The European Securities and Markets Authority (ESMA), the EU’s securities markets regulator, has updated its Q&As on Mifid II and Mifir market structure to provide more detail on various elements of algorithmic trading, particularly around the issues of automated order management and third party systems.  

Clarified in mid-July, the first update confirms that orders executed through trading functionalities which offer automated managing of the order do indeed qualify as algorithmic trading.  

As specified in Article 4 of Mifid II, ‘algorithmic trading’ means “trading in financial instruments where a computer algorithm automatically determines individual parameters of orders such as whether to initiate the order, the timing, price or quantity of the order or how to manage the order after its submission, with limited or no human intervention.”  

That means that orders which are executed through functionalities which (additionally to routing orders to trading venues) offer automated managing of the order (e.g. automatically redirecting unexecuted portions of such orders to other venues or slicing orders prior to execution) do fall under the scope of the Mifid II definition.  

“Such functionalities differ from automated order routing systems, as the latter merely determine the trading venue (or trading venues) to which the order has to be sent without changing any parameter of the order,” explains ESMA. “On the contrary, algorithmic trading encompasses both the automatic generation of orders and the optimisation of order-execution processes (e.g. slicing of orders) by automated means.” 

“Firms trading through these functionalities should be considered as engaged in algorithmic trading and must therefore comply with the relevant regulatory requirements.”

The guidance confirms that firms trading through these functionalities should be considered as engaged in algorithmic trading and must therefore comply with the relevant regulatory requirements (found in Article 17 of Mifid II and RTS 6).  

In addition, the Q&As also clarify how firms should ensure compliance when using third party systems which offer algorithmic trading functionalities.  

“When firms use third party systems offering algorithmic trading functionalities, they are ultimately responsible for compliance with the relevant requirements,” confirms ESMA.  

“However, lacking direct control over the system, its operation and the algorithms deployed, these firms might not be materially able to ensure that all requirements are met.”  

In these instances, firms are allowed to ensure compliance with any technical requirements that can’t otherwise be met, through contractual arrangements with the system provider.  

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Speaking to The TRADE, Peter Ward, global head of futures and options electronic execution at JP Morgan, explains that while the volatility contributed to recent growth, adoption of futures algo trading has picked up pace with clients significantly in the last few years. 

Since 2016, futures volumes traded via algos at JP Morgan has increased 40% year-on-year. In fact, algos now comprise of almost 20% of the bank’s total futures trading flow, up significantly from roughly 4-5% in 2016 and 2017, figures seen by The TRADE have revealed.

The period of intense volatility in 2020 due to the global pandemic played a key role in the cumulative buy-side adoption of futures algos as traders became more accustomed to on-screen execution and liquidity.

“When liquidity is harder to source and there is more volatility, execution performance becomes challenged,” Ward explains. “Clients are driven to look at the problem areas in executions and that’s when we consult with them to figure out ways to bring in that performance. Maybe they should consider trading at higher volume at the open or close, or perhaps sitting out the first five minutes on the cash open because of the noise. All of that we can customise for them.

“I think the more challenges clients see in execution, the more opportunity there is for us to come in and help them, and the solution is increasingly the customised algorithm.”

Customised algorithms have become particularly popular with traders in 2020 and in recent years. Volumes on customised algos at JP Morgan have roughly tripled in each of the past three years, alongside a 21% increase in the number of custom algos in 2020 to almost 50 customisations, up from close to zero in 2017.

 The bank’s flagship liquidity-seeking algorithm, known as Aqua, is the most common foundation for modified client parameters and customisations. A classic example of customisation is where a client wants to follow a particular trading pattern but then switch urgency or strategy based upon predefined triggers.

JP Morgan rebuilt its algo platform around five years ago to provide the buy-side with more choice about the parameters they can set on their side for algorithms, and there are further customisations that the bank’s electronic traders can configure on behalf of clients. Ward adds this has allowed his team to have a “richer” dialogue with clients and demand is clearly there.

“There has always been demand for customised algos, even 10 years ago there was a lot of demand,” he says. “We just didn’t have a scalable way back then to adapt an algo to what a client really wanted. The reason for that is when a client wants something different, we needed developers to code that and then release it for implementation in the client’s platform, which takes a lot of time.” 

Reinforcement learning

The Aqua algorithm has been a particular area of focus for JP Morgan recently. It uses a technology referred to as reinforcement learning to create advanced signals on order routing and placement.

