big xyt named Philip Lemmon head of TCA sales EMEA as it seeks to enhance its growth initiatives across the region. His move follows two and a half years as head of EMEA sales at ISS LiquidMetrix. Lemmon has experience working with exchanges, the sell- and buy-side, and has previously served at SteelEye, Abel Noser, Pipeline Financial Group and E*TRADE.  

Liquidnet appointed Ayesha Rasheed Boulware as senior trade coverage consultant, based in San Francisco. Boulware will provide primary account coverage for Liquidnet members, with a focus on strengthening relationships and meeting the trading needs of clients across the West Coast. She brings 25 years of industry experience to the role, having held positions at Portware, Tradebook, and Flextrade. Boulware also previously managed the West Coast region for ITG’s electronic products prior to its acquisition by Virtu.

The post People Moves Monday: SEC, big xyt, and Liquidnet appeared first on The TRADE.

Robin Mess

Commenting on the investment round, Robin Mess, chief executive of big xyt, said: “This investment is testament to big xyt’s strong reputation and commitment to innovation, unrivalled data quality, and exceptional service.   

Finch Capital’s support accelerates our product development and team growth and enables us to take our proven expertise to the next level to meet the rising demand for advanced analytics. These include the need for automation and data-driven decisions to navigate regulatory pressures and stay competitive in increasingly complex financial markets.”

The investment is earmarked to facilitate big xyt’s global expansion across Europe, the US, and APAC, as well as enhancing its product development. 

The firm added that the capital will “strengthen our position as an independent leader in AI-based data analytics for financial markets for some of the world’s leading financial institutions”.
 
Earlier this week, big xyt claimed the award for Outstanding Market Data Services Provider – Equities at The TRADE’s coveted Leaders in Trading awards. 

Aman Ghei, UK partner at Finch Capital said: “big xyt’s expertise in automating capital markets data is crucial as financial institutions face mounting competitive pressures and regulatory demands. 

“[…] big xyt’s team is uniquely positioned to dominate the market with their unmatched expertise in financial data analytics, robust tech innovation, and deep industry insights. Their strategic vision, combined with a proven track record in scaling complex, data-driven solutions, empowers them to stay ahead of evolving market demands.”

The post big xyt secures €10 million capital injection appeared first on The TRADE.

This landscape has experienced notable evolutions in recent years due to advancements in technology, increased market participation, and regulatory changes. An understanding of the current liquidity landscape allows traders to optimise their strategies, manage risks more effectively, and make informed decisions in both stable and turbulent market environments. The topic of liquidity and its shifting dynamic has been a key theme amongst the industry at various conference held over the last few months.

Read more: Combating liquidity challenges in Europe requires caution especially when considering alternative means of trading

Speaking to The TRADE about key themes in this space, Mark Montgomery, head of strategy and business development at big xyt, notes “We’ve had the change in the RTS 1 and 2 OTC trade flags. That as a comment sounds dull, but is actually significant beneath the surface; what’s actually happened is with the FIX community getting involved and the regulators listening, they’ve got rid of a lot of the noise that made-up non addressable volumes in the marketplace.

“The big thing about that is the trades that are going on off-book, off-venue, off-exchange and are not contributing to price formation – i.e. it’s noise, it’s synthetic trades or it’s not actionable or addressable – we’ve seen evidence of a big decrease in that and we’ve also seen within that, some more specific and better flagging of trades that have particular characteristics.”

Montgomery went on to add that benchmark trades, which might be a program trade benchmarked to something or a trade that was a bit vague beforehand, are experiencing more specificity around the flagging and the reduction in noise.

“One of the challenges within all of this is you can see percentages swing. Because OTC was so big, it makes the growth in some of the other mechanisms maybe look disproportionately large, but in value terms, we’re going to have to look a little bit deeper and see what it means for the subcategories,” adds Montgomery.

Electronic liquidity providers

Another key theme highlighted by Montgomery is the bilateral liquidity provision being offered by electronic liquidity providers. He notes that these providers “were previously providing liquidity to the markets in a way where they interacted with the exchanges and they were known to certain people in the community, but not everybody. However, today, we see ELPs more active and vocal about their provisions and what they offer.”

Speaking to The TRADE about the evolving nature of ELPs in relation to the buy-side, Anish Puaar, head of European equity market structure at Optiver, adds: “We’re seeing more interaction between buy-side firms and ELPs in equities, be that directly or via SIs. The liquidity on offer from ELPs is another channel that the buy-side can look to use alongside other execution options.” Optiver currently connects directly to the buy-side via execution management systems (EMS).

