Citi has expanded its FX markets team in Asia with the appointment of two new individuals, according to an internal memo seen by The TRADE. Anand Goyal was appointed head of FX institutional sales for Japan, Asia North, Australia and Asia South, based in Singapore. Goyal joins from JP Morgan where he had been serving as head of macro FX and real money sales for Asia Pacific. Alongside him, Hooi Wan Ng was appointed head of markets for Malaysia. She also joins from JP Morgan where she had been serving as head of local corporate sales and private side sales.

Hugh Whelan was named head of liquidity management and data strategy at SGX FX, having previously worked as head of CME Group owned EBS Direct. Specifically, London-based Whelan will be responsible for overseeing the strategic direction and growth of the liquidity provider client segment. He is also set to develop the data products within SGX FX. Whelan’s career has had a strong focus on FX markets, having previously led the launch of EBS Direct into a new bilateral FX trading venue – which is now owned by CME Group.  

Muzinich & Co appointed George Kierton as a trader based in London. He joined from Amundi, where he spent the last two and a half years, most recently as a senior fixed income trader. Elsewhere in his career, Kierton served as a fixed income trader at BMO Global Asset Management and MUFG.

The post People Moves Monday: Groupama AM, Citi, SGX FX and more… appeared first on The TRADE.

His departure comes in the wake of the new Groupama-Amundi partnership – which he has led for the last two years.

Heleine is not set to join the new offering, and instead will be moving on to embrace a new challenge according to sources familiar with the matter.

Read more: Groupama and Amundi team up to boost trading

Groupama Asset Management and Amundi Intermediation’s strategic partnership, announced in May 2024, saw the firms merge their trading capacities to enhance trading efficiencies.

Speaking at the time, Heleine explained: “With Amundi Intermediation, we share the conviction that execution is changing radically with rapid and global digitalisation. 

“[…] By making the most of advances in AI and data science, Groupama Asset Management and Amundi Intermediation aspire to define new standards of excellence in transaction execution, while strengthening the teams’ ability to respond quickly and precisely to market challenges.” 

Read more: Fireside Friday with… Groupama Asset Management’s Eric Heleine

Heleine had been with Groupama AM for 15 and a half years and has also previously worked in other buy-side roles at firms including BGC Partners and Etoile Gestion.

The post Heleine set to depart Groupama AM appeared first on The TRADE.

The move follows Groupama Asset Management and Amundi Intermediation’s strategic partnership, announced in May, which saw the firms merge their trading capacities to enhance trading efficiencies.

Both moved to Amundi on 1 September, The TRADE understands.

The Paris-based traders previously worked under head of the trading desk at Groupama AM, Eric Heleine, and will now report to Christophe Marcilloux, head of fixed income dealing at Amundi Intermédiation. 

Read more: Fireside Friday with… Groupama Asset Management’s Eric Heleine

Elsewhere in her career, Sueng – one of The TRADE’s Rising Stars in 2019 – worked in fixed income sales at both Tullet Prebon and Newedge as well as previously serving in fixed income performance analyst roles at Amundi and Societe Generale Asset Management.

Prior to joining Groupama AM in 2023 as a fixed income trader, Nakache worked in front-office roles at Agence France Trésor and Unibail-Rodamco-Westfield. He has also previously served at firms including Credit Agricole CIB, Beijaflore, and Societe Generale.

The TRADE has also learnt that the transfer of other traders is also planned to take place before the end of this year.

More to follow…

The post Groupama AM fixed income traders join Amundi appeared first on The TRADE.

Eric Heleine

The partnership will see Groupama AM combine its team of traders with Amundi Intermediation to help achieve its ambitions.

Alongside this, Groupama AM will gain access to a wider range of activity with improved negotiating power, allowing the firm to better serve its clients and optimise its performance, the pair said.

Meanwhile, Amundi Intermediation’s international teams – which consist of over 60 staff – will be strengthened by the addition of Groupama AM’s traders.

“With Amundi Intermediation, we share the conviction that execution is changing radically with rapid and global digitalisation,” said Eric Heleine, head of the intermediation desk and overlay management at Groupama Asset Management. 

“[…] By making the most of advances in AI and data science, Groupama Asset Management and Amundi Intermediation aspire to define new standards of excellence in transaction execution, while strengthening the teams’ ability to respond quickly and precisely to market challenges.”