With reinforcement learning, which is a form of machine learning, the algorithm essentially learns from itself over time by looking back at previous signals that it has generated and evaluates performance. The signals will dictate whether the algo crosses the market or stays passive.

Reinforcement learning technology was first applied to a recently launched model of Aqua that is focused on navigating quarterly roll dates when futures contracts expire. It can be a high-volume period and volatile time for traders as everybody is typically rolling in the same week to the next expiration date. In recent years, this activity has evolved from manual, voice-based trading to more electronic, low-touch trading.

“Previously, a lot of this business was executed through voice desks and one reason for that was because trading systems out there couldn’t handle multi-leg products,” he says. “As those systems have been developed in the last few years, we found more of that activity moving to electronic channels.

“A lot of volume goes through on calendar rolls and the challenge is around optimising that experience for the clients rather than imposing a model of trading without looking at the particular client objective.”

In response to the trend and client demand, JP Morgan developed a model of its Aqua strategy, known as the Roll Algo, which went live not long ago for the most recent US treasury roll in February. It has been especially popular with buy-side traders, according to Ward.

“The Roll Algo model focuses on maximising liquidity and pricing opportunities by using signals that help it understand when to cross the spread. It’s the most important area we are working on and has peaked the greatest interest from clients.

“It performed really well in February and there was a lot of client use in that period. With that, the algo learned a lot along the way so we can expect the performance in the next quarter’s roll to be improved.”

The Roll Algo is not the only new addition to JP Morgan’s new strategy line-up. Advanced strategies like Target to trade around the cash or futures close, Multi Leg Strategy for trading multiple instruments at the same time across futures and US treasuries, and options algos have also been developed by the bank.

Volumes in options on futures surged in 2020 as trading floors at major derivatives exchanges like CME that facilitate options trading were forced to shut down. As a result, liquidity shifted to low-touch and electronic channels and JP Morgan’s clients began to ask more questions about trading options through algorithms.

“Options on futures volumes have seen significant growth in the industry over the past few years and 2020 was a breakout year for liquidity on-screen,” Ward adds.

“With that said there are still challenges and nuances to trading them and that’s where we see opportunities to innovate and help our clients with their execution. This can be through simpler Peg and Cross type strategies and ultimately more targeted strategies using a delta or volatility reference.”

JP Morgan expects buy-side adoption of futures algo trading to continue increasing in the near future, having been driven by ongoing market developments and trends over the past few years.

Explicit regulatory requirements on best execution and growing appetite among the buy-side to address challenges in futures and options market structure have been instrumental in the growth of this trend. Best execution essentially forces traders to establish benchmarks to measure performance and trading through algorithms can provide an effective way to do this.

New products have also entered the market where liquidity is shared on multiple markets, which presents challenges in trading those products. Nifty derivatives, for example, are now tradeable in both Singapore and India after the Singapore Exchange (SGX) and India’s National Stock Exchange ended a two-year dispute which put SGX’s futures into question.

Other developments such as extended hours in futures markets also means there are now more hours to trade what is often the same amount of volume. Add periods of decreased liquidity and increased volatility to the mix, traders have progressively sought algorithmic strategies and automated solutions for consistent execution in volatile products, and when targeting cash settlement periods, for example. 

It’s not just JP Morgan that is doubling down on efforts in futures algo trading. In January, rival investment bank Citi rolled out a suite of execution algorithms, including its flagship Arrival strategy, for futures markets across all major exchanges in the US, Europe, and Asia Pacific.

In contrast to JP Morgan, the electronic traders at Citi handle all of the algo customisations on behalf of clients. Head of EMEA futures electronic execution at Citi, Gordon Ball, said at the time clients don’t want to enter numerous parameters to execute an order. He added: “the complexity of operating an intelligent algorithm and fine-tuning customisations sits with us, so our clients can focus on their overall investment and trading objectives”.

Elsewhere, a start-up founded by former global head of trading at AQR Capital Management, Hitesh Mittal, launched its own suite of execution algorithms in early 2020 that aims to reduce costs for the buy-side with customised and high-performance strategies. In December, BestEx Research secured $5 million in funding as it prepares to roll out its algos in futures markets.