“As with all participants, we believe it is important for ELPs to be open and transparent about how they provide liquidity. As buy-side-to-ELP interaction grows, it’s important to make sure the nature of these interactions is well understood,” says Puaar.

Reporting

Elsewhere, Montgomery discussed the nature of reporting from ELPs in relation to the SI mechanism. “It seems like ELPs are not necessarily reporting their trades in the way that we expected, which is through the SI mechanism, because it would seem to be systematic and a risk price. But actually, they’re doing it through a waiver through off-book (on-exchange) in certain cases,” he says.

What this means is there’s an interesting shift in risk provision and risk transfer in the marketplace.

“If I’m a buy-side firm and I want to suddenly trade, rather than trading agency through a vanilla VWAP algorithm, I’m suddenly trading on risk,” adds Montgomery. “This could either be in a block or throughout the day with a risk counterparty where I’m giving away a little bit more information to somebody who can do more in terms of price movement, in terms of access to other markets and other venues with that information and with that flow.”

This could ultimately result in a shift which could be taking place away from the bank risk desks and into the ELP risk desks. Montgomery explains that that is “something that the banks will be watching very carefully, and I think it’s something that the ELPs will be looking to see how they manage that risk.”

The post Regulation and fragmentation’s influence on the current liquidity landscape appeared first on The TRADE.

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

Evolution

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

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

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

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

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

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

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

Value

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

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

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

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

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

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

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

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

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

Usage depending on asset classes

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

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

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

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

Mark Montgomery

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

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

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

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

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

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

Making trade analytics more actionable

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

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

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

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

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

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

Victoria Bryan

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

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

Collaboration is key

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

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

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

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

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

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

Kevin O’Connor

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

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

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

Moving forward

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

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

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

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

Erin Stanton

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

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

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

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

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.

The post The TRADE predictions series 2024: The future of growing datasets appeared first on The TRADE.

The new solution provides buy-side firms with an independent view of the liquidity of equity portfolios by using market data.

A dilution levy is applied by investment managers as part of their cashflow management process of pooled vehicles to ensure clients are treated fairly while also mitigating the impact of price volatility resulting from cashflow activity.

The new portfolio liquidity analysis tool from big xyt, a subset of its Open TCA solution for execution analysis, can be beneficial when sourcing datasets, which must be harvested, harmonised and processed over time to provide estimates.

“Working on this solution in partnership with Baillie Gifford has provided a new turnkey solution for the buy-side,” said Mark Montgomery, head of strategy and business development at big xyt.

“It addresses the major challenges faced by investment managers when managing portfolio liquidity, and reduces operational risk and user error with a robust yet streamlined process that automates dilution charges on a daily basis, which saves teams significant research time that can be used elsewhere.”

The solution automates the dilution application process in a way that is data-driven for existing shareholders, alongside providing a quantitative view of the application of dilution charges.

If correctly applied, this can assist investment managers with minimising the impact of trading on a fund’s performance and apply test scenarios to determine the effect of conditions with increased volatility.

Trade data is utilised to calibrate the market impact estimates to improve the model fit and accuracy of the estimates, based on realised episodes of comparable flow types.

The market data model responds to shifts in market conditions, allowing for accurate pricing in line with market conditions.

Results are provided through interactive web-based dashboards in which bespoke views can be created to meet a range of industry needs.

“The partnership with big xyt was a collaboration between our trading and operations teams,” said Adam Conn, head of trading at Baillie Gifford.

“Our aim was to create a robust dilution adjustment and a threshold process for large deals, that is in the best interests of our clients, with high regulatory standards.”

The post big xyt and Baillie Gifford partner to launch new portfolio liquidity analysis solution appeared first on The TRADE.

Next up in our introduction to the distinguished nominees for the Leaders in Trading 2023 Editors’ Choice Awards, we bring you the shortlist for the Outstanding Market Data Provider category, shining a light on those displaying recent excellence in the data sphere.

Over the last year, the market has been increasingly turning its attention to data quality and accessibility, with providers continually enhancing their offerings for clients through technological developments, new partnerships and significant investment.

Among the key players in this competitive landscape, The TRADE has selected big xyt, BMLL Technologies, Neptune Networks, and TP ICAP, Parameta Solutions for the 2023 shortlist, following various individual achievements by these vendors over the past year. 

big xyt

Market data provider big xyt has had a stellar year. Among its recent moves was the unveiling of its real-time analytics offering in October in which it leverages its own proprietary European tape. The turnkey solution provides trading firms, exchanges, dealing desks and issuers with enhanced post-trade metrics, as well as pre-trade support. The ‘first-to-market’ service is focused on a high quality, consistent and normalised dataset of European trades and EBBO benchmark prices.