Read more: Fireside Friday with… Groupama Asset Management’s Eric Heleine

Groupama AM will interface its existing technology architecture with Amundi Technology’s ALTO ecosystem, which is used by Amundi Intermediation teams. As a result, Groupama AM will have increased execution, innovation and investment capacity to coincide with the digital transition of its business.

“We are delighted with this partnership with Groupama AM, a leading player, which will enable us to continue to develop our capabilities and provide Groupama AM with a comprehensive, robust and innovative system to offer end investors the best possible conditions,” said Christophe Kieffer, chief executive of Amundi Intermediation.

The post Groupama and Amundi team up to boost trading appeared first on The TRADE.

It’s a combination of different aspects. On the one side we need to continue to invest in technology infrastructures to be sure to follow trends and not lag behind. Essentially, we would like to remain ahead of the curve and for that we need to be more efficient in terms of data analysis and how we store the data. If we are to be more data-centric, we need to be sure that the backbone of processes follows the cutting edge of technology in terms of data computing. This is a key component of success as we try to maintain some hybrid capabilities.

In addition, to leverage the technology provided by third-party vendors, we need to create a seamless operability between the different components of the systems to be sure that we can address both speed and efficiency. At Groupama AM, we especially want to enrich the pre-trade analytics of the traders and have always invested in this angle. We want to be sure that we have automation on one side, and on the other, the ability to face the most complex execution in parallel. Connectivity therefore is a key part of success; to imagine any enhancements on pre-trade, this is essential.

At the same time, we on the buy-side need to be sure that we are following the regulatory constraints. This year T+1 is one of the hardest topics because it concerns post-trade and therefore firms need to be sure that their overall lifecycle is taken into consideration. What is key is ensuring that we have the capacity to follow the different rates for the different asset classes. The key challenge typically is the evolution of microstructure in this way, which for us is specifically in fixed income – an important asset class for us.

As we have 70% of fixed income in assets under management, a key component therefore becomes the capacity to follow evolutions and use the right information to facilitate the smartest decisions in terms of execution, based on historical data and on real-time data, which is quite new for fixed income business. That’s why it’s interesting in this time of testing and also why technology is so very important for us to address this point.

How would you describe the dynamic and culture of Groupama AM’s trading desks? 

Since the beginning, our desks have been designed to be a mix between strong senior expertise and historical presence, and new energy. We build up the desks with senior traders, but year after year we focus on onboarding more junior people in order to provide alternative views and different manners of manipulating data, essentially mixing the new with historic processes.

This approach refreshes the minds of the teams and is  very positive – we like to have people come in from different backgrounds because it promotes positive reflection which creates efficiency. 

In addition, we ensure that the team is very comfortable in navigating data and can manipulate that data effectively. It’s important to have a high level of autonomy in order to create developments and be agile as opposed to just waiting for some big enhancement. This goes back to having dedicated people for all data topics to remain ahead of that curve in terms of market evolution.

With the ongoing technological advancements, how are the skillsets that make for a good trader evolving?

It’s important to be specific in your skillset but also be prepared to jump on new jobs. We need to have a good mix between a strong knowledge of the market and how it works, combined with some data appetites, and if you can create good partnerships that’s also key. 

These components are important because if you only have quant people, then sometimes you are missing a part of the mood or behaviour of the market. Everything is not quants, historical knowledge and awareness of best practice is important (which is why seniority also counts). 

Essentially, being able to adapt to changing conditions is very important and is why – in terms of skill-sets – the challenge is to have people liking to manipulate the system and be looking forward at what’s next. It’s important to have some value proposal to enhance the day-to-day business and understand how things are evolving.

In terms of the role of the head of trading, in the end our main job is to be sure that our traders are well-equipped to work well in the day-to-day and look at how we can address this point in order to invest correctly in the infrastructure. It’s important to listen to your teams in order to enhance those trading capabilities.

It’s also important to educate PM’s in terms of execution, execution strategy, and complexity of the market and share that between them and the traders in order to be most effective. It’s a smart combination between seniority, specific knowledge, and technology. If the level of confidence between the PM and the traders is increasing, the short-term alpha generation is improving. 

