we flip coins and decide to buy or sell, or are motivated by some signal. They’re just indistinguishable.”

The subtle effect observed by Bouchaud is completely different from the complaint of information leakage made by traditional buyside traders, he points out. “It’s statistical, that there’s more buys vs sells at a given instant of time, and so the market, on average, reacts to that, but it’s not that some people detect something and spot you.”

For Bouchaud, information leakage is why he avoids bilateral trading. “This would happen on an OTC market, where you say, hey, I want to buy something, and so you’re detected. So that’s why we’re not on OTC markets or very little, because you can’t be anonymous.” CFM keeps a cap on individual stock volumes for this reason, he adds.

For similar reasons, Bouchaud distrusts dark venues. “Because it’s dark, it’s more scary because we don’t really know what’s going on there,” he explains. “I think there’s a little bit of illusion among the buy side about these venues. People who have large quantities to trade, it’s not clear that they wouldn’t be better off just trading on the lit market in a continuous way; that is, not execute their trade in a single go, but spread it out on over one week. It’s not that clear that they would get a worse deal. The problem with dark pools is that they’re dark, at least for us.”

Using lit venues allows CFM to conduct endless trading experiments, Bouchaud continues. “Doing all our execution in house, we always have different models running in parallel as a horse race,” he says. “We use different trading schedules throughout the day. Half of the pool of the stock uses one trading schedule, like front loaded in the morning, then decaying over time, or vice versa. And we compare the two. Because we’re trading so many stocks, after a few weeks or a few months, we get statistical results that make sense.”

That isn’t to say these results can always be trusted. “The delicate thing, which is the predicament of quants, is how to get rid of all sorts of biases,” he points out. “But also, for example, when do you do these randomised experiments. Are you sure that you’re really, truly random?

Jean-Philippe Bouchaud

“Suppose I use six months of data to compare two execution models. One seems to be statistically better than the other one, and we’re going to trust this result and go for it, because we know that having a true statistically significant difference in a way that would justify you publishing in a paper is close to impossible.

“There can be also changes of regime that you haven’t thought about when you were doing your experiment. For example, you’ll be happy with your models, and then suddenly there’s a change of regulation, where the spread, or a tick rule suddenly changes.”

Alpha signals

Having tested execution strategies, CFM finally comes to the area that would be considered foremost at a traditional fund – alpha generation. And here, Bouchaud is acutely aware of the in-sample bias that deludes investors into chasing spurious signals. “In terms of alpha, with longer term signals, although you spend a lot of time trying to avoid the in-sample bias, it’s extremely difficult,” he warns. “We had models which we thought were well designed that we had to turn off because they were not.”

“One reason is just pure in-sample bias, the fact that the signal is pretty weak, and you’ve over-fiddled with your parameters, and thought that you found something that actually doesn’t work,” Bouchaud explains. “There’s something else, where you’ve really done your work properly, but then a lot of people simultaneously find similar effects, or signals, and start trading them.”

This notorious problem of crowding is a double-edged sword. “It can go both ways,” Bouchaud says. “Sometimes the fact that they’ve starting trading them enhances the signal. In other cases, it makes a signal disappear or go too fast for you to trade, or increases your costs to trade the signal. Your access to the signal becomes more expensive because of what we call co-impact. You think you’re going to have an impact which is square root of your volume Q, but it’s square root of 10 times Q, because simultaneously to you, there are 10 other people trading the same Q. And so suddenly your cost is much higher.”

The problem of co-impact powers an arms race between CFM and other systematic hedge funds. “This is the reason why we and our competitors, I guess, need to hire more and more researchers because we need to innovate, get more models, understand better all these aspects to avoid in sample bias, degradation of alpha, and crowding.”

Left in CFM’s wake is a trail of discarded strategies that have been discovered and over-exploited, such as the tendency of the idiosyncratic or non-beta component of stock returns to mean-revert. “This is a well-known statistical effect, but it’s nearly gone now, in terms of alpha generation. It’s so well-known and so traded that it’s very difficult to make money on these things that become commoditised.”

On the other hand, some strategies seem to thrive when imitated, such as trend following. “Trend following is amazingly robust and it’s still performing. For example, we have a trend-following fund which made around 19% last year, and it’s been extremely good in the last 10 years.”

“There’s a portfolio effect where you need to diversify trend following on many different underlyings. And what we’re trying to tell our clients is that it’s not only trend following on the major factors, it’s also trend following on spreads, between contracts and things like that, that you need to include.”

Trading against Black-Scholes

One portion of CFM dear to the founders’ hearts is option trading, which dates back to the fund’s creation under its previous title of Science and Finance, in 1994. “The initial motivation to create Science and Finance was really option pricing and to create an option fund, which we progressively built and started trading 10 years later,” Bouchaud recalls.

After studying option pricing, he and Potters became convinced that the universally accepted formula used on Wall Street was flawed. “It is really believing that Black-Scholes is wrong and that you can do better than Black-Scholes in having a normative price to which you can compare the market price,” Bouchaud says. “Now the vol programme is diversified in all sorts of directions, but the basic initial model was really believing that you can do option pricing much better than what the market does.”

Conveniently for CFM, the terminology of option pricing helps entrench the mispricing. “When you give people the language, it’s extremely difficult to get rid of that language. What you need to do is to keep the language, but progressively try to tell people that they should not give the same meaning to the words that they were using for years. Like implied vol or delta.”

However, Bouchaud warns that this money machine is liquidity-constrained because “liquidity is scattered between different maturities and strikes”. This explains CFM’s breakdown by contract type. “It’s mostly futures and stocks, half and half and 10% in vol. It’s really hard to get big in the vol space.”

When it comes to risk, Bouchaud highlights two areas that affect CFM. One is the emergence of new systematic factors, such as during the Covid pandemic.

“There’s been a few major events that hurt CFM badly,” he recalls. “One of the recent ones is the day [9 Nov 2020] when Pfizer declared that they had a vaccine. Suddenly, there was this very strong divergence between two hemispheres of the industry, one which relies on people staying home, and one which relies on people going out. And, this thing was not really expressing any risk before the pandemic. And then became very important. If don’t have the tools to detect that, you can get caught very badly.”

The other risk is inherent in the fund’s use of leverage. “There’s less leverage on futures than stocks, but if you want to trade market-neutral portfolios of stocks then you need leverage, to enhance the volatility.” The problem comes when other hedge funds are forced to deleverage.

Jean-Philippe Bouchaud

“There are these hidden latent risks that realise themselves” Bouchaud says. “For example, the 2008 quant crunch. This is really a major concern, to try to be sure that these types of mass de-leveraging don’t affect you.”

In August 2008, CFM had a lucky escape, Bouchaud remembers. “Fortunately, we had very strong signals that were indicating that something really strange was going on in the market. And so we were able to deleverage our book before the big day.”

Harnessing technology

Artificial intelligence is one area that CFM is focusing on. Noting that the latest large language models give PhD-level responses, Bouchaud warns that widespread adoption in the investment community is inevitable. “As far as I can see, either people get crazy and use these black boxes and make really completely autonomous robots to trade, and then there’s a problem of crowding out.”

Bouchaud’s answer is to keep humans at the heart of the process. “We will use these tools, but every model, everything that’s going to go in production at the end of the day, will have to be rubber stamped by someone who puts his skin in the game. It’s not going to go out of whack, because there’s this restoring force, which is personal responsibility.”

And CFM is hiring new AI experts from academia, he adds. “We’ve just created something that we call the machine learning lab, which is going to be a kind of internal AI consultant and will be publishing papers.”

