Laura Misher, Kat Sweeney

As part of the partnership, S&P Global bond reference data will be integrated into MarketAxess’ suite of data products, while MarketAxess CP+ real-time pricing will also be included into S&P Global’s evaluated bond pricing.

MarketAxess CP+ is a real-time bond pricing source for global credit, rates and emerging markets powered by AI and proprietary data. The source is used by clients for price discovery, transaction cost analysis, and automated trading strategies.

The combination of S&P Global’s evaluated bond pricing of over 1.2 million corporate, sovereign and municipal bonds with MarketAxess CP+ is expected to lead to more consistent pricing and greater efficiencies across the trade lifecycle including front-, middle- and back-office functions.

“We are excited about this collaboration as it connects a market-leading institutional trading platform with a leading data provider, bringing transparency and consistency in data used across the different functions of our customers,” said Laura Misher, vice president at SPGMI.

“Additionally, our combined expertise will allow us to develop solutions that will address customer challenges across the trade lifecycle.”

The data integration is expected to start in H1 of 2025, enabling the companies to differentiate their existing product offerings and innovate new solutions.

The development follows Intercontinental Exchange’s ICE Bonds and MarketAxess connecting their respective liquidity networks in a bid to improve efficiency and access to deeper liquidity in fixed income markets, announced in August.

Read more: ICE Bonds and MarketAxess connect liquidity networks to bolster bond market efficiency

“Incorporating CP+ into S&P Global evaluated bond pricing service will enable our firms to close the gaps between best execution, intraday trading decisions and end-of-day valuation,” said Kat Sweeney, global head of data and ETF solutions at MarketAxess.

“We are thrilled to be working with S&P Global Market Intelligence, an innovator across the entire fixed income ecosystem, to further our common goal of bringing more transparency to the fixed income cash and ETF markets.”

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The QX digital platform from QUODD features security look-up and query capabilities using predefined or custom templates, which claim to shorten mid- to back-office workflows down to minutes. 

According to the firm, the digital experience provides the same security pricing, reference data, and analytics across all asset classes that QUODD’s end of day service provides.

S&P Global Market Intelligence provides the QX digital platform independent pricing and liquidity data for bonds to support back-office processing.

S&P Global Market Intelligence’s fixed income evaluation analysts aggregate transaction data to generate pricing content, ranging across nearly three million corporate and sovereign bonds, municipal bonds, and securitised products.

“With S&P Global Market Intelligence’s bond data on QUODD’S QX digital platform, we are empowering institutional customers to harness data on demand, setting a new standard for agility and client-centric products,” said Bob Ward, chief executive of QUODD.

“Our focus on the mid- to back-office user allows institutions flexibility within their organisation to maximise their market data spend by providing user specific information to more of their employees at a fraction of their current spend without sacrificing quality and improving workflow.” 

S&P Global Market Intelligence’s bond pricing and reference data, as well as the global equities and funds via QUODD, is now integrated within the QX digital platform.

The integration will provide a display experience alongside a connection to downstream wealth management users for the integration of daily pricing, reference data, corporate actions, among others.

The data usage entitlements for each tailored workflow are automated by the platform, helping institutions optimise their market data consumption.
 
“Offering our bond data along with QUODD’s equity data sets [means] we can deliver an integrated experience and tooling requirements across key asset classes that minimises switching costs and improves workflow efficiencies,” said Tasha Gonska, head of pricing valuations and reference data at S&P Global Market Intelligence.

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

Evolution

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

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

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

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

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

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

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

Value

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

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

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

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

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

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

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

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

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

Usage depending on asset classes

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

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

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

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

Mark Montgomery

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

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

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

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

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

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

Making trade analytics more actionable

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

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

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

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

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

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

Victoria Bryan

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

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

Collaboration is key

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

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

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

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

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

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

Kevin O’Connor

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

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

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

Moving forward

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

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

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

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

Erin Stanton

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

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

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

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There is an incredible, unquenchable thirst for data and trading performance numbers that is growing and growing. Some of this has been regulatory driven of course but there remains the quest – and rightly so – to utilise TCA for actionable insight and additional alpha and the users are getting smarter and smarter in how they approach this.   

Investment firms have become a lot more joined up around TCA certainly in the last 10 years and I mean this in terms of internal interactions around the TCA numbers. The trading desk and the compliance teams form a feedback loop and dovetail a lot better than they used to around TCA. They operate together and joined up as opposed to separate business functions looking at and for entirely different data points/benchmarks within TCA. Again, the same with management and the TCA stakeholders, there seems to be more synchronisation around the analysis internally than there ever was. 

The way firms trade now differs whether that be by algo usage, venue interactions, strategy attached to orders and the TCA has had to evolve with these changes, allowing the user to maximise the benefit from the solution and meeting business needs. Firms are a lot better at tagging orders, providing strategy and algo fields whereas eight years ago, it was sometimes a challenge to get Fix Tag 30 (Last Market) which provides the venue the fill was carried out on. 