In addition, we need to be a data provider to PMs in order to add liquidity proxy in the investment decision and also continually consider the second step in the execution process. If you are successful in creating this bilateral sharing process you improve both sides and increase trust.

Looking at the rest of 2024, what changes in market structure are you most conscious of?

On our equity side we have seen some new liquidity providers emerge, meaning that we’ve seen a spike in more bilateral trades than in the past. This means that we now need to combine the multilateral trade market on a one-on-one execution point by mixing these different execution protocols into one process.

That’s why we have developed more waterfall processes to account for different content, take into consideration different execution channel streams and add more smart execution processes for low touch.

At this point we need to have more pre-trade transparency in fixed income and we need to introduce more electronic capabilities. For digitalisation in execution processes, we need to receive more streams and store these streams intelligently, especially with the combination between different execution venues. We need real partners to help the buy-side in revising and managing these developments. 

More transparency to facilitate the execution process or distribution of the execution process is key, and in the end very positive for the end user because it will reduce the implicit cost.

The post Fireside Friday with… Groupama Asset Management’s Eric Heleine appeared first on The TRADE.

The intensification of financial regulation is a fundamental trend transforming the asset management ecosystem. The planned transition to T+1 settlement in 2024 and the forthcoming revision of Mifid II are poignant examples of this. These developments highlight how regulations, even when specific to a market like the United States or the European Union, can have global repercussions, compelling actors to revise their operations to remain competitive and compliant. 

Beyond T+1 and Mifid II, the overall increase in regulatory requirements presents significant organisational and technological challenges. Players must adapt quickly, investing in advanced systems and enhancing team skills to manage increasingly complex and interconnected processes. This regulatory pressure highlights the issue of critical size. Smaller entities might find it challenging to sustain the financial and operational weight of these demands, potentially catalysing consolidations, strategic partnerships, or outsourcing initiatives for specific functions. 

All these factors create an environment where flexibility and innovation are paramount. Asset managers need to respond not just to current challenges but also anticipate future regulatory developments, requiring a long-term strategic vision and continuous adaptability.

Vikesh Patel, President at Cboe Clear Europe 

Repeated efforts by incumbent exchanges to restrict clearing competition in equities will be a dominant theme of Europe’s post-trade environment in 2024. We could see pressure being exerted to remove interoperability arrangements, as well as undermine the open access provisions that sit at the heart of Mifir – moves that would drastically impede competition. 

The benefits of competitive clearing are recognised and undisputed across the industry – from driving innovation to reducing costs, enhancing service levels, increasing transparency and accountability, and so on. Market participants have clearly been voicing their desire for competitive clearing. We also expect to see a heightened demand for the pan-European clearing model, which we pioneered in cash equities, in other asset classes. In terms of regulation, we believe focus will intensify around settlement efficiencies, anticipating challenges in aligning US and global settlement cycles. Meanwhile, the EU’s CSDR Refit, expected to be implemented next year, could impose buy-ins on UK participants, adding another layer of cost on market participants.   

Natan Tiefenbrun, president, North American and European equities, Cboe Global Markets 

2024 will be the year that the rubber hits the road for the EU’s Mifir review, as the political framework agreed in 2023 is transformed into implementable rules by ESMA. While the EU has agreed to a real-time pre- and post-trade consolidated tape (CT) for equities – the devil will be in the detail. ESMA has important decisions to make including what constitutes ‘real-time’, the workings of the selection and authorisation process (which will determine the governance and pricing to consumers), and the revenue sharing model for data contributors. Thus far, the only declared bidder is the consortia of exchanges that fought the introduction of the CT and tried to undermine its viability, so ESMA needs to encourage additional bidders. To meet consumers’ long-term needs, including for reasonably priced consolidated market data, ESMA must attract and select a provider committed to the speedy delivery and enabling broad adoption of the CT, and to a governance framework that gives consumers a voice. 

The UK’s plans for an equities tape will also become clear: the FCA recently noted overwhelming market support for it to be more ambitious than the EU’s plans, including attributed quotes, and legislation currently before the UK parliament will give the FCA powers to launch a tender process.  Another big theme will be the importance of the retail investor, and how European capital markets can grow by better serving this community. Investor protection measures such as the EU-wide ban on payment for order flow (enforced from 2026) and restrictions on CFD trading may re-shape how retail brokers handle their customer order flow, and what products they make available for trading. It is against this backdrop that Cboe launched single stock options trading on Cboe Europe Derivatives and is enhancing its proposition to retail brokers.