Amid the arms race in this space, JP Morgan’s Ward predicts the pace of fixed income futures algo trading adoption, particularly customised algos, will continue apace in 2021. It remains a significant focus at JP Morgan as different buy-side clients are also now using algorithms to trade futures.

In the past few years, the type of buy-side client seeking algorithmic execution has shifted from being a relatively small number of large hedge fund clients to the more traditional managers, including pension funds, asset managers and insurance companies.

“Five years ago, there were pockets of interest in executing this way, depending on the specific trader or firm’s appetite. It’s now become far more mainstream, driven by broader electronification in fixed income markets as well more investment firms adopting more explicit execution benchmarks,” Ward concludes.

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The report stated that during interviews with market participants, several referred to the fact that visible depth in public order books is lower nowadays due to increased automation of FX trading and the use of execution algorithms.

It added that there is evidence that FX algorithms contribute to thinner order books as they slice orders and spread them over time, which has reduced the need to provide large amounts of liquidity.

With less large limit orders to buffer market shocks, a thinner order book leads to a lower ability of the market to absorb shocks in periods of stress from news or ‘fat finger’ trades, the report explained.

However, as the order book is often replenished quickly, market functioning as a result of thinner order books is not as heavily impacted. Although in the long-term and under certain market conditions, price discovery could be hindered.

“Although visible liquidity is likely to be thinner due to the slicing of orders into small pieces, market resilience is not affected as long as the order book is replenished sufficiently fast,” said the report. “Over time, however, by reinforcing the trend towards smaller order sizes and internalisation, execution algorithms could contribute to a reduction in visibility of depth and turnover on primary markets, which could, in the extreme, hamper price discovery and market functioning.”

Generally, the report from BIS found FX execution algorithms are beneficial to market participants in terms of navigating the fragmented landscape, with most providers surveyed providing access to more than 10 liquidity pools via their algo trading services. 

FX algos can route orders to the best available source of liquidity making them an effective tool to help match diverse trading interests.

They can also be used to construct an aggregate order book by combining information from their own transactions and market data from various trading venues for an aggregated picture of prevailing market conditions that can inform execution decisions.

This aggregated view provides traders with estimates on transaction volumes in near real-time that otherwise would only become available with long lags.

“This study clearly shows that when it comes to the use of algos in FX, the benefits far outweigh the concerns. Fundamentally, algos are enabling the end-user be it active trader or portfolio manager to deliver better execution for clients,” Hugh Whelan, global head of liquidity management at CME Group’s EBS, commented on the report.

He added that as portfolio managers devise their month-end hedging strategies ahead of the US election this week, traders are often faced with having to make execution decisions during times of significant volatility, making the cost of adding hedges potentially very expensive.

“Depending on how large major currency market swings and how long uncertainty and volatility lasts, a portfolio manager will need all the tools at his disposal to source the best liquidity and execute efficiently. In this scenario, the use of algos has become very popular and allowing the end-user to manage the situation far more efficiently than a human trader possibly could,” Whelan concluded.

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Known as Aiden, the platform uses deep reinforcement learning processes to adapt to changing market environments and execute trading decisions based on live market data.

Aiden uses pre-programmed data inputs and can make more than 32 million calculations per order without the need for re-coding like traditional algorithms.

Shary Mudassir, co-head of global equities execution at RBC Capital Markets, stated that the market volatility seen during ongoing coronavirus pandemic has provided the firm with an opportunity to prove its ability.

“Aiden was able to recognise the sudden changes in the market and adapt to them, allowing it to preserve performance,” Mudassir added. “The Aiden platform is part of our vision to be one of the best in the world at unlocking the potential of artificial intelligence and delivering those insights to our clients in an explainable way.”

RBC Capital Markets said its traders worked alongside AI scientists at its Borealis AI research centre to create the initial concept and develop the platform. A volume-weighted average price (VWAP) algorithm was the first Aiden solution to go live, allowing reduced slippage to improve the broker-dealer’s clients trading performance.

“The Aiden VWAP algorithm has demonstrated, under a standardised evaluation framework used to eliminate bias, the ability to significantly reduce slippage against market VWAP in the US equity market,” said Jas Sandhu, head of equities algorithmic trading at RBC Capital Markets. “There are plans to expand the Aiden application to RBC Capital Markets’ full suite of electronic trading algorithms.”

Aiden is available to RBC Capital Markets clients that are trading US and Canadian equities, with plans to roll out the platform to other clients across its geographical footprint in the coming months.

