While the implementation and development of artificial intelligence (AI) and machine learning projects have continued apace in the industry, attitudes have noticeable shifted. Buy-side representatives at some of the industry’s biggest conferences ditched the usual hyperbole in favour of more nuanced opinions about the transformative potential of AI, extolling the virtues of patience and long-term planning or just straightforwardly dismissing the misconceptions of what AI is actually capable of. Despite the dangers and pitfalls that technology hype present, the buy-side is powering on with AI adoption and it looks certain to be a central facet to asset management strategies for the foreseeable future. The sell-side, for its part, is also heavily investing in AI for the future, evidenced through financing deals such as that led by Goldman Sachs in start-up H2O.ai.

One of the most interesting technology stories of the year also featured an industry-first, as BNP Paribas took a leaf out of Silicon Valley’s book with a Siri-style digital trading assistant, which launched alongside its real-time market analytics and interactive algorithms as part of major upgrade to the bank’s Cortex FX trading platform.

The TRADE’s annual Execution Management Survey found that EMS providers will have many reasons to be pleased, as scores continue to rise as the industry moves further away from the compliance-focused days of two years ago when MiFID II came into play. However, there are signs that some providers may be resting on their laurels, falling behind the high standards their peers have set in this year’s survey.

The fire around cryptocurrencies and digital assets may have not burned itself out entirely this year, but it is certainly dimmer than what we saw in 2018. In January, the European Markets and Securities Authority (ESMA) stated that certain crypto platforms should be subject to MiFID II rules on trading, while the chair of the European watchdog said the following month that regulators should show “objectivity and open mindedness” when it comes to developing frameworks for the supervision of crypto-assets and distributed ledger technologies. The lack of clearly defined regulation for crypto-assets hasn’t put major industry players off developing their own offerings, with Deutsche Börse outlining its plans for a regulatory compliant, full crypto ecosystem in March, while one of the buy-side firms to invest in the space with conviction, Fidelity Investments, announced at the end of the year that it would be bringing its own platform and custody services to Europe after first launching in the US last year. The less said about Facebook’s much pilloried entry into the space with its Libra project, the better.

There were markedly fewer big money acquisitions in the last 12 months compared to the previous year, but it was the analytics space that was most notable. In September, SS&C Technologies agreed to acquire risk analytics products and services from IBM’s Algorithmics division, expanding its range of data products, while in October, cloud-based portfolio analytics and data services provider StatPro was acquired by investment data automation specialist Confluence Technologies for just over £160 million. Elsewhere, Liquidnet completed deals for marketplace and aggregator RSRCHXchange in May and natural language processing (NLP) specialist, Prattle, a month later.

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Speaking at the ‘Gaining the Edge Hedge Fund Leadership’ conference in New York, representatives from the hedge fund and allocator industry discussed the significant importance the technology will have on investment strategies and processes. 

“AI and machine learning is going to raise the bar across everything. Those that are not paying attention to it now will fall behind,” said one panelist from a $6 billion alternative investment manager, speaking under Chatham House Rules. 

Another panelist from a $4 billion hedge fund agreed, and argued that “every manager has to be looking at it”, while another explained it is beginning to analyse where it can use machine learning for its investment strategies. 

“We are looking at AI across all of our strategies – whether it is for execution, research, capital allocation etc. The technology is something that is very real, but at the same time, it is not a strategy by itself. It depends on how managers use it,” said one senior investment manager. 

The technology is becoming a necessity for hedge funds in order to analyse masses of data, predict corrections in supply and demand imbalances, as well as forecast market movements for tactical asset allocation. 

A recent poll conducted by BarclayHedge found that 56% of hedge fund respondents have used AI or machine learning to inform their investment decisions. Two-thirds also said they use machine learning to generate trading ideas and optimise portfolios. Just over quarter also said they use automation to execute trades. 

One speaker from a global asset manager highlighted that emerging hedge fund managers (sub-$1 billion in AUM), could become specialists in machine learning investing and develop viable systematic strategies. 

However, some of the largest hedge funds are developing machine learning tools or investing in it, including Bridgewater Associates and Man Group. In addition, Renaissance Technologies and Two Sigma have used the technology for investing for a number of years.

JPMorgan Asset Management arm is also planning to invest in emerging and established machine-learning statistical-arbitrage hedge funds. 

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NICE Actimize has launched a new artificial intelligence-based surveillance platform aimed at providing financial services firms and regulators with a single overview of market abuses.

The SURVEIL-X solution is a cloud-based trade, risk and market abuse detection platform launched in response to growing demand from buy- and sell-side market participants for greater oversight and control of surveillance operations in the face of heightened global regulatory scrutiny.

Adopting an AI-based and rules-based analytics foundation, SURVEIL-X reviews the disparate elements surrounding possible market manipulation actions, pieces them together without manual intervention and delivers a single alert to compliance.

By pulling data from various sources, including traditional market information, behavioural data derived from analytics, and various communication platforms, the platform provides a holistic view of market abuse and “unknown” factors, according to NICE Actimize.

“For financial services organisations (FSO), surveillance capabilities have become a compliance necessity for detecting known regulatory risks. The only problem is that risk is a moving target, and the things you don’t know or uncover can also hurt your firm,” said Chris Wooten, executive vice president at NICE.

“With SURVEIL-X’s complete surveillance coverage, AI-powered analytics and anomaly detection, and its other next-generation surveillance capabilities, FSOs can move out ahead of the curve and insulate themselves from reputational damage and fines.”