Stuart Lawrence, head of UK equity trading, UBS 
   
This year was a volatile year as markets adjusted to the new “normals” of the central bank policies and dealt with the continued impact of a poor liquidity landscape and ongoing geopolitical events. While the former is now better understood – with the market pricing in potential interest rate cuts next year – the others remain unknowns. 

Next year will bring us the move to T+1 in the US which has knock-on effects for the rest of world. We should gain more clarity on the timing of the UK and EU moves to T+1 settlement, and whether they will move in tandem or not. We feel that the sooner settlement dates are aligned globally the better. 

The discussion around rebundling of payments for research will continue, and we should expect consultation papers from the UK regulator to appear in Q1 and the EU later in the year. If agreed, legislative change could follow soon afterwards. General consensus is that this change will occur but it is important for us to see the details before we can assess the impact. Finally, we await the Mifid II reviews from both the EU and UK which may come next year unless there are setbacks. The details within them will improve clarity on the regulatory landscape for the future. 

Stanislav Ermilov, chief executive, Tallarium 

Emir Refit, which goes live in the EU from April, will undoubtably be an area of significant focus for participants trading over the counter (OTC) derivatives markets. To meet the spirit of the revised rules, which aim to increase harmonisation and standardisation, firms will need to put a renewed focus on data quality. While the rules are geared to simplifying reporting requirements for the post-trade, there is still a need to lay solid pre-trade data foundations for successful compliance. Having a single source of data can allow for much greater cohesion between the front- and back-office with a view to minimise risk – as it helps ensure there is a full audit trail.    

Ultimately, these trading, compliance, and operational challenges that Emir Refit shines a light on can only be overcome fully if firms have the exact same data sets across the front-, middle- and back-office. Currently, a huge amount of resource goes into collating data and verifying that data. Following that, a substantial amount of company time goes into constructing a reliable market picture, where each company replicates the exact same process in-house. This is done based on a combination of multiple sources, raw data, company front office excel and whatever other sources they can obtain. 

The post The TRADE predictions series 2024: Regulation – Mifid/Mifir  appeared first on The TRADE.

In the buy-side trading world, technological innovation is a perpetual driver of transformation, and large language models (LLMs) are emerging as key players in this ongoing evolution. These models, with their ability to analyse vast volumes of unstructured data, are opening new perspectives for understanding market dynamics. The extraction of sentiment and trend indicators from diverse sources – financial news, expert analyses, discussion forums – could revolutionise how buy-side trading desks approach investment decisions.

These real-time insights allow for quicker reactivity and adaptation to market fluctuations, providing a valuable competitive edge. Furthermore, LLMs promise to transform the execution of trading orders. By anticipating and assessing the potential market impact, these models could optimise timing and pricing strategies, thus minimising costs and maximising transaction efficiency. This ability to dynamically adapt to ever-changing market conditions signifies a significant step forward towards more strategic and thoughtful trading management. Next year could witness this revolution, where LLMs are no longer just analytical tools, but integral strategic partners in the buy-side trading world.

Sylvain Thieullent, chief executive officer, Horizon

The world of trading has become increasingly automated, relying on quantitative analysis, predictive models, and advanced algos to increase both speed and efficiency. It is without question AI is going to continue to dominate headlines in 2024. The data required to meaningfully apply AI is becoming cleaner, and the technology tools smarter, and I’d argue this marks the beginning of a completely new way to trade.

One of the use-cases set to take off next year is around liquidity management, utilising AI-based tools to seek out market inefficiencies and generate more profitable trading opportunities. Recognising when the market’s liquidity is changing is essential for traders, investors, and financial institutions, and AI can be used to enhance the way in which we identify these fluctuations. Applying this to trading strategies, market participants will have an even more optimised way of executing orders for their clients.