Meanwhile, CFM’s IT team turns academic-type strategy ideas into robust algos. “There’s a research code, and there’s a production code, for which the oversight needs to be much stronger, and then we need to be sure that there’s no problem,” Bouchaud explains. “We have professional coders that ensure that the whole production chain works. It’s like a factory every day, minimal level of errors. So that takes time, and a lot of people.”

“We have 200 developers, from the start we’ve seen a ratio of one researcher to two developers. IT means developers, but also data science.” Ensuring reliable sources of data to power the firm’s statistical trading models is a constant headache, Bouchaud says.

“Every day we discover a new data source somewhere. And there’s a huge business around providing data to people. The problem is that some of these data sources are extremely interesting and contain information, while others are cooked up and can’t be trusted.”

While CFM continues to use Bloomberg and other traditional sources, because others look at them, the firm’s developers are building their own data sources as well. “Scraping the web and creating your own data is very important,” Bouchaud concludes.

Jean-Philippe Bouchaud, is co-founder and chairman of Capital Fund Management (CFM), adjunct professor at École Normale Supérieure and co-director of the CFM-Imperial Institute of Quantitative Finance at Imperial College London. He is a physicist and member of the French Academy of Sciences, and in 2020 held the Bettencourt Innovation Chair at Collège de France.

 

KCx: Bringing execution technology in-house

By
GlobalTrading
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Kepler Cheuvreux UK team
Left to right: Robert Miller, Chris McConville and Bobbie Port

Global trading speaks to KCx’s Chris McConville, Bobbie Port, Robert Miller and Serge Reydellet.

Breaking barriers: the innovation that’s reshaping execution

The execution landscape is evolving rapidly, and standing still is not an option. In an industry where technology is often outsourced or built for the market conditions of the past, we chose a different path – one focused on innovation. Bringing our execution technology in-house was not just a move toward greater efficiency or control; it was a strategic decision to shape our future with clarity and intent.

With full control of our technology, we are delivering more than just better performance: we are offering a tailored, responsive, and dynamic trading experience.

Kepler Cheuvreux UK team
Left to right: Robert Miller, Chris McConville and Bobbie Port

What inspired KCx to redefine its infrastructure with in-house technology?

Chris McConville, Head of KCx: Over the last 2 years, KCx has been embarking on an ambitious journey of reinvention. Through this journey, we began to reimagine what a modern trade execution platform could look like, incorporating open-source technologies and cloud computing into the very fabric of the platform. We did not want to upgrade just one aspect of our execution suite; we wanted to improve the entire suite.

The objective was to build a bespoke execution platform geared for agility and growth; with KCx Omni, we believe we have done just that. Supported by our partners, Adaptive, we have leveraged event stream sequencer systems to build a scalable, resilient, low latency next-generation trade execution platform.

How does owning your technology translate to delivering better solutions to clients?

Bobbie Port, Head of Electronic Distribution: KCx is leveraging technology not only to enhance performance but also to drive service delivery – an aspect that is often overlooked yet crucial for client success. Technology will empower us to deliver on client requests going from two to three deliveries a year to four to five every week. Our teams sit close to the code and the client, enabling fast, informed decision-making and support. This is a more direct model, leading to less friction, faster feedback loops, and ultimately better outcomes. In an environment where commoditisation is increasing, owning our technology stack allows us to remain flexible, client-led, and quality-focused. This is where we believe long-term value is created.

Owning our technology is a key differentiator in how we support clients and deliver solutions that matter. While much of the market is focused on internalisation and cost reduction, our priority remains on what clients genuinely value: customisation, agility, and service quality. By controlling our technology stack, we are able to move quickly. We are not dependent on external vendors or delayed by legacy infrastructure. This means we can adapt in real time, whether implementing a client-driven feature, tuning an algorithm, or adjusting to market dynamics.

Customisation is another core strength. Every client has different objectives, some are focused on alpha capture, others on execution quality, footprint, or workflow integration. Owning the technology allows us to tailor our offering precisely to those needs, rather than pushing a standardised solution.

How has client feedback shaped your decision to overhaul and internalise your technology stack?

Robert Miller, Head of Market Structure: Clients are instrumental in everything we do at KCx. We know that every trading desk operates with unique workflows and objectives – as the buyside and sellside become more objectively aligned, we need to provide more than out-the-box execution. Our clients’ insights made it clear that a one-size-fits-all solution wouldn’t suffice in today’s fast changing market. Clients depend on robust performance, not just execution performance, but resilience from a technology perspective too. They need a product that can be tailored to their specific operational objectives.

This understanding drove us to redesign our technology stack, ensuring that it is both high performing and highly customisable. Bringing our technology stack in house gives us complete control over the architecture, allowing us to swiftly integrate feedback and adapt to new challenges. This could be workflow changes from our clients or market evolution. It also gives KCx more flexibility to deploy different agents within the algo functionality as execution research continues to evolve.

Our continuous feedback loop ensures that as these conditions grow, our product remains resilient and forward-thinking. This strategic overhaul not only enhances our product’s execution and performance but also positions it to meet future market demands. Ultimately, the client is at the heart of our business, and by evolving our technology in line with their needs, we are building a platform that is flexible, scalable, and ready for the future whilst maximising execution performance.

How do these upgrades future proof KCx’s execution services?

Serge Reydellet
Serge Reydellet

Serge Reydellet, Head of Quant Execution: The changes discussed so far are just the beginning; every decision has been deliberate, ensuring that the solutions we provide will be scalable and grow with KCx. KCx Omni and KCx Spark will be the foundation on which we build.

The introduction of KCx Omni, an advanced event-driven equities trading system, marks a significant evolution in how trading components interact and scale. Powered by Adaptive’s event stream sequencer, KCx Omni acts as a central nervous system, seamlessly connecting components such as the Algo Centre, OMS/EMS, Vector TCA, IOI management, portfolio optimisation, AI agents, and the Quant Data Interface (QDI).

Most importantly, this will be a fully front-to-back solution controlling everything from pre-trade, post-trade, platform analytics, and trading interfaces, alongside robust risk layers. The technology is low latency, capable of managing high throughputs which can easily be deployed onto our user interfaces to be distributed to all our teams quickly and effectively.

KCx Spark, our next-generation Smart Order Router (SOR), has been purpose-built for low latency to improve liquidity capture. Spark optimises conditional and block venue performance through Level 3 order book insights and real-time execution inputs. With dark liquidity fully transitioned to Spark in the EU – and rollout across lit venues now underway – clients benefit from significantly improved outcomes in both dark and lit execution.


www.keplercheuvreux.com

 

 

Demystifying the Dragon

By
Lucy Carter
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Chinese Market Structure

Thanks to technology and automotive innovation, the Chinese equity market is re-piquing investors’ interest – but some are concerned about opacity in the region, and restrictive algo trading practices that could make it hard for international investors to get a foot in the door.

Global Trading explores some of the most recent changes that have come into play, considering whether the Chinese equity market is as scary as people think.

Algo trading

In October 2023, China introduced a new slate of algorithmic trading rules with the goal of preventing “abnormal trading activities” and improving overall market stability. Avid support for the changes is voiced by Jacques Lemoisson, founder of Gate Capital Management, who has serious apprehensions about algorithmic trading practices globally.

“In Europe, there are too many algos,” he asserted. “They need visibility of the market to work, and exchanges in the US and Europe allow them to read the order books.

“They can also, and this is concerning from my point of view, populate order books.” He highlights the issue of shadow orders, a form of insider trading whereby investors buy or sell securities with knowledge of companies linked by economic or market factors.