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Known as Prism Frontier, Virtu said in a statement that the TCA has been designed as a transparent tool to gain actionable insights on execution for its Frontier algos, including Fan, Oasis, Covert, Catch and Opportunistic.

It will provide traders with post-trade analysis of algorithm-specific performance metrics, liquidity codes on executions from its alternative trading systems (ATS), and analysis of fills based on child order intentions.

“Virtu’s algos don’t over-complicate the task they’ve been assigned,” Steve Cavoli, global head of Virtu Execution Services, commented. “They digest massive quantities of market data, in real-time, and make appropriate decisions. When the trade is complete, Prism makes it easy to review the results. This is TCA for a trader. It’s a practical review of execution.”

As well as the Prism Frontier post-trade TCA, Virtu revealed it expects to launch a real-time analytics tool to allow traders to monitor algo executions as they progress in the third quarter this year.

Results of The TRADE’s 2020 Algorithmic Trading Survey revealed that hedge funds are increasingly focused on trade performance as a driver of execution quality.

Virtu has made efforts in the past year to boost its analytics capabilities, after expanding its TCA to FX algo trading in August. The TCA for FX algos aims to provide traders with performance evaluation and comparative analysis to improve execution outcomes. Prior to the launch, Virtu had also extended its TCA and market impact models to FX and fixed income.

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ALiX, which BNP Paribas said is the FX industry’s first digital trading assistant, will deliver content and running commentary on execution to traders using the bank’s modernised Cortex LIVE platform. As market participants continue to battle with the onslaught of data volumes, ALiX will digest market information, presenting tangible options to improve algo execution mid-flight, effectively giving BNP Paribas’s FX algo­rithms a voice.

“We’ve taken a leaf out of Silicon Valley’s book with ALiX,” says Asif Razaq, global head of automated client execution (ACE) at BNP Paribas. “Similar to the concept of Alexa or Siri, ALiX was designed to focus on financial markets. These assistants are solving multiple workflow problems. ALiX is designed to be your personal trading assistant, where it will monitor multiple live market data feeds with­in our real-time analytics portal.

“ALiX will also intelligently inform the client of any key market events and present context relevant options to choose from, ulti­mately providing the client with an intelligent roadmap of what to do next.”

Alongside ALiX, BNP Paribas has launched its Cortex LIVE platform with a real-time market analytics portal, known as Insight LIVE, which the digital trading assistant will use to deliver market content, data and intel­ligence to traders.

Insight LIVE builds on the FX pre- and post-trade transaction cost analysis (TCA) that BNP Paribas already offers clients through its Insight portal, however, the Insight LIVE data has been expanded to in­clude real-time information to bridge the gap between the pre- and the post-trade TCA.

Navigating the execution flight path

BNP Paribas launched its FX single-dealer platform, Cortex, in 2013 at a time when FX markets were undergoing a major evolution following the introduction of algorithmic trading and electronic trading platforms. FX markets first adopted the basic TWAP and VWAP algo trading strategies from equi­ties, but as the arrival of multiple new FX execution platforms caused fragmentation in liquidity, second generation algorithms were rapidly developed to aggregate that liquidity across numerous venues.

Although slightly late to the party, BNP Paribas says it pioneered the third generation of FX algorithms. Razaq, who has been part of the FX algorithmic trading team at BNP Paribas for almost a decade, was tasked with developing the bank’s expedition into FX algorithms and soon developed what would become the third generation algo: adaptive algorithms.

Razaq says he deployed basic forms of AI technology and real-time insights so that the algos can self-adjust when working a trade. At that time, BNP Paribas launched two adaptive algorithms, Chameleon and Viper, providing clients with both aggressive and stealthy algo trading strategies when the Cortex platform first went live.

“Execution algorithms have grown in use in the FX space, whereas in the equities space, they’ve existed for a very long time,” Razaq adds. “FX algorithms were more difficult to build due to a lack of transparent data and fragmented liquidity. As we saw the elec­tronification of the FX market take place, we started to develop models to gather market intelligence and were able to build algos that adapt in real-time.

“It was a greenfield project in terms of having a blank slate, and this is where I really wanted to utilise my expertise in artificial intelligence. Can I build something using basic AI techniques that will give us an edge, to improve on what the market is doing with the first and second generation algos? We also wanted to simplify the algo journey, because clients think of algos and see complexity. Sim­ plicity was key. When BNP Paribas launched its algo platform, one of the key objectives I set myself was not to overcomplicate the algo selection process for the client.”