How does the use of TCA differ between various asset classes?   

We have seen the expansion of the multi-asset class TCA driven by Mifid II. If you would have suggested fixed income TCA 15 years ago, people would have looked at you in disbelief. Now it’s a mainstream product being used globally by investment firms. The same applies for OTC derivatives. Again, firms are using solutions to meet their requirements in this asset class.   

There is a large demand for multi-asset TCA, and a lot of this is driven by Mifid II and the changes in best execution requirements. FX and equity TCA are now mature products within the TCA suite and people are familiar with what can be done on these asset classes. It’s the newer asset classes that are seeing the most demand, for example bonds, CDS and OTC derivatives. Investment firms still need to provide a proof of best execution across hard to value assets using either an in-house or vendor solution. The approach to these asset classes is very different – it has to be. Each asset class has to be taken on its own merits, as they have unique market microstructures and nuances which have to be taken into account. 

The limitations of the data in asset classes like CDS for example, leaves the user across the board with a fairly simplistic analysis. That’s not to say that won’t change going forwards with improvements to data. Someone running granular analysis on equity TCA in their approach will differ drastically with what can be done for example on an OTC derivative. We see much more of a compliance use case in these asset classes as opposed to the preferred actionable insight approach. 

How can the use of TCA be improved/what are the key areas for growth linked to TCA? 

 Data quality is probably the largest most important piece within TCA, whether that be the client data or the benchmark data that the client data is measured against. Data quality has improved significantly over recent years. If you go back 15 years, orders would be placed over the phone, with no accurate timestamps, and very little transparency. Today, multi-asset orders are feeding through electronic platforms, in some cases with millisecond timestamp precision. This has led to an improved set of execution data for TCA purposes. The buy-side have also been good at pushing the sell-side to provide the necessary data points to enable them to run the analytics they want to see. Transparency has improved greatly, and this will continue to improve.   

Analytics are becoming more sophisticated and the thirst to measure execution quality with new benchmarks and metrics is growing all the time. This is particularly prevalent in the newer asset classes people are starting to analyse. I think players in the TCA space who don’t acknowledge the part AI will play in TCA could potentially end up losing in the long run. I do think AI in the electronic trading space will create clear winners and losers. There will be less human interaction in the execution process as intelligence evolves. It’s inevitable. 

 AI does exist today for TCA; there are offerings that can look at an order from a pre-trade perspective and ascertain the optimal approach to execute, looking at historical data, patterns in momentum, liquidity, volatility, news stories, etc. The machine can make these decisions in seconds, whereas a human would have to spend a fair amount of time collating all this information. As times, data, technology and regulations change, so will TCA. AI will play a part in an intelligent, efficient execution process across all assets, and I see this as a growth area for TCA. 

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Data analytics in financial markets has reached the point where clients have access to complete global data sets, and we see 2024 as the year where this will drive change in how counterparties interact and in automating trading workflows. Firstly, with more data freely available in data networks it will become easier for buy- and sell-side firms to interact and collaborate, using data to make decisions about who they trade with and how they execute trades most effectively and profitably. Trading data made available via APIs informs trading decision making, and trading desks are starting to automate their workflows using the information contained in the data sets.

In 2024, we to see an increased use of trading algos in FX. As the use of data increases, we also see increased demand for data networks supporting multiple asset classes. Tradefeedr’s initial focus was FX, and in response to client requests we will launch equities and futures in 2024.

David Taylor, chief executive officer, Exegy

In 2022, we conducted a survey of executives in principal trading, brokerage, and asset management firms to quantify demand for buying predictive signals and content from third-party providers (like us) as a supplement to internal development. Around 10% of firms were engaged with third-party providers, 20% were actively evaluating third-party offerings, and 40% predicted that an engagement with a third-party provider was likely in the next one to two years. This aligned with other industry analysts who projected the alternative data market to grow at over 50% CAGR over the next five years. 

As the table stakes continue to rise to be competitive in electronic trading globally, firms will expand their sourcing and integration of solutions from trusted third-party experts. Increasingly, this will include AI technology, predictive signals, and components of quantitative strategies, as firms are forced to rethink the boundaries of their own expertise and drivers of their sustained alpha.

Robin Mess, chief executive officer and co-founder, big xyt

In recent years, navigating the European market has proven to be a formidable challenge, marked by growing competition, additional legislations and venues. The industry shares the hope that policymakers will transform the entire region into a more alluring investment hub, fostering consistency over internal European competition.

Propelled by innovation and technology, 2024 will witness exchanges and venue operators enhancing their appeal to market participants through the introduction of novel or revised mechanisms, whilst liquidity providers increasingly engage with buy-side firms. Asset managers are anticipated to spearhead and embrace emerging trends, particularly the rise of active ETFs along with ETF-specific execution algorithms. Across all asset classes, the significance of execution analytics, pre-trade estimations, and the automation of processes such as swing pricing will be more pronounced than ever.