“The issue is that when volatility is going mad, the algo just withdraws its orders,” he adds. “You think that you have plenty of liquidity on the book, and then when you send the order the algo withdraws on you and your order is scratched.”

As such, Lemoisson believes that China’s decision to ramp up algo surveillance was a smart move. “They understood that international – and some local – systematic and algo funds were using their visibility to play against the market.”

“Limiting order book visibility for algos was brilliant, for me. It makes it tougher for the monster firms to be efficient,” he adds. Rather than restrictive, he argues, this is an equalising change.

Jacques Lemoisson

The Shenzhen and Shanghai exchanges are described by one market participant as “difficult” when it comes to regulation. Guidance sits in a grey area, they say, without clear guidelines on what is and is not permissible.

Melody Yang, funds and regulations partner at Shanghai Yaowang Law Firm (the strategic alliance firm of law firm Simmons & Simmons in China), agrees that algo trading rules have, to date, been evolving yet with certain areas to be further clarified on. “For a long time, people have been wondering whether programme trading is legal or not in Chinese equities,” she says. “It’s fair and important for the regulators to explain what the rules are and recognising the strategy itself as legitimate.”

There is still a way to go though, she adds, with many industry players waiting for further guidance on how they can interact with the market. “Our global clients who really want to use their own algos to generate or execute strategies are going to have to wait for other parts of legislation to come into play before they can meaningfully trade the equities market systematically, for example, the rules on allowing for direct market access (DMA) which was restricted for quite some time,” she says.

Melody Yang

Under the updated regulation, algo trading firms are required to disclose a number of details about their models before being allowed to trade on the mainland. The rules are enforced through increased surveillance from onshore brokers and exchanges, including via on or offsite inspections of any algo trading parties at any time. For firms that pride themselves on their secrecy and proprietary technology, this is a lot to accept.

Those coming under the scope of these regulations are broadly defined as investors with a high degree of order placement automation, high-speed order placement, high turnover rates and those using self-developed or customised software. However, there is a flexibility to these guidelines; exchanges can determine if other firms should be subject to the rules.

Firms must provide their algo trading strategies, with an explanatory description, their trade order execution methods, the size and source of investment proceeds and their leverage ratio. They must also share their highest frequency of inputting trade orders, with a threshold set at 300 per second, and the largest number of orders they will place on a single day, with a cap of 20,000. If these limits are breached, firms could face categorisation as high frequency traders, even closer surveillance, potential disciplinary measures, and may be asked to disclose their server location, a test report and a contingency plan in case of malfunction.

By contrast, Eurex has an upper limit of 250 orders per second for each connection, with a maximum of 600 such connections permitted per firm, which in principle would allow a single firm to send up to 150,000 orders per second.

Concerns have been raised by some market participants that these rules limit firms’ access to the country, with disclosure requirements preventing them from trading to the best of their abilities. However, those familiar with the market are less worried by the changes.

One trader operating in the market argues that the changes are not as scary as they sound – and are not, as many assumed, an anti-algorithm initiative. In fact, there are several similarities between China’s new requirements and those already laid out across the US and Europe, they said.

For international investors already active in these markets, the day-to-day impact of these rules is minimal, the trader continued. The majority will not breach the execution restrictions, and the bulk of the work involved in compliance takes place at the onboarding and pre-trade stages.

With these rules, Chinese regulators are pushing for more transparency – the very thing that global players have been asking of their markets.

A source familiar with the issue explained that more concerns were raised by the sell side, citing initial “extreme concerns” about the impact the rule could have on cancellation rates. However, these algorithms have recalibrated and bounced back, they said, while quant firms are slowing to a mid-frequency pace to align with requirements.

“Some China onshore vendors have introduced controls to align with the requirements, and others were thinking of putting in a speed bump to ensure that activity doesn’t breach thresholds and to minimise administrative reporting, but I don’t think it has impacted overall business much given the current reporting scope is China onshore,” agrees Kitty Li, APAC co-head of execution services for global markets at UBS.

Contrary to public perception, the current rule doesn’t drill too deep into the details of how an algo works, Li continues. “From a broker side, we just have to give a very general description. It’s quite a common practice.”

Kitty Li

“Nevertheless, we need to monitor the implementation of the Stock Connect Northbound programme trading reporting rule closely, as it affects a much larger number of international investors accessing China through various brokers. Managing the reporting for the programme trading can be expensive.”

International investing

International investment plays a significant role in China’s efforts to boost its economy. However, accessing the country’s stock market is a more complex process than foreign investors may be used to.

There are two routes on offer for investment into China. Through the first option, the Qualified Foreign Institutional Investor (QFII) programme, international firms register for a licence to trade A-shares of Chinese stocks on the Shanghai and Shenzhen exchanges. Introduced in 2002, the initiative was instrumental in China’s economic expansion and opening up to global investors.

Since 2014, investors have also been able to access Mainland China markets through Stock Connect. Through a partnership with the Hong Kong Exchange (HKEX), clients can invest in Mainland markets without onshoring any capital. This has become an increasingly popular option since its introduction, taking the majority of international flow. According to one source familiar with the service, up to 90% of institutional clients are employing the service.

The reason for Stock Connect’s success is broadly down to convenience. Investors do not have to move their money onshore, which gives them more flexibility, can reduce latency and removes the issue of capital repatriation. “You don’t have to pre-fund, and you don’t have to dedicate yourself to one broker,” Li explains, something that QFII requires.

Although QFII offers the ability to trade a wider range of stocks and use block trading, this often isn’t enough to draw investors in.

“There are about 5,300 stocks listed in Mainland China. Stock Connect covers around 2,700, but that’s about 90% of the market cap. So for the majority of clients, that’s probably good enough,” Li comments. Quantity is less important than quality, for many investors.

Similarly, block trading is an appealing prospect – but the lack of liquidity onshore means that the chance of crossing a trade is continually dwindling.

In a potential bid for further market share, the Hong Kong Government announced in its latest budget speech that block trading would be introduced on Stock Connect within the next few years. The confirmation came as the country seeks to build on its connection with the mainland.

Market participants are confident that this will have a positive impact. “It’s an enhancement from a liquidity perspective,” Li affirms. “We often try to match up clients with buy and sell interests, but sometimes only one has it.”

“Pure equity investors are more likely to opt for Stock Connect, but we still get a lot of client enquiries about QFII,” Li says. “It gives you a lot more access if you want to cover different asset classes. You can trade commodities futures, access IPOs in China A-shares – and you can also trade equity futures, for example. That’s not available via Stock Connect.”

“There is also the option for short selling,” Yang adds. “For a lot of investment banks and hedge funds, it’s difficult to get their shorting strategy approved. “For most investors, the disadvantages of Stock Connect are tolerable at the moment, but QFII can give people an edge in areas like futures, where they can profit substantially. Overall, though, the gap between the approaches in trading equities is marginal.”

One trader notes that when volumes in China soared last October following announcements of economic stimuli, the percentage share of Stock Connect Northbound turnover showed a minimal drop. If this had been more drastic, it would have demonstrated that market activity was concentrated on onshore trading. With the figure remaining fairly stable, it can be surmised that investors are continuing to favour the HKEX investment route.

Limiting data access

On 13 May 2024, Stock Connect market data availability changed. Suddenly, real time buy and sell net data flows were no longer shown for Northbound flows.

HKEX, the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE) first referenced the measures in April. On both the Northbound and Southbound connections, the real-time available daily quote balance is only shown if it falls below 30%. In all other cases, it is listed only as ‘available’.