In July, big xyt and the Johannesburg Stock Exchange launched a joint venture, named big xyt ecosystems, to offer data analytics to global trading venues. It offers the Trade Explorer data platform, recently launched in South Africa, to financial centres globally, offering analytics tools which allow users to understand market liquidity and flows, market share, business concentration and execution performance, alongside delivering tools for analysing trading patterns and for pre-trade decision support. Prior to this, in May, big xyt launched a new trade verification tool – an extension of its Open TCA solution – to improve ETF execution, with the move having come in response to the pre-hedging debate. It is focused on execution analytics at both the pre- and post-trade stage, with users able to submit any trade or order for the analytics returning the verification of orderbook characteristics, price reversion and execution performance instantaneously.

With Europe as its key market, big xyt has also continued to expand globally, onboarding and upgrading a number of clients. Through these relationships, the business has worked hand in hand to offer some of its clients’ functionalities through its own product suite for use by other users. A key characteristic of the independent business is its autonomy – to date the provider has not received any external investment, maintaining its stance as an entity with zero change of conflict of interest. 

BMLL Technologies

BMLL has gone from strength to strength in recent months following its Series B Funding in October 2022, raising $26 million from Nasdaq Ventures, FactSet and IQ Capital’s Growth Fund. Following this, in September of this year, Snowflake Ventures joined the Series B investment round as BMLL entered into a partnership with Snowflake. The business provides its clients – banks, brokers, asset managers, hedge funds, global exchange groups, academic institutions and regulators – immediate and flexible access to Level 3, harmonised, T+1 historical order book data and advanced pre- and post-trade analytics at scale.

The provider has continued to expand through partnerships and launches, with Magma Capital Funds; Aquis Exchange; SIX Group; Jefferies; Berenberg; FactSet; Bank of England; Financial Conduct Authority; NYU’s Quant Team; and Kepler Cheuvreux as the latest additions to its client roster. In September, trading infrastructure provider Exegy entered into an introducer partnership with BMLL Technologies to help free up time spent on data analysis, allowing for algos to be enhanced and for clients to derive alpha. BMLL has continually stressed the importance of global coverage in today’s markets and recently added Aquis data for listed companies, data from FINRA US, Cboe Global Markets Japan and CBOE Australia, the Australian Stock Exchange, Japannext Co., SGX Group, the Johannesburg Stock Exchange (JSE), and Hong Kong Stock Exchange (XHKG), as well as expanding its equities and ETF data offering to include Shenzhen Stock Exchange data.

Through recent investments BMLL has been able to expand geographically, as well enhance its data coverage and grow the team. Over the last 12 months, the team has grown by 35% in areas including: engineering, technology, sales and marketing. Recent people moves include industry veteran Rob Laible appointed as head of Americas, Jenny Chen as head of sales, Americas, and Tom Jardine as customer-facing data scientist, Americas.

Neptune Networks

Neptune has made several strides and seen significant growth in the last 12 months, working to enhance liquidity in global markets. It provides axes from top global fixed income dealers to various buy-side firms, supplying the highest quality bond pre-trade data from the sell-side via FIX. Neptune data is delivered via several workflow options, including direct to OMS and/or EMS, or via a number of APIs, covering various asset classes, including: credit, emerging markets, rates and Munis. Its network provides data from 32 global dealers via one connection and in the last 12 months, the business has developed its buy-side client remit, with more than 90 firms live on the platform, managing over $60 trillion in global assets under management.

In June, Mizuho EMEA joined the Neptune network as axe dealer for bonds, set to distribute axes for investment grade and high yield corporate credit. The following month, fixed income axe provision platform, Neptune Networks, added Lloyds Bank Corporate & Institutional Banking to its bond dealer community. Following the addition, Lloyds Bank confirmed that it would distribute axes of GBP and EUR investment grade and high yield corporate credit as well as UK gilt through Neptune. Other recent additions include Santander and Lombard Odier. Through this move, Lombard Odier has access to Neptune’s axes across rates, investment grade, high yield and emerging markets bonds. 