As the trading industry increasingly becomes data-centric, its pivotal role in driving and implementing long-term initiatives will include actively contributing to the realisation of the consolidated tape and transitioning towards T+1 settlement. The year will also bring more clarity about use cases leveraging generative AI and digital assets.

Kamala Kannan, director, corporate actions, S&P Global Market Intelligence

Trade data between parties has always been undisclosed, with anonymity maintained throughout the trade life cycle leading to difficulties in tracking the end-to-end trade movement, eventually leading to trade matching or settlement failures. Considering recent developments around Unique Transaction Identifier (UTI) and fintech firms offering collaborating solutions, remaining undisclosed will no longer be possible, as further data sharing platforms emerge focused on marrying both sides of the transactions and provide a unified view to the final consumer.

As participants openly share their data, platforms can link entire settlement parties involved in the trade life cycle and provide end-to-end transaction visibility with the transaction status (both buy- and sell-side), discrepancy details etc., using a common reference like UTI or other trade parameters. When settlement information is shared reciprocally between counterparties, it will enable firms to identify and correct the inefficiencies at early stages of the trade and prevent failure in matching or settlement, which will be crucial in T+1 environments with shortened settlement cycles.

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UK-based fixed income investment banking boutique KNG Securities appointed LATAM specialist Chrisitian Laloe as head of debt capital markets and special situations in Latin America. He joined from Globex Advisory, where he advised Brazilian corporates on solutions to optimise their capital structure. Earlier in his career, Laloe served within BTC Pactual’s proprietary trading desk. According to the boutique bank, Laloe’s experience in the LATAM region will be important for the firm’s growth and its strategy to expand in emerging markets.

Man Group’s current chief executive officer Luke Ellis has announced his retirement, with Robyn Grew set to replace him effective from 1 September 2023. Grew currently serves as president of Man Group and a member of the senior executive committee, based in the US. She has spent the last 14 years at Man Group, having previously managed the solutions business, overseen trading and execution, alongside serving as group chief operating officer, head of ESA and general counsel. Before joining Man Group, Grew held senior positions at investment banks Barclays Capital and Lehman Brothers, as well as at LIFFE which has since been renamed as ICE Futures Europe. As part of the new role, Grew will relocate to the UK while still continuing to spend a large proportion of her time in the US, given the firm’s presence there. Ellis will continue to serve as chief executive and remain an executive director of Man Group until 1 September 2023 to allow for an orderly transition and oversight of the firm’s 2023 interim results.

Mediobanca appointed Mario Crovato as its new co-head of fixed income sales. Crovato joined the bank from UniCredit, where he spent the last nine years. Most recently, he served as head of markets for the UK, US and Asia – after previously serving as co-head of markets for the same regions. Elsewhere in his tenure at UniCredit, Crovato spent two years as head of markets US, based in New York. Prior to that, he was global head of rates sales, based in Milan. Crovato initially joined UniCredit as managing director, head of fixed income and credit sales for Italy. Before joining UniCredit, he spent several years at Barclays, Merrill Lynch, Deutsche Bank and Banca IMI. Earlier in his career, Crovato held fixed income sales positions at Rasfin Sim and Banca Commerciale Italiana.

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

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

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

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

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

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

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

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

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

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

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

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European credit, covered, sovereign, supranational and agency (SSA) bonds are currently included in the product scope. 

Tradeweb’s integration of S&P Global Market Intelligence’s digital primary market platform, InvestorAccess, will provide its clients with electronic access to new deals alongside the ability to manage orders in the primary market via the Tradeweb platform.

“Launching a single consolidated workflow for primary and secondary markets is a natural evolution for our European fixed income offering,” said Enrico Bruni, head of Europe and Asia business at Tradeweb.

“Our collaboration with S&P Global Market Intelligence will accelerate the automation of manual steps across primary workflows for our mutual clients, resulting in a more immediate and seamless transition from primary issuance through to secondary trading.” 

InvestorAccess seeks to improve the issuance of new deals through the automation of typically manual processes for accessing deal terms in addition to communicating order and allocations.

By integrating InvestorAccess into Tradeweb’s European fixed income marketplace, clients of the latter will be able to leverage a full suite of electronic trading tools both during and after issuance.

Tradeweb stated that the faster set up process of newly-issued bonds means that clients can now service their early secondary execution needs much more quickly and efficiently.

In addition, through the use of S&P Global Market Intelligence’s deal services API, Tradeweb will be able to process all the syndicate desk’s structured deal terms, resulting in a quicker set up of securities and faster access to electronic secondary liquidity via Tradeweb.

“Buy-side traders using Tradeweb are now able to send orders electronically into the orderbook being managed by the syndicate banks and, importantly, receive allocations electronically back into Tradeweb for onward processing in their order management system (OMS), significantly streamlining the transition from primary to secondary,” said Chris Sztam, head of global markets group at S&P Global Market Intelligence.

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