Northbound, real-time buy, sell and total turnover data has been removed. In its place, users can see historical daily and monthly total market turnover, number of trades, and the turnover of ETFs and the ten most active stocks. The short selling balance for individual stocks is tagged as ‘available’ unless it falls below 300,000 shares. This has cut down the amount of colour that traders can access, making the market more opaque.

For many in the industry, a reduction of data availability and the thought of a less comprehensive picture of a market is concerning. It was also an unexpected choice for Chinese regulators to make, given that foreign investment into the country was already dwindling at the time.

HKEX stated that the programme has been amended to align with Mainland A-share market practices. However, some suggest that it could have been down to a desire to downplay a sell-heavy environment and the impact of foreign investors on the market.

Those whose businesses run on providing market colour to investors will be hit hard by the move, but from a trader’s perspective it is not necessarily a major loss. Data on individual stocks, which is more commonly used to trade, is still available.

Stimulus package

The last few years have been eventful for the Chinese equity markets – and not in a good way. Expectations that the country’s manufacturing and financial status would be surpassed by India, an unrelenting real estate crisis and a stock market falling well behind global benchmarks painted a bleak landscape for the region, with rapid international outflows and a burgeoning trade war with the US only enhancing the crisis.

Last September, though, a governmental stimulus package prompted a rally in the market, with a five-year plan including monetary easing, support for the property sector and bond issuance programme to tackle local government debt encouraging reinvestment in the country.

“These were great headlines for people to look at China again. They drove a lot of volumes in Q4, and prompted record days for the market,” Li observes.

However, the excitement of high volume during this time was dampened by significant deviation in daily turnover. A trader in the market suggests that a second rally, which took place after the reveal of the DeepSeek AI model, was a better indicator of a strengthening market. Deviation was less drastic, and liquidity improvement more significant.

One source familiar with the issue agrees that the effects of the stimuli have not been immediate, arguing that they have not meaningfully or practically filtered through the market. The headlines are there, they suggest, but the degree to which investors believe their claims is less solid. While liquidity and equity conditions have improved somewhat, it is still up in the air whether the long-term effects will meet expectations.

Li sees the stimulus package as having a longer tail when it comes to market improvements. “The impact wasn’t overnight – it takes the offshore space time to understand what China is trying to do,” she says. “However, we’ve had a lot more inquiries from investors. The international money is coming back in.”

Conclusion

While there is an aura of mystery and suspicion around the region, much of this is the result of cultural and political perception rather than real market conditions. Many regulations and structural points of the Chinese equities market initially seen as obfuscatory are, in reality, closer to leading developed markets than many expect. While it is true that investing in the country’s markets requires an idiosyncratic approach, inaccessibility has been greatly overstated.

As China’s economy shows persistent signs of revitalisation, and particular sectors like technology and electric vehicles boom, this is a market that cannot be ignored.

Outsourced trading: What the buy side really thinks

By
Lucy Carter
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Outsourced Trading

While it’s not a new concept, outsourcing has been growing in popularity over recent years. The ability to delegate aspects of the investment process to a provider has freed up time, money and crucial resources for funds with increasingly stretched budgets. Like any solution, though, it’s not a magic bullet. There are times when buy-side clients see the service as a risk rather than an efficiency gain, and even some providers admit that there are times when keeping things in-house is the smarter move.

UBS’s shock exit from the outsourced trading space in March reinvigorated questions of whether outsourced trading is worth it – for both vendors and clients. Global Trading spoke to several buy-side firms, many of whom wished to be kept anonymous, about what they really thought of the practice.

Investment without trading

Handing over trading control to a third party is a complex decision to make, potentially far more serious than proponents suggest. Some also think that outsourcing trading removes an integral component of a company’s purpose, eroding the sense of a group trading strategy and ethos.

“I want traders to understand the strategy behind what we’re putting on,” explained Cathy Gibson, global head of trading at investment manager Ninety One. “The traders understand the underlying strategies behind what we’re doing,” she said. “They know if a particular portfolio manager is very price sensitive and wouldn’t want them to continue purchasing into a rising market, for example. They can add value there. You lose a lot of that if you go to an outsourced execution desk.”

Cathy Gibson

“An important part of alpha is market colour. How a market reacts when you trade holds a lot of information about the asset you trade,” affirms Eric Boess, global head of trading at Allianz Global Investors. “Our job is to send that information back to the portfolio managers who are interested in it. How can you do this with an outsourced manager, if they don’t understand the portfolio manager’s strategy in depth?”

The head of trading for a large UK asset manager added that as markets become increasingly complex, the importance of trading within the investment process will only grow in tandem. Outsourcing may be more convenient now, but it makes little sense to jettison such a critical component of a business from an efficiency or brand-reputational angle.

Kevin O’Connor, global head of portfolio solutions sales for State Street Global Markets, contends that the situation is more nuanced than outsourcing everything or nothing. “There are some traders who outsource easier trades so that they can focus on the difficult trades and there are some traders who prefer to outsource more difficult trades in instruments or in markets that they don’t trade every day,” he said.

“If a trader really feels that they can add value to the trading process while working with their trusted counterparties, then that trader is going to want to trade that instrument themselves rather than outsource to another trader. “If I was a trader who really felt that they could add value to the trading process, I wouldn’t outsource a trade if I thought I could do it better.”

All-in on efficiency

The chief operating and financial officer at a boutique New York long-short equity fund, and a Marex outsourced trading client, told Global Trading that the choice between hiring an in-house trader and going to a provider came down to two key points: time and money.

“It’s definitely cheaper for the management company to outsource the trading function than hiring someone internally,” they said.

Then there’s the geographic element. “We trade globally, and it’s helpful to have an outsourced team that sits in other parts of the world, versus one trader sitting here in New York who is sleeping while some of the markets are open,” the executive continued. “When we launched, we’d be asleep when orders we worked were completed. It wasn’t ideal.”

“Also, there’s additional complexity when trading in global markets. In the US, we can just trade in cash. It’s pretty straightforward. But in certain overseas markets, you’re required to trade on swaps. Having trades that are partly in cash, partly in swaps is cumbersome for a portfolio manager, who shouldn’t be spending their time figuring out how to enter an order on swap in one market and in cash in another.”

Having a specialist in place to handle these complexities both frees up time and increases efficiency.

A modular approach to outsourcing is also popular, by which firms can break into new asset classes without having to grow their internal team.

“At larger funds and banks that I used to work at, I saw the execution desk would cover the execution of a lot of different asset classes. They were spread very thin, and didn’t have the feel or understanding for a particular product,” Martin Bercetche, chief investment officer at emerging markets credit investment firm Frontier Road and a Marex client, recalled. Bringing in a product specialist can improve execution quality and allow investment ideas to be realised, he suggested. “I think a larger institution would probably have more incentives to adopt an outsource model.”

“Based on the results of our Outsourced Trading Survey, mid-sized firms between US$30 billion and US$50billion in AUM reported reduced transaction costs as a key cost consideration whereas larger firms reported more cost savings on infrastructure cost,” O’Connor responded. “Between execution and order management, risk management, compliance, booking and settlement systems – the infrastructure can become expensive and can eat into your investment performance,” he explained.

Gibson added that geographically outsourcing may be a good choice for larger managers – particularly as settlement cycles shrink.

“If a firm is heavily concentrated in one time zone but has a small percentage of their assets trading in another, then even for a large manager it might make sense to add an outsourced trader to the team,” she said.

This is something she has observed since the US moved to T+1. “Outsourcing FX execution increased because all of a sudden managers without trading functions in US time zones needed to cover T+1 FX at the end of the US session,” she explained. “It’s not necessarily cost efficient to open a trading desk in a region solely to cover late night FX flow.”