Lisa Schirf, global head of data and analytics, Tradeweb

Artificial Intelligence (AI) being front-of-mind for next year is no surprise, but it does continue to present significant opportunities in applying data science to develop more transparent, intelligent and efficient ways to trade. AI models are useful in looking at historical information and estimating what could happen in the future based on a set of parameters. It can synthesise all of the information that goes into an optimised execution decision by helping to predict the best possible parameters, such as timing, dealers chosen and number of dealers selected, based on a set of given requirements. We expect to see the technology increase low-touch trading, with trades being executed without human intervention. We’re also looking at the technology’s capabilities in liquidity predictions.

We expect the use-cases in electronic trading to only keep on growing next year, particularly in the management of this proliferation of available data we’re seeing, from solutions for predictive prices for dealer selection and count, to different trading protocols, to handling less liquid securities. As we see the demand from the market increase, we’re already building model-based prices with advanced AI techniques that allow us to predict the price of a security based off of public and Tradeweb proprietary data sources. For municipal bonds, this means leveraging proprietary data science to calculate both intraday and end-of-day prices for these bonds.

The post The TRADE predictions series 2024: Artificial intelligence appeared first on The TRADE.

AI has become a part of our daily lives, with platforms such as ChatGPT being increasingly used by anyone with access to a computer. Looking through the lens of fixed income, a potential new era of precision, efficiency and insight is set to dawn as traditional workflows become paired with the boundless capabilities of AI. Already, the technology has begun to redefine how the industry approaches trading strategies, risk assessment and portfolio management. 

A paradigm shift

“AI’s integration into fixed income trading is not just a mere addition; it’s a paradigm shift,” says Eric Heleine, head of buy-side trading desk at Groupama Asset Management. “By emphasising data integration, real-time analysis, predictive modelling, and advanced computational techniques, AI is setting the stage for a more informed, efficient, and profitable trading future.”

The use of AI can help traders move away from grid based and structured auto-execution rules. Instead allowing them to become more agile and data dependent when it comes to execution strategies for specific orders. 

While AI is currently being used for the automated routing of simpler trades and smaller orders, this can be taken further still. For example, to determine which broker should be used according to an order’s level of difficulty using real-time data including axes, trades, wire time behaviour and related markets.

“The next step would be broker selection: If we balance information leakage against price discovery and only ask a limited set of counterparts, the key question becomes who to ask,” says Eric Boess, global head of trading at Allianz Global Investors. “Experienced traders do all this naturally, but machines are faster in digesting data and turning it into actionable information.”

Real-time analysis in the fast-paced world of trading becomes vital. As new market data sets continue to emerge, AI is able to analyse that data, ensuring that traders are ahead of the curve and able to adjust their strategies alongside everchanging market dynamics. The use of AI allows trades to be executed autonomously while also adapting to market conditions and developing from past trading scenarios. 

“This not only streamlines the trading process but also minimises human errors, leading to more consistent and profitable outcomes,” adds Heleine. “AI’s ability to analyse past trading patterns and forecast future liquidity conditions is invaluable, especially in a market known for its occasional illiquidity. Such predictive insights ensure traders can time their trades optimally, mitigating risks and maximising returns.”

Execution

The role AI plays in fixed income is proving to be more and more impactful, especially in the execution process. AI has the ability to amplify the ways in which traders operate, allowing them to scale their processes and make complex decisions in a more informed way. The technology, which has somewhat evolved from automation in the pre-trade stage, where data is used to create rules for low-touch trading, and is shifting towards more complex high-touch trades. 

“We now have a sea of data, the question is how you use AI to deal with and manage that sea of data in intelligent ways to bring the most important things up to the top and how do you deal with attention in the sea of data,” notes Chris Bruner, chief product officer at Tradeweb.

“To execution, which is more concrete, where traders need to pay the lowest cost and have the best liqudity across a wide swathe of trading, AI is able to lower costs, scale humans and make the process much more efficient so you can do more with less and you can handle lots of market environments. Broadly, it’s going to affect pretty much every part of fixed income – it’s just a matter of how.”

AI is able to assist in decision making processes when determining the timing of a trade, what dealers to use and all the other parameters that go into making a trading decision. These tools are able to optimise the decision that traders are making, but AI is only as good as the data that goes into it. 

“These models are very good at looking at historical information with a level of statistical significance, giving an estimation of what might happen in the future based on all the different parameters that one looks at right now,” says Lisa Schirf, global head of data and Analytics at Tradeweb.