Simplicity has remained a key part of Cortex following the revamp, and rather than build­ing several different algorithms, BNP Paribas focused on enhancing its existing Chame­leon and Viper algo strategies. While clients found value in the self-piloting and adaptive algorithms BNP Paribas had developed, the bank says many wanted to be more involved in the algo execution process. To meet this demand, BNP Paribas has introduced what it is referring to as the ‘fourth generation’ of FX algorithms: interactive algos.

The interactive algorithms provide a view of the data it is processing and reacting to from the Insight LIVE market data hub, allowing clients to consider various options for order execution. But, rather than running the algo on autopilot, as has traditionally been the case, traders are handed the controls to turn off autopilot and navigate the execution path, based on real-time analytics delivered via ALiX.

The four pillars

The FX algo trading team at BNP Paribas focused on four pillars when building Cortex LIVE; user experience, market analytics, integration and intelligence. For user expe­rience and integration, market participants have a multitude of single-dealer platforms to choose from, all of which are competing aggressively for client desktop real-estate. Expert user interface developers were brought in by BNP Paribas to examine the Cortex platform and map out the user’s journey to identify where that workflow could be streamlined or simplified.

Simultaneously on the client side, firms are upgrading or implementing digital infrastruc­tures to automate their own workflow, so BNP Paribas says it is imperative that Cortex LIVE can be integrated within the whole FX ecosystem and with client workflows. In order to achieve this, BNP Paribas teamed up with financial markets operating systems specialist and industry disruptor, OpenFin.

 “Cortex Live is the first external platform that BNP Paribas has put on OpenFin, but the bank has been a supporter of OpenFin in terms of internal use cases for many years,” explains Adam Toms, CEO of OpenFin Europe. “With the development of the new major integration process platform, BNP Paribas adopted some of the best components that OpenFin has to offer, including the FDC3 interoperability stan­dards and user experience. It is a big step for­ward and, frankly, any organisation moving forward with interoperability is going to be a market leading vendor.

“It’s encouraging to see, given my back­ground and that FX has historically been an asset class with a lower level of transparen­cy, that BNP Paribas has made a real push with transparency, with real-time TCA and in-depth venue analysis. That is a powerful differentiation.”

Onboarding clients, particularly the more traditional asset managers, to a new plat­form can be a headache in terms of legal paperwork and getting the system onto client desktops. To address this, BNP Paribas has ensured that the Cortex LIVE platform is ac­cessible and downloadable via the app store on Bloomberg, which means that clients can download the platform within the Bloomberg ecosystem without having to go through a major integration process. For those that often struggle to keep track of usernames and passwords, Cortex LIVE clients will not even have to log in, as Bloomberg is able to authen­ticate a client’s system for easy access.

“Cortex LIVE being available via Bloomberg removes that integration barrier, and that’s why we expect the adoption rate of clients consuming Cortex LIVE to be significant,” says Nick Hamilton, head of EMEA eFX sales at BNP Paribas. “It streamlines and simpli­fies workflow. Clients just have to download the platform from the Bloomberg app store and they’ll have access to all of the enriched functionality.”

Future-proofing

With the OpenFin integration, BNP Paribas claims it is ‘future-proofing’ the new platform and, eventually, systems on the client side will be able to communicate with BNP Parib­as’ digital trading assistant to create a fully automated and integrated trading platform. Although the FX industry is not quite at this stage yet technologically speaking, BNP Pari­bas has laid the foundations to take ALiX and Cortex LIVE a step further.

In the early days, ALiX will be fed with basic responses, but the machine will self-learn and be able to respond, building a rapport with the client as they ask the digital trading assistant more questions. As ALiX is given more data its intelligence will grow and broaden its remit from guiding clients not just through their FX algo trading execution, but for all FX activities.

“The vision is that we will broaden the ALiX concept as a holistic solution across the FX execution environment for all of your activities as a user on Cortex,” says Joe Nash, digital FX chief operating officer at BNP Paribas. “Then it becomes more than the execution control point for the algo business, it will be a digital trading assistant for every­thing you’re doing in the FX space. Pushing content, trade ideas, figuring out when you expect to put a particular trade on and provide pricing to you in that regard, there’s a whole sphere of functionality that we can bring to the FX space through ALiX.”