This is exactly the argument that outsourced trading providers use; the burden of operating a new trading desk can be reduced significantly with a little help, and might even make the less-optimised execution worth it. Big-name firms like Goldman Sachs Asset Management have outsourced elements of their trading to BNY Mellon, fuelling the pro-outsourcing crusade.

Cons

None of these statements are unexpected; outsourced trading is popular for a reason, and its successes are widely publicised. On a pure cost level, it’s indisputable that outsourcing saves money in the immediate term. However, other market participants are sceptical of the benefits to companies beyond boutiques.

The equity COO warned that bigger firms may become fragmented with an outsourced approach. “If you’re trading global markets, does that mean you’re going to hire a trader to sit in Hong Kong when you’re based in New York? Trading teams typically sit together, work together. At a certain size there might be some comfort in having an internal team.”

“There are certain advantages to having an internal trader that are difficult to achieve without them.”

One of the main drawbacks of outsourced trading is also one of its main features: the hired traders are not on the ground with the rest of the team.

“An internal trader being involved in direct communications with the various desks can build a rapport, and could get us better information than if we ask our outsourced trader to go and find out about this or that name. The relationship-building piece is intermediated with the outsourced trader,” an equity fund executive noted.

More broadly, poor communication can limit execution success. “Knowing the market view and positioning, where the sellers and buyer are, is a very important part of the investment thesis,” Bercetche told Global Trading. “If the execution desk doesn’t properly reflect that information back to the portfolio manager, back to the investor, then part of the investment decision gets lost.”

As Boess put it: “Are they working in your name or theirs? They will have a large number of clients they serve. We have good relationships with some of our larger brokers – can you have those if you’re going through an outsourcing provider?”

Regulatory consequences?

Aside from suboptimal outcomes, some are concerned that outsourced trading’s dangers could be more drastic – to the tune of regulatory breaches.

Traders need to be aware of any restrictions on what they can trade, either in line with universal regulations such as the SEC’s Regulation M, which prevents firms from participating in an IPO if they’ve shorted the stock in the five business days prior, or individual companies’ requirements.

This is usually established in the pre-trade process, but could be missed between organisations. “If our portfolio manager Bloomberg Chats the outsourced trader to trade a name that’s on our restricted list, how do we know that the outsourced trader has it on their restricted list? I don’t know if all firms and traders are completely buttoned up on this,” an equity COO noted.

“I’m not sure that the outsourced trader is necessarily incentivised or even motivated or concerned about the manager’s regulatory issues. They’re just entering orders — it’s not their responsibility,” another executive said.

Clients must govern their outsourcing providers with the same rigour that they do their own desks, one head of trading said. As such, they were sceptical of whether the savings outsourcing brings are as good as they look.

Eric Boess

“The national competent authority will look at your trades irrespective of whether they’ve been executed on a proprietary system or through an outsourced trading provider, and one of those is easier to monitor,” Boess adds.

Bercetche is less bothered about the issue. “A best execution definition is something that would have to be agreed on before outsourcing trading,” he said. “As long as the execution desk understands what that threshold is, I don’t see any regulatory issues at all.”

However, “if there were consistent issues with outsourced trading firms not being able to meet regulatory requirements, then the practice would not have grown the way that it has over the last 15 to 20 years,” argues Bobby Croswell, co-head of Americas prime brokerage sales, outsourced trading and capital introduction at Marex.

It is important to note that regulators are not focusing their efforts on these perceived problems, and that there have been no public prosecutions of providers or clients for poor outsourced trading practices. Regardless, security, compliance and trust anxieties abound.

“There are definitely some concerns about Chinese Walls that need to be checked before engaging with an outsourced trading partner,” the equity COO said, referring to information barrier protocols within organisations. “Are our orders being commingled with other clients? How do we know that the information being shared with the sell side is in our best interests? Is there any component of frontrunning?”

Gibson voiced a similar point. “Consolidating my order flow with another large asset manager’s and ending up with my trades being stuck behind theirs does not benefit my client. I don’t want my orders to be in a queue for execution.”

O’Connor counters that all trades are handed off at some point in the execution process. “At the end of the day, every trade has to go to a broker, no matter the channel. You still have to hand it over to someone, put it out into the market.”

Croswell concurs. “We’ve never had a client concerned with information leakage in terms of working with our outsourced trading desk,” he said. “Marex, as a whole, doesn’t have a large equity sales trading operation like some of our competitors. Our desk is agency only.”

However, it still all goes back to money and motivation. Outsourcing providers can, in some ways, afford to be less driven in their trading practices, less invested in their trading outcomes. Despite their assurances to the contrary, with Croswell attesting that “our interests are fully aligned with our clients,” the buy side is not yet fully trusting of their intentions.

Where’s the money?

The more popular outsourcing pricing model, according to Croswell, is the service fee model. With this approach, a commission is tacked onto a client’s rate with the street.

For prospects and clients, “we have an out-of-the-box pricing model, but it isn’t always used. It’s typically negotiated for higher volume clients and whether high- or low-touch trading is used, among other things,” Croswell explains. “Not everyone is going to get the same pricing.”

Bobby Croswell

Just as pricing is ambiguous, it’s safe to say that firms providing outsourced trading do not like to disclose their revenues. There’s also a hesitancy for providers to shout about their client wins. Many users do not wish to be identified publicly, making it difficult to gauge the true scope of the space. Although the perks of the practice are well-publicised, just how much companies are making from their lauded outsourcing services is under wraps.

What is clear is that revenues are increasing at some of the largest providers.

Northern Trust, which states that its number of outsourced trading clients has more than doubled over the past five years, saw a 12% hike in ‘trust, investment and other servicing fees’ over the year, reaching US$1,222.2 million. In the last year, Ned Group, 2X Ideas, Artemis Investment Managers, Waverton, and True Potential have all selected the provider of outsourced solutions.

Marex, which acquired TD Cowen’s prime services and outsourced trading business in December 2023, declined to provide details of its revenues in the space. The firm’s 2024 results report US$161.5 million in ‘hedging and investment solutions’ and US$695.2 million in ‘agency and execution’, up 26% and 28% respectively.

State Street’s revenues for back- and front-office ‘servicing fees’ within its investment servicing division were up just 2% YoY to U$5 billion at year-end 2024. State Street and BNY declined to specify which category outsourced trading revenue sits within, and told Global Trading that they do not disclose product revenue levels. Profits are healthy, but whether the amount of money buy-side clients are putting into these services is worth the investment is less certain.

Worth it?

With revenues rising, providers are keen to extol the virtues of their offerings – but it is clear that the industry is not yet entirely convinced. The lack of clarity around profits does little to assuage worries around trust or client care, and with market and regulatory complexities growing, outsourcing this key function could increase rather than reduce stress.

Despite concerns, as of mid-2024 at least 10% of asset managers had used outsourced trading within the last 12 months, according to a Coalition Greenwich survey. Questions around the efficacy of such solutions is not dampening buy side interest.

Some market participants are taking the solutions and finding new ways to use them: “I use outsourced trading primarily as a sounding board,” one head of trading said. “If I’m not better than what they can offer, then I have a problem structurally.” As always, the industry is finding a way to adapt burgeoning trends to their own needs.

Whether outsourced trading, in its traditional sense, is worth the cost is case-dependent. It has a place in the industry – as proven by its more than 15 years of increasing popularity – but it is far from the catch-all saviour that many claim it to be.

 

Four market microstructure papers you might have missed

By
Etienne Mercuriali
-
Market Microstructure

Global Trading examines four of the most influential trading and market microstructure papers published online in the past two months.

Does the square-root price impact law hold universally?