“They’re very good at doing it in a way where humans simply can’t take that much information into account. Liquidity is certainly an area that can be predicted whether it’s for security or overall market.”

Targeting liquidity is an ever-important part of a trader’s role and AI can help assist in this process. However, liquidity is subjective and can mean different things to different people. Historically, metrics have been created that are fairly linear but inherently, liquidity tends to be feature based and dynamic in markets. 

“The latest AI model approaches are really well suited to help humans understand what liquidity conditions are and how they rapidly change, and then use that information to execute within changing markets,” adds Bruner. “There is this inherent non-linear, picking up the patterns and features in large, complex multidimensional data sets, where AI can be really well suited to helping people understand liquidity.”

In fixed income, a large instrument universe with sparse data exists where one may not see much observable liquidity on a certain ISIN. However, when you start to look at a collection of instruments, axes, real quotes or trades, AI is able to provide more observable prices. 

“With some of these machine learning or AI models, you can start to use AI to help you to impute where certain pricing or where liquidity should be, based off a broader set of data,” notes Chris Murphy, chief executive of Ediphy. 

“The optimal right now is man and machine working together in tandem. We’re going to see an acceleration of people deploying machine learning algorithms to assist in that process. All of these models are only as good as the data they’re trained on. Because traditionally in fixed income, there has been a paucity of real high-quality data that you can rely on, you need to be a little bit more careful about whether the model is sitting on top of weak foundations.”

The importance of data

In fixed income, a growing number of machine learning techniques are required to help aggregate and make sense of the very broad and quite disparate set of data which exists in fixed income markets. 

What AI enables is the ability to aggregate – in real-time – data sets and to make predictions about where a bond is likely to trade and how much liquidity there is likely to be in the marketplace. AI is able to produce signals which can help inform decision points for traders, with data essentially being a vital part of the decision-making process in the execution workflow.

“What we’re able to do with AI is feed into the model a very, very broad set of data points – including data points that may not be about the specific bond that we’re trying to solve for,” says Gareth Coltman, global head of trading automation at MarketAxess. 

“The machine learning model can then iterate and select which of those data features are the most reliable signals of liquidity when they’re tested against the market. What we’re seeing today is very broad adoption of these machine learning tools, and that’s because their ability to predict reliably and consistently is really benefiting the traders that are using them.”

The role of passive inflows and ETFs

The evolution of the fixed income ETF space has brought more automated market makers into the space, due to the ETF market based around equity market structure. This has seen fixed income markets translate more of their activity in the ETF market into the single lane market, allowing the ability to stream firm pricing on single names and respond to RFQs in a more automated fashion. 

“It’s really raising the bar for all of the dealers out there to say look, if you’re not able to algorithmically price a certain part of the credit market then you’re not going to be fast enough to be able to respond to RFQ inquiries,” adds Murphy. “Someone else is going to respond quicker, with a more accurate price and your market share is going to be eroded. Via an indirect mechanism, the growth in the ETF market and growth in passive is actually changing the rules of the game and raising the table stakes for you to be in the flow credit space.”

Managing high levels of flow

In terms of helping larger firms manage flow, AI can be extremely useful in detecting outliers by monitoring previous history of how traders approach their accounts including when certain inflows come in and how much is traded. If something were to go wrong in the execution process, AI is able to notify traders that something is not right and can help stop that specific trade from executing. 

“AI is essential for this problem domain because it’s difficult to capture all possible outliers using simple rules, but AI can even spot complex outliers in seemingly conforming individual data units that collectively represent an outlier,” notes Miles Kumaresan, founder and chief executive of Wavelabs. 

However, Kumaresan re-emphasises the need for AI to be based on accurate, useful data. 

“A neural network is just a sophisticated statistical engine. Its magic comes from its ability to identify levels of abstract relationships between a set of inputs and the associated target dataset. AI’s performance is not so much defined by the large amount of data it uses, but about learning from the representative dataset. Finding the representative dataset is a challenging task and is essential for the AI’s prediction accuracy, and to avoid unwanted biases and surprises.”

For firms managing large flows, automation solutions are essential to help desks execute low touch flow. AI is able to assist traders when assessing whether resources need to go into a specific trade and whether it is something that a human trader needs to see. However, it is important that appropriate barriers are set to make sure traders are able to catch everything that needs human intervention. 