TP ICAP, Parameta Solutions 

Parameta Solutions is TP ICAP’s data and analytics division and provides a range of services for the market: OTC market data, benchmarks and indices, risk solutions, and regulatory solutions. In the last year its data offering has developed through product launches and updates in different areas. As of August 2023, Parameta Solutions’ revenue had increased 5% to £91 million, with an overall uplift in margin and profitability. The firm provides users with pre- and post-trade analytics, as well as unbiased OTC content and proprietary data, price discovery insights, and risk management services. Its offering also includes benchmark and indices, and the business was recently recognised as an EU benchmark administrator by ESMA, becoming the first inter-dealer broker to administer OTC benchmarks and indices across both Europe and the UK. The post-trade offering helps users control their counterparty and regulatory risks. The tools manage balance-sheet exposure and also provide compression and optimisation services.

In August 2022, the firm joined forces with PeerNova to launch ClearConsensus, a transparent high-fidelity consensus network for independent price valuation (IPV). A year on, in August this year, TP ICAP, Parameta Solutions launched a new family of interest rate volatility (IRSV) indices in a bid to provide robust and transparent daily indices in interest rates swap markets. IRSV indices, built on a theoretical foundation when measuring interest rate swap volatility, allows for a model-free measure of spot implied volatility for market participants.

The business is focused on the consolidation of Parameta Solutions companies – which is well-progressed according to the business – in a bid to develop more partnerships with third-party data providers, with a confirmed investment planned for the expansion of the business. In addition, Parameta Solutions is working in conjunction with global analytics firm Numerix to build independent fair valuation of OTC derivatives – set to launch a new valuation product in H1 2024, adding to the risk management solutions.

The post Leaders in Trading 2023: Meet the nominees for… Outstanding Market Data Provider appeared first on The TRADE.

Market data provider big xyt and the Johannesburg Stock Exchange (JSE) have announced the launch of big xyt ecosystems, a new joint venture company.

The joint venture will provide the Trade Explorer data platform, launched in South Africa earlier this year, to financial centres globally.

“We have created a new company as a joint venture because we believe that we have highly complementary capabilities,” Richard Hills, head of client engagement at big xyt, told The TRADE. 

“While big xyt brings a proven technology stack and domain expertise in the data analytics field, the JSE brings its knowledge of the needs of a financial market ecosystem and experience of providing very high service levels in the information service space.  The combination leads to a trusted partner for other exchanges seeking to acquire this capability.”

Trade Explorer allows trading venues to distribute data analytics solutions to information services clients, including trading firms, issuers and investors.

End users receive the solutions directly through web hosted services under the branding of a sponsoring venue, which the two firms state will mean rapid time to market and low cost of ownership.

Trade Explorer also offers analytics tools which allow users to understand market liquidity and flows, market share, business concentration and execution performance, alongside delivering tools for analysing trading patterns and for pre-trade decision support.

“All trading venues understand the need for a market data business adjacent to the core mission of providing high quality markets,” said Leila Fourie, chief executive of JSE Group.

“Innovative data analytics solutions form a major component of the future growth in this multi-billion dollar sector. big xyt ecosystems will empower our peers in other financial centres to develop this opportunity.”

The new joint venture will benefit from big xyt’s technology stack and experience with providing these solutions to the market, while JSE will bring proven use cases from its own marketplace as well as its peer network within the global exchange community.

“Trading venues can rapidly realise additional revenue streams by leveraging their unique data sets without making heavy investments in new technology,” said Robin Mess, chief executive and co-founder of big xyt.

“For market participants, this offers greater accessibility to data analytics for firms of all sizes as there is no longer the need to develop such capabilities in-house.”

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It allows users to rapidly verify “suspicious” trades, providing transparency and returns verification of price reversion and execution performance instantly on any trade or order.

It specifically focuses on mid-price movements, changes in spreads, and changes in volume at-touch – all both before and after a trade.

big xyt highlighted the relevance of the significant proportion of RFQ trading in Europe currently – which reached more than 50% this year – as a key driver for the tool. It said: “Trading with non-lit mechanisms (such as RFQs or Systematic Internalisers) may expose the participant to market impact and/or information leakage and thus higher trading costs”.

As a result, there is an increased need for accurate post-trade analysis to review the quality of execution and reliably understand the total cost of trades.

In addition, the need to fully understand changes in liquidity during the trading process was also emphasised, specifically criticising using the time of execution to benchmark execution performance when trading ETFs. big xyt said: “This is highly inaccurate and does not provide any information on changes of liquidity during the trading process (usually several seconds either side of the RFQ execution time) such as price movement, reversion, or the widening of spreads”.

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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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