For years, researchers have debated whether large trades impact stock prices in a way that follows a strict universal pattern. A breakthrough study by Yuki Sato and Kiyoshi Kanazawa from Kyoto University, using eight years of Tokyo Stock Exchange (TSE) data, provides strong evidence confirming the ‘square-root law’ of price impact. This law states that trade size influences price in a predictable way—specifically, impact scales with the square root of the volume traded. While some questioned whether this scaling varies across markets, the study finds it holds consistently in Tokyo, reinforcing its universality. This has significant implications for institutional investors managing large trades.

https://arxiv.org/pdf/2411.13965

When trading one asset moves another

Iacopo Mastromatteo, CFM
Iacopo Mastromatteo, CFM.

The square-root law is relevant to trades in closely-related assets, such as futures with different maturities on the same underlying. A study by Natascha Hey (École Polytechnique), Iacopo Mastromatteo (Capital Fund Management), and Johannes Muhle-Karbe (Imperial College London) sheds light on this—a phenomenon known as ‘cross impact’. Analogous to the ‘no-arbitrage’ rule of option pricing, the authors use the absence of price manipulation in multi-asset trading to devise tractable models that can be calibrated for practical use. Using metaorder trading data from a large hedge fund, they demonstrate that cross impact follows the square-root law, showing how multiple trades can compound or offset one another. This insight is key for risk management and multi-asset execution strategies.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5046242

The theory of HFT:  when signals matter

The strategy of latency arbitrage is well known in high frequency trading, and depends on traders submitting or cancelling market orders microseconds before other orders reach a venue. Peter Bank (TU Berlin), Álvaro Cartea (Oxford), and Laura Körber (TU Berlin, Oxford) present an stochastic control model where traders use short-term signals to anticipate order flow, optimising execution strategies. Their framework accounts for the dynamic interplay between market and limit orders, showing how traders can use these signals to reduce costs and enhance performance. The findings refine the understanding of price impact and could shape next-generation algorithmic trading strategies.

https://arxiv.org/pdf/2306.00621

The rhythm of market trends

Christof Schmidhuber
Christof Schmidhuber

Adapting theories from physics is an increasingly fruitful area of microstructure research. Markets oscillate between trending and reverting behaviours, but over what timeframes? Researchers Sara A. Safari and Christof Schmidhuber (Zurich University of Applied Sciences) analyse data from minutes to centuries, finding that trends persist in the medium term but often revert before becoming statistically significant. They adapt the so-called ‘lattice gas’ model of fluid dynamics, where a network of traders forms a lattice with financial assets moving between them. The paper suggests markets operate near a critical point, balancing efficiency and volatility. Understanding these cycles is crucial for asset managers seeking to capitalise on momentum or mean reversion.

https://arxiv.org/pdf/2501.16772

Which trading & markets microstructure research is important for you as a practitioner?

Contact Etienne Mercuriali with your suggestions at emercuriali@marketsmedia.com. 

The evolution of basis trading: principles, techniques and new frontiers

By
GlobalTrading
-
The Agency Broker Hub

By Federico Bardelli and Ivan Brambilla – Market Hub, IMI Corporate & Investment Banking Division, Intesa Sanpaolo.

Introduced in the second half of the 19th century, financial derivatives represent one of the most significant innovations in the history of financial markets. Initially designed to address the growing need for hedging against price fluctuations in agricultural commodities, they have since evolved to serve speculative and arbitrage purposes, becoming essential tools for the functioning of modern markets. Among the strategies that use derivatives, basis trading holds a prominent position, due to its widespread use and the level of technical sophistication it demands.

This article examines the evolution of basis trading, exploring its principles, technical aspects, and challenges, with a particular focus on its application in the fixed income markets, where this strategy has found its most natural and productive use.

The concept of basis trading and the logic of the spread

Federico Bardelli
Federico Bardelli

The basis trade is an arbitrage strategy where a trader simultaneously takes opposing positions in a physical asset and its derivative, either going long on one and short on the other, or vice versa. The primary objective of this strategy is to exploit the price difference, known as the basis spread, between two instruments. This spread arises due to various factors such as market fluctuations, liquidity fragmentation, or macroeconomic conditions.

At the core of the basis trade is the basis spread, which can generate consistent returns while minimising directional risk. The scope of these strategies is broad, as there are numerous instruments – such as ETFs, futures, and swaps – that are correlated with the underlying physical assets. This analysis focuses specifically on basis trades involving futures contracts.

Futures contracts, the most commonly used derivative in basis trades, possess distinct characteristics that make them ideal for this strategy. These contracts are quoted with different expiration dates and can trade either above or below the spot price of the underlying asset. The futures curve typically manifests in two primary configurations:

  • Contango: This occurs when futures prices are higher than the spot price of the underlying asset. It typically happens in markets with significant holding costs, such as storage and insurance, or when there are bullish expectations about future prices.
  • Backwardation: This happens when futures prices are lower than the spot price of the underlying asset. It is commonly seen in markets where short-term demand for the asset exceeds its immediate supply. As the futures contract approaches expiration, its price generally converges with the spot price (ceteris paribus), offering an arbitrage opportunity for traders who can identify and exploit such market discrepancies.

Basis trading in fixed income: A key strategy

Ivan Brambilla
Ivan Brambilla

The fixed-income market has long been the preferred venue for implementing basis trading. Bond instruments, with their predictable cash flows and fixed maturities, offer an ideal environment for this strategy. Traders can capitalise on price differentials between bonds and their derivatives, which are quoted in key markets such as treasury futures or bund futures. A key feature of basis trading in fixed income is leverage, which is applied through two main tools:

  • Repo financing: Traders can access additional liquidity by using physical assets as collateral to enter new basis trades. Of course, leverage is only effective when the repo rate is lower than the expected return on the trade.
  • Margining in futures: When trading futures contracts, an initial margin must be deposited with the clearing house. This margin requirement enables traders to take large positions with relatively minimal capital commitment.

The combination of repo financing and margining allows traders to maximise the potential return on basis trades, though it also introduces significant market risk. Adverse market movements can quickly deplete margin deposits, potentially forcing traders to liquidate positions or meet margin calls.

Risks and operational challenges

Basis trading is undoubtedly a complex strategy that demands a thorough understanding of the markets, the mechanics of the instruments involved, and the risks associated with their use:

  • Market volatility: A sudden surge in volatility can lead to increased margin requirements for futures, compelling traders to address margin calls. Failure to meet margin calls promptly may result in the premature liquidation of positions, adversely affecting the overall portfolio.
  • Liquidity: The ability to buy or sell an asset at the desired price is crucial for the success of a basis trade. In illiquid markets, spreads tend to widen, making it more challenging to close positions effectively.
  • Macroeconomic environment: Decisions made by central banks—such as adjustments to interest rates or interventions in the bond markets—can significantly influence the prices of cash assets and futures, potentially undermining the profitability of such trades.
  • Operational risks: The selection of the underlying instruments in a basis trade demands precision and expertise. Mistakes in instrument selection or trade management can jeopardise the entire strategy. Moreover, costs associated with off-exchange trading and clearing can erode profit margins.

Despite these risks, basis trading has seen steady growth in recent years, accompanied by increased scrutiny from regulators who have tightened rules to enhance transparency in trading.

Execution and brokerage activity

In addition to the technical aspects related to strategy implementation, executing basis trades also involves a practical component, typically carried out by brokers. A broker such as Market Hub, part of Intesa Sanpaolo’s IMI Corporate & Investment Banking Division, offers execution and clearing services for basis trades on markets, where it holds clearing membership. Execution typically occurs via an off-exchange process, essential for registering the trade and informing the market. One of the two counterparties, referred to as the “initiator,” creates the block by entering all relevant trade details, including those for the derivative and cash instrument. This block is then sent to the market identification code of the counterparty, known as the “reactor,” who is responsible for verifying and confirming the trade.