“You don’t want to have situations where a trade that needed manual attention has gone through for automation because the rules are too simplistic. You also don’t maximise your opportunities for automation using that type of rigid, parameter-based approach,” adds Coltman. 

“AI can be used to make a better, more finely tuned decision about what needs to end up with a human trader, and that really is going to allow you to maximise how much benefit you are getting from automation and able to tweak that efficiency to the maximum possible level.”

The evolving role of the trader

As with any new introduction and evolution to the trading desk, things have to adapt. AI has the potential to elevate the trading process, reduce the amount time spent on trades that could be automated and also help traders make more informed trading decisions. However, the role of the human trader remains vital, as AI has not reached a point where trading flows do not require managing and monitoring. 

Financial markets, as we see repeatedly, are very complex. Traders and market practitioners still hold an edge by being able to provide a real view on context. Although the role of the trader is shifting, AI is far from being a human replacement. 

AI requires a high degree of care when implemented. However, the benefits that AI can provide to fixed income markets should be taken into account when developing new trading strategies. 

“This shift doesn’t diminish the importance of traders but redirects their expertise,” notes Heleine. “For complex execution or bundle multi-asset execution, the trader will be equipped with richer insights and predictions. This empowers traders to make more informed decisions, rapidly respond to market shifts, and identify opportunities or threats that might have previously gone unnoticed. As AI constantly evolves, traders must continuously update their knowledge to effectively harness the latest technological advancements in the market.”

The post Artificial Intelligence in fixed income: A paradigm shift appeared first on The TRADE.

Groupama Asset Management is set to expand its use of Virtu’s Triton Valor EMS to another asset class, now encompassing fixed income trading.

The move comes off the back of continued electronification of the fixed income space, according to Eric Heleine, head of trading at Groupama AM, with the business’ workflow moving away from chat and telephone and as a result seeking a more centralised approach to execution management.

“Virtu’s Triton Valor EMS provides us with a single view that brings together all our workflows including electronic RFQs, automated execution, and negotiated trading and provides us with tools to aggregate real-time feeds, liquidity sources and valuable pre-trade analytics to make trading decisions faster and with confidence. It’s the key to success if we want to enhance the FICC market structure which has largely stayed the same for the last 50 years,” said Heleine.

Read more: Buy-side market data costs on the rise as asset managers tap fixed income, equities, and ETFs for the greatest spend

Triton Valor supports trading across: Munis, MBSs, global corporate and sovereign bonds, fixed income ETFs, EM debt, futures and CMOs, aiming to allow users to utilise a single dashboard across asset classes.

Melissa Ellis, head of workflow technology in Europe at Virtu, said: “We designed Triton Valor’s fixed income capabilities to empower traders with pre-trade transparency, operational efficiency, and control in navigating the market. These capabilities enable clients to assess individual and aggregate trade difficulty and find liquidity in a simpler and more cohesive manner–that’s where we see a key added value for a fixed income EMS.

“Our fixed income solution is differentiated by the close integration of Triton Valor’s EMS capabilities with Virtu’s advanced trading analytics–both pre-trade, post-trade, and real-time data handling. Data and analytics are key in electronic trading and especially in fixed income given the fragmented market structure and complexity of OTC data collection.” 

The post Groupama AM integrates Virtu’s EMS for fixed income appeared first on The TRADE.

According to an update on social media, Jean-Philippe Lhomme is set to join Societe Generale after spending the last three years with the asset manager.

“Friday was my last day at Groupama AM, I have been lucky to work with some outstanding colleagues during my time here, and, I can genuinely say I have learnt a lot from each one of them,” said Lhomme in his update.

“Special thanks to Eric Heleine for these 3 enriching years, that was a real pleasure. Now it’s time to start a new exciting adventure at Societe Generale as electronic sales trader.”

Lhomme joins the investment bank with an extensive equity and equity derivatives career behind him, having previously served in various trading roles across Societe Generale, BNP Paribas, Credit Suisse and futures brokerage, Newedge and Franco-German financial services firm ODDO BHF for the last 15 years.

The post Groupama execution trader departs for SocGen appeared first on The TRADE.