In the long-term, BNP Paribas aims to take the new technologies in the form of ALiX and the new functionality and analytics on Cortex LIVE to other financial instruments and asset classes. Cortex LIVE, ALiX, as well as interactive algos Chameleon and Viper, could soon embark on a journey into equities or fixed income. BNP Paribas says this expan­sion is being considered amid a trend towards multi-asset trading in asset management.

“We are now looking to extend our award-winning algo execution service to other asset classes such as fixed income, futures and so on. This is against the backdrop of the buy-side consolidating their activities and forming multi-asset trading desks. Eventually, we want ALiX to become that multi-asset BNP Paribas trading assistant,” Razaq concludes.

This article was originally published in the TradeTECH FX Daily magazine, produced by The TRADE, which was distributed to attendees of this year’s TradeTech FX Europe conference.

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The nine categories listed will be supplemented with two further awards for overall outperformers across algorithmic trading and EMS based on the results of The TRADE’s respective surveys carried out earlier in the year.

In an unprecedented trend, The TRADE’s Algorithmic Trading survey was dominated by four companies this year, Bernstein, Exane BNP Paribas, Jefferies and Mirabaud Securities, all of which earned nominations for the categories in this section.

The prime broker of the year award will be fought out between BNP Paribas, Cowen, Nomura and Wells Fargo. Again, following the Algorithmic Trading survey trend, the EMS will also see four companies, Instinet, Portware, TORA and Virtu Financial, battle it out for the product consistency, availability and customer support awards.

Visit our event page here for more information and to book your place, and don’t forget to submit nominations for this year’s Rising Stars Awards.

Best trading performance:

Bernstein

Exane BNP Paribas

Jefferies

Mirabaud Securities

Best customer support & consulting

Bernstein

Exane BNP Paribas

Jefferies

Mirabaud Securities

Best access to market

Bernstein

Exane BNP Paribas

Jefferies

Mirabaud Securities

Best user experience

Bernstein

Exane BNP Paribas

Jefferies

Mirabaud Securities

Best price improvement capabilities

Bank of America Merrill Lynch

Bernstein

Exane BNP Paribas

Mirabaud Securities

Prime broker of the year

BNP Paribas

Cowen

Nomura

Wells Fargo

EMS best product consistency

Instinet

Portware

TORA

Virtu

EMS best product availability

Instinet

Portware

TORA

Virtu

EMS best customer support

Instinet

Portware

TORA

Virtu

The post Leaders in Trading 2019: Algorithmic Trading & EMS awards shortlists revealed appeared first on The TRADE.

TABB Group drew up the league table following a poll of 92 heads of trading at buy-side firms earlier this year to score brokers on various categories, including commissions allocation, algorithmic trading, high- and low-touch coverage, execution consulting, transaction analytics and capital.

Morgan Stanley led the league table in terms of commissions, followed by JP Morgan, Bank of America Merrill Lynch, Credit Suisse and then Goldman Sachs for the top five brokers list. Although Morgan Stanley took the top spot, JP Morgan led the commissions ranking for servicing larger asset managers with more than $150 billion in assets under management.

“It’s critical for brokers to understand where they sit in the eyes of their clients, that the buy-sides’ largest brokers receive by far the largest percentage of flow. The implications are far greater for hedge funds as their top broker tends to be their prime,” said Larry Tabb, co-author of the report and founder of TABB Group.

The survey also found that although commissions are a critical component in broker allocation, asset managers ranked algorithms as the main reason they allocate flow to a broker. For algorithms, the heads of trading ranked the combined Virtu and ITG company as top, but when Virtu and ITG are considered separately, funds agree that JP Morgan is the top algo provider, followed by UBS and Sanford Bernstein.  

A separate report from TABB Group earlier this month said that US commissions are plummeting in light of a shift towards passive investing, and the unbundling of research and execution under MiFID II. Despite MiFID II not being directly enforced in the US, larger institutions have opted to separate research and execution commissions, leading to a decline in the US commission pool of 42% since 2015, and 27% since 2017.

The post Morgan Stanley tops US equities broker league for commissions appeared first on The TRADE.

Last week, Credit Suisse recorded income of $359 million in its global markets division, a massive 141% increase year-on-year, despite facing tough market conditions.