Impact of the Covid-19 crisis

The Covid-19 pandemic marked an unprecedented period of stress for financial markets, which also had a significant impact on basis trading. During the March 2020 sell-off, global uncertainty and a flight to liquidity led to massive selling pressure in the markets, particularly affecting U.S. Treasuries. This crisis drove bond yields higher, bid-ask spreads wider, and margin requirements for futures to increase.

Despite these challenges, basis trading showed remarkable resilience. Several studies have indicated that the cash securities involved in basis trading strategies maintained higher levels of liquidity compared to those that were not included in such trades. This helped support market stability during a critical period.

Technological innovation and regulation

Recent technological advancements have revolutionised basis trading. The integration of sophisticated algorithms, artificial intelligence models, and big data analytics has enabled traders to identify arbitrage opportunities with increased precision and speed. Additionally, trading platforms and exchanges are working on operational solutions that minimise execution times and reduce operational risks associated with basis trading strategies and workflows. Moreover, the introduction of new products like micro-futures, as well as the expansion of asset classes to include cryptocurrencies, has provided traders with new arbitrage opportunities and increased liquidity in these market segments.

On the regulatory front, authorities have implemented stricter requirements to enhance transparency and mitigate systemic risk. Tighter margin rules, leverage limits, and reporting requirements have contributed to greater market resilience, though they have also led to a reduction in overall trading volumes.

Conclusion

Basis trading remains one of the most complex and sophisticated strategies in financial markets due to its capacity to create multiple arbitrage opportunities and manage risk effectively. Despite the operational and market risks involved, its role in institutional portfolios and trading operations continues to be crucial. Looking ahead, technological innovations and evolving regulations are expected to further redefine basis trading strategies, expanding their applications and enhancing efficiency in navigating the challenges of an ever-evolving financial landscape through the increased automation of operational processes.

Sources:

1. Daniel Barth & Jay Kahn, “Basis Trades and Treasury Market Illiquidity” (Office of Fin. Research, Brief Series No. 20-01, 2020), available at https://www.financialresearch.gov/briefs/files/OFRBr_2020_01_Basis-Trades.pdf.

2. Annette Vissing-Jorgensen, “The Treasury Market in Spring 2020 and the Response of the Federal Reserve”, (Nat’l Bureau of Econ. Research, Working Paper 29128, © 2021), available at https://www.nber.org/papers/w29128.

3. Avalos, F. and Sushko, V., “Margin leverage and vulnerabilities in US Treasury futures”, BIS Quarterly Review, Bank for International Settlements, September 2023.

4. Committee on Capital Markets Regulation, “An Overview of the Treasury Cash-Futures Basis Trade”, December 20, 2023

5. Jonathan Glicoes, Benjamin Iorio, Phillip Monin, and Lubomir Petrasek “Quantifying Treasury Cash-Futures Basis Trades” March 08, 2024.

©Markets Media Europe 2025

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KX: Transforming AI application

By
GlobalTrading
-
Ashok Reddy
Ashok Reddy

Markets Media spoke with Ashok Reddy, CEO of KX about the criticality of merging temporal intelligence with ultra-fast data processing and analytics to transform capital markets.

What key market factors are driving financial institutions to adopt AI solutions?

Ashok Reddy
Ashok Reddy

Machine learning algorithms and discriminative AI have long been mission-critical in capital markets. These firms use specialised technology, like we offer at KX, to process massive datasets, find patterns and make predictions with speed and accuracy.

Today, financial institutions are increasingly adopting AI solutions due to a convergence of market forces that demand greater speed, precision, and efficiency in a world where milliseconds can mean millions. As market velocity accelerates, and scalability and adaptability become critical qualities that move AI from experimentation into production, firms must break through what we call the ‘AI sound barrier,’ where traditional data infrastructure creates drag that prevents organisations from fully harnessing their time-sensitive data. At the same time, increasing regulatory scrutiny is pushing financial institutions to adopt AI models that are both auditable and compliant.

AI can transform financial operations by augmenting research, generating and debugging code for financial models, streamlining documentation and enhancing pattern recognition and contextual processing.

Customers increasingly value personalised services and community engagement from their financial institutions and by combining the creativity of generative AI with the precision of analytical AI, banks can deliver real-time, highly accurate insights that transform customer experiences by allowing then to refine products, optimise onboarding, and adapt services to evolving needs – ensuring relevance, precision, and personalisation at every critical moment.

Why is the “when” of data just as important as the “what” in AI-driven trading decisions, and how does this temporal dimension create competitive advantages?

In capital markets, success hinges on precise timing – understanding when a signal emerges can be the difference between a profitable trade and a missed opportunity. AI models that incorporate temporal intelligence enable firms to learn, predict and act in real time.

Temporal AI goes beyond traditional time-series AI by handling asynchronous events (not just evenly spaced time steps), causal reasoning (how past events drive future outcomes) and adaptive learning (models that evolve over time). The temporal dimension is the key to real-time intelligence, autonomous decision-making, and next-gen AI applications that will give companies the ability to act on the dynamics of time-driven data faster than anyone else.

How are the demands of temporal-aware AI reshaping technology infrastructure requirements for trading firms?

The demands of temporal-aware AI require cloud-native architectures, vector databases and high-performance data engines. High-performance time-series databases are becoming essential as they enable seamless handling of sequential market data. Firms are investing in low-latency processing engines to support real-time trading decisions while ensuring their AI models provide explainability and regulatory transparency.

Effective AI depends on accurate and complete data, as well as the latency and throughput to generate rapid insights. It’s about completeness, timeliness and efficiency – or, to put it another way, having all the data, exactly when you need it, delivered most effectively.

Beyond raw computing power, what foundational capabilities must firms establish to effectively implement AI systems that position companies to beat their competitors time and again?

Financial firms are increasingly seeking AI systems that combine structured numerical computation with unstructured data processing in real time. This reflects a shift toward more versatile, context-aware architectures, or, hybrid AI.

True numerical computation is critical, as LLMs (such as ChatGPT) rely on statistical pattern recognition rather than performing real mathematical calculations. AI models must also be capable of processing sequential time-series data to avoid misleading predictions caused by disordered inputs.

Additionally, regulatory explainability is paramount – financial institutions cannot afford to rely on opaque, black-box models that offer no visibility into their decision-making processes. Scalable, low-latency processing capabilities further ensure that AI-driven trading strategies remain competitive in fast-moving markets. KX’s software addresses these requirements by providing firms with the infrastructure necessary to make informed, high-speed financial decisions while maintaining compliance and transparency.

What are the biggest data-related challenges in applying temporal AI to real-time trading environments?

Applying temporal AI to real-time trading environments presents several challenges, mostly related to data accuracy, computational integrity, and latency constraints. AI models require precise timestamps and consistent data streams to generate reliable market insights. However, as research by Bradford Levy demonstrates, LLMs often fail at basic numerical reasoning, meaning they cannot be relied upon for financial decision-making. Additionally, commercial LLMs exhibit significant look-ahead bias, meaning their seemingly strong performance may be due to implicit knowledge of future outcomes rather than true predictive ability.

Compounding the issue, these models struggle with time-series forecasting. Since LLMs fail to retain and understand the sequential nature of data, their ability to generate reliable financial predictions is severely constrained. Financial strategies often depend on precise timing, but GenAI models fundamentally lack the temporal awareness needed to interpret long-term dependencies. To combat this, purpose-built platforms are emerging to enable more reliable, explainable outputs in regulatory environments.

KX’s high-performance analytics engine is designed to ingest, analyse, and act on time-sensitive data at unmatched speed, ensuring that trading firms maintain a real-time competitive advantage. Unlike traditional solutions that treat time as just another data point, our approach understands how data changes over time, providing crucial context for predictions and decisions.

How can organisations better structure their data architecture to support the precise timing needs of modern AI trading algorithms?

To support the precise timing needs of modern AI trading algorithms, organisations must structure their data architecture with a focus on real-time efficiency. Time-series databases provide the foundation for accurate market predictions by allowing AI models to process data in a strictly sequential manner. This requires moving beyond surface-level data accuracy to ensure that the data truly reflects the temporal nuances needed for effective decision-making. Optimising data pipelines for ultra-low latency ensures that AI-driven trading systems can react instantaneously to new information.

A hybrid AI approach – where analytical AI handles rigorous computation, and GenAI assists with research and code automation – ensures that firms benefit from both precision and adaptability. Built-in auditability and explainability are becoming foundational requirements as regulatory expectations increase in both scope and sophistication. Compliance requirements necessitate AI models that provide clear, auditable reasoning for their trading decisions. Utilising a platform that is purpose-built for these types of challenges allows organisations to manage complex financial data with the precision and speed required for success.

Looking at successful implementations, how are firms that effectively merge temporal intelligence with ultra-fast processing gaining measurable performance advantages over competitors?

Firms that effectively merge temporal intelligence with ultra-fast processing gain measurable performance advantages over their competitors in the following ways:

  • Enhanced Predictive Capabilities: By understanding temporal patterns, AI can make more accurate predictions about future events.
  • Improved Anomaly Detection: Temporal context allows AI to identify anomalies that occur over time
  • More Sophisticated Decision-Making: AI can make more nuanced and context-aware decisions by considering the temporal relationships between events.
  • Personalisation: By understanding a user’s past history, and how that history changes over time, AI can provide very highly personalised real time responses such as dynamic portfolio adjustments or real-time onboarding optimisation.

Superior trade execution timing enables them to act milliseconds ahead of the market, leading to increased profitability. Additionally, firms leveraging real-time AI are better equipped to mitigate risks by responding to market shifts as they happen rather than after the fact. The ability to provide clear, explainable decision-making also reduces regulatory risks, ensuring compliance with evolving financial regulations.

kx.com

 

Iress: Future-proofing trading desks

By
GlobalTrading
-
Kyle Marais
Kyle Marais

Kyle Marais

Global Trading spoke with Iress Product Manager – Trading, Kyle Marais, about the challenges legacy platforms pose for trading desks, and how interoperability and cloud technology are shaping the future of trading.

What challenges are capital markets and trading desks facing due to legacy platforms?

Legacy platforms were often built to support single-asset, region-specific trading. However, today’s capital markets demand multi-asset support, global reach, and the ability to adapt quickly at scale.

Disjointed workflows arise when firms operate with multiple data sources and proprietary systems that do not integrate. As a result, traders frequently switch between different interfaces, tools, and platforms to complete a single trade creating inefficiencies that legacy systems struggle to support. Limited integration with modern technology makes it difficult to onboard new trading venues, while rigid workflows restrict operational firms’ ability to adapt to market changes.

These fragmented workflows are further compounded by siloed data, further undermining operational efficiency. Critical market, order, and trade data remains scattered across disconnected systems, preventing firms from achieving a unified view across the full trading lifecycle. Legacy formats require extensive transformation, hindering data-driven decision-making and limiting the adoption of AI and analytics.

Operational costs continue to rise as aging systems demand heavy maintenance and custom integrations. Expanding into new asset classes or regions requires complex development work and manual processes, both of which slow speed to market and innovation. Legacy infrastructure also lacks the resilience to withstand market volatility, increasing the risk of downtime.

How can these barriers be overcome?

To overcome these challenges, firms need to shift away from rigid, monolithic systems and adopt flexible, integrated trading solutions. This allows them to keep pace with evolving market demands while modernising their infrastructure, without disrupting critical trading operations.

A key enabler of this transition is interoperability. Instead of undergoing full system replacements, firms can integrate modular, API-driven components that connect existing infrastructure with modern trading venues, asset classes, and tools. This incremental approach minimises disruption, increases flexibility, and allows firms to evolve their trading architecture at their own pace.

Cloud-native technology offers a solution to enabling this transformation. By moving away from on-premise infrastructure, firms can reduce hardware costs, accelerate feature deployment, and improve system resilience. Continuous updates strengthen security and functionality, while elastic infrastructure supports scale, and adaptability, promoting self-healing during periods of market stress.

Breaking down data silos is another crucial step. Cloud-enabled data hubs, open APIs, and industry standards such as FDC3 enable seamless data-sharing across trading, risk, and compliance functions. By unifying data across departments, firms can improve analytics, reporting, and decision-making. Providing a consolidated view of market and order data allows trading desks to operate more efficiently and with greater confidence in their insights.

What is Iress’s approach to supporting this evolution?

At Iress, we’re enabling seamless integration between Iress applications, third-party tools such as CRM platforms and pre- and post-trade analytics, as well as proprietary front ends and custom content. This ensures that trading desks can create a tailored desktop experience which combines multiple technologies without disruption.

Cloud technology is central to our modernisation strategy. We are transitioning our platforms to cloud-native solutions that offer zero-install, multi-OS compatibility, ensuring users can access trading tools instantly. Leveraging cloud infrastructure also enables faster expansion into new regions and seamless scalability, without the need for additional hardware.

As part of this evolution, our product suite is also advancing. Iress Pro is evolving into a modern, cloud-based platform with modular widgets and improved tooling, ensuring that traders benefit from new capabilities without disrupting existing workflows. Iress EMS is a simplified, cloud-native execution management system designed for buy-side firms, unifying the user experience across asset classes, trading venues, and workflows. Iress’ cloud-native FIX Hub enables firms to access global markets and trade multiple asset classes through a single, frictionless connection, simplifying execution and connectivity.

How do you expect the demand for interoperability to evolve?

The demand for connectivity is only going to increase as market structures evolve. The rise of 24/5 trading, T+1 settlement, and alternative trading venues signals a shift toward an always-on, globally connected market. To keep pace, trading systems and workflows will need to communicate across asset classes, geographies, and platforms. Interoperability will be the foundation that enables firms to operate in real-time, eliminating inefficiencies and ensuring smooth cross-market execution.

Cloud technology is also levelling the playing field for mid-tier and growth-focused firms. Previously, access to broker networks, liquidity providers, and global exchanges was restricted to large institutions with extensive resources. Cloud-based infrastructure is now removing those barriers, allowing mid-tier firms to integrate faster, onboard new trading venues more easily, and connect with the broader trading ecosystem without the high cost of custom development. As the need for connectivity and speed grows, cloud-technology and interoperability will be a key enabler for firms looking to compete on a global scale.

Regulatory scrutiny is another factor driving the demand for interoperable systems. Frameworks such as DORA and APRA CPS 230 are reshaping operational resilience expectations for financial institutions and their third-party providers. Firms must now implement continuous risk management, strengthen vendor oversight, and meet heightened compliance standards. Cloud adoption and interoperable architecture reduce reliance on legacy systems, supporting continuous testing, impact tolerance, and stronger governance. By enabling better system monitoring, testing, and auditing, interoperable technology will help firms meet regulatory requirements more efficiently and with greater confidence.

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