In comparison to its Wall Street peers, it was the only bank that recorded positive trading revenues for the second quarter. It was also one of the stronger performers across fixed income, currencies and commodities (FICC) and equities trading.

US investment banks posted an average 8% drop in trading revenues in the second quarter, as did Swiss rival UBS.

The significantly improved performance of the bank coincides with the Credit Suisse’s shift in focus to electronic trading over the past year.

Speaking on Credit Suisse’s second quarter earnings call with analysts, its CEO Thidjane Thiam, explained that it has now begun to take back market share after turning around its flagship advanced execution services (AES).

“AES, our automatic trading platform which we’ve really revamped and we’re regaining market share,” said Thiam.

“Clearly during the restructuring, we have lost share. We’ve come back with a completely different business.”

He added that the bank has reshuffled around half of its managing directors within the equities division, and in March, appointed 18-veteran Julian Corner as head of EMEA for AES. All of this has resulted in the bank re-establishing historic electronic trading relationships, Thiam added.

“So its new people, the leadership team is new, new technology and we’ve been gaining share almost with every client,” he said.

Credit Suisse also reported further gains in its prime brokerage division, both in revenues and new mandate wins, which helped offset losses in equity derivatives.

Credit Suisse recently reorganised its global markets prime services top brass by appointing two of its most senior prime brokerage executives to jointly lead the division. In June, John Dabbs and Ryan Nelson have been promoted to Credit Suisse’s equities management committee, with responsibility for global markets prime services

In the previous quarter results, Thiam said to investors that the business was able to generate more revenues with 25% less leverage, and revenues per unit of leverage were up 31% year-on-year.

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While there are clear areas of differential performance among various algo providers, the overall showing from the 2019 survey is largely positive, as the industry moves further away from regulatory compliance concerns brought about by MiFID II and reprioritises quality of execution and securing better outcomes for the end investor.

This trend is once again evident from responses provided by the contingent of hedge fund firms in this year’s survey, with the total average score across all algo providers rising to 5.72, a significant increase on the total average of 5.47 in 2018 and even above the pre-MiFID II score of 5.68 in 2017.

The highest score achieved in this year’s hedge fund algo survey was in the customer support category (5.92), closely followed by anonymity (5.81), ease of use (5.81) and improve trader productivity (5.80). There was no area of performance that received a score lower than 5.50 – the lowest being 5.57 for price improvement – which indicates a good overall level of satisfaction among hedge funds towards their algo providers.

Similarly to the responses from long-only buy-side firms, the scoring in this year’s survey suggests that efficiency has become the key watchword for algo end users.

One of the most interesting trends that the algo survey has thrown up in recent years has been the fluctuating number of providers that buy-side firms, both long-only and hedge fund, are choosing to implement and use. In last year’s survey, hedge funds were almost exclusively using greater numbers of algo providers than they had historically – the only exception being on the lower end of the AuM scale – however that trend is now clearly in reverse.

Hedge fund firms that are managing up to $0.25 billion and $0.25 to $0.5 billion in assets were the only two groups to record increased numbers of algo providers this year, with the latter bracket actually doubling the average number of providers year-on-year, from 1.5 in 2018 to 3 this year, still far above the figures from the 2017 survey as well.

Mid-sized and larger hedge funds, however, showed that they have been cutting down on the number of algo providers they are engaged with, most noticeably in the $0.5 to $1 billion AuM bracket, which recorded a sharp drop from  an average 4.5 algo providers last year to just 1.8 in this year’s survey.

There has been a significant year-on-year shift for hedge funds to move towards using between one and two providers, accounting for nearly half of all hedge fund respondents, whereas previously these firms said they were using more than 5 in last year’s survey. All of this would suggest that hedge funds used 2018 as a sort of ‘test drive’ period, to take new algos out for a spin before deciding on which ones they would adopt on an ongoing basis.

The conclusions that can be drawn from this year’s algo survey – incorporating both long-only firms and hedge funds – is that the focus has firmly swung back to increased execution efficiencies and outcomes as the use of algorithmic trading increases, as the buy-side also applies increasing scrutiny on their providers. The question going forward is who among these providers, as the industry begins to eat itself through consolidation, will be left standing to fulfil the evolving needs of the market.

The 2019 Algorithmic Trading Survey for hedge funds can be read in full here.

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