As new and expanded regulatory initiatives have been rolled out across the global financial markets, including MiFID II and Dodd-Frank, both financial services firms and regulators have sought to increase the use of AI-based tools to capture and reduce market abuse.

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A panel of buy-side speakers said that while there has been progress made with various AI use cases and systems in production by certain quantitative hedge funds, the majority of firms, particularly among hedge funds, are still only in the evaluation or research phase.

The reality, according to Rafael Molinero, CEO of Molinero Capital, is that most firms are using AI on an ad-hoc basis but the hype around AI means the truth becomes obscured, while Richard Bateson, director at Bateson Asset Management, said that the larger buy-side funds with more resources are perhaps in more advanced positions but AI still only represents a fraction of what they are doing.

However, there are areas in which AI holds potential to improve processes and Sunil Patel, a senior trader at APG Asset Management, highlighted how his firm is currently working on using AI within its pre-trade analytics, although he qualified that it will only be adopted “if there are clear patterns that can be discerned”.

While there are aspects to machine learning and AI that are already in use in the industry for algo wheels and broker allocation, Bateson said that there is a lot of potential for AI to become more ingrained within execution processes, at least where available data was concerned. However, he did acknowledge that there are challenges with data work as well.

“It’s not just the cleaning of the data that presents an issue, it’s the synchronisation if you are using alternative data. What date was it published? If you take something off Bloomberg, some of it has been back-dated and cleaned after the event it was actually published. That’s a big issue,” he commented.

APG’s Patil highlighted the mundane side of using AI, stating that “80% of the job is cleaning the data” and commenting that such work requires a significant investment of time and resources to complete before AI systems can even be started up.

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Users of the FactSet workstation will be able to build and deploy their own machine learning models for workflows such as equity volatility, bond performance, and macroeconomic event predictions, according to the vendor.

Existing client workflows and data within the FactSet platform will be supplemented by the AI solution, providing “guardrails for those without data science expertise to build and deploy advanced machine learning models”.

“Clients are looking for more effective data and AI tools that will help them surface new investment insights faster and with greater efficiency,” said Rob Robie, executive vice president, Analytics at FactSet.

“We are excited to be working with DataRobot to provide an elegant and intuitive solution that allows users to develop and execute successful machine learning strategies.”

Earlier this year FactSet expanded its alternative data ecosystem to include IHS Markit’s research signals as both firms seek to address challenges around data fragmentation.

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Buy-side cover interview – Since joining Royal London Asset Management in early 2018, the firm’s head of dealing, Cathy Gibson, has been busy establishing and running a new centralised trading desk. Gibson talks to John Brazier about building a desk on the foundation of diversity in the face of increasing market complexity and evolving trading conditions.

The big interview – James Baugh, head of EMEA equities market structure at Citigroup, talks to The TRADE about the impact MiFID II has had on European market structures, the changing relationship between the buy- and sell-sides, and what further regulatory changes may be in store.

Buy-side interview – The TRADE talks to Alison Hollingshead, chief of staff for trading platform & core tech at Man Group, about the importance of diversity to the financial industry and how the firm is taking a proactive approach to this challenge.

Timeline: The first 500 days of MiFID II – The TRADE presents a comprehensive timeline of the major incidents, developments, statistics and talking points that have occurred during the first 500 days of the MiFID II regulatory regime, a milestone that occurred on 18 May 2019, including data delays, the rise of periodic auctions and systematic internalisers, and inevitable demands.

Outsourced trading: The future of the buy-side desk? – With an increasing number of buy-side firms turning to outsourced trading providers in recent years, Hayley McDowell finds out how providers really operate and differ from agency brokers, and examines what benefits opting for outsourced execution can offer to the asset management industry.

Embracing artificial intelligence for the buy-side – The increasing use of artificial intelligence and machine learning systems among buy-side firms is in danger of creating a hype bubble. The TRADE examines where asset managers are currently using these technologies to optimise their trading strategies and what pitfalls firms must avoid to (eventually) foster a harmonious trading between man and machine.

The fixed income desk in 2019 – Chris Hall talks to four leading fixed income heads of trading desks around the world to gauge the state of the fixed income markets in 2019, covering the biggest opportunities and challenges for asset managers, how the relationship with the sell-side and vendors has evolved, and what new skills are required on a successful desk.

The 2019 algorithmic trading survey (hedge fund) – Brokers are stepping up to the plate in the post-MIFID II landscape to provide consistent execution to long-only buy-side firms that are more knowledgeable and discerning than ever before.

Hayley’s comment – Facebook’s foray into finance

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The application of artificial intelligence (AI) and machine learning (ML) technologies in financial services is being increasingly positioned at the vanguard of technology-focused industry discussion and strategies, as discussions focus on the practical implications of deploying such tools beyond middle- and back-office functions.

As margins are squeezed and costs continue to rise in light of fee compression and an increased regulatory environment, the buy-side is increasingly turning to AI and ML in the search for further efficiencies.

There can be no doubt that huge increases in data and trading volumes that the buy-side is now transacting, is forcing the need for new technologies. While taking part in a panel discussion at this year’s TradeTech Europe conference, AXA Investment Manager’s global head of trading, Daniel Leon, told delegates that his firm has no choice but to invest in new technologies because his trading desk simply cannot keep up with the sheer amount of data and information required to maintain its trading activity.

“We are not able to do what we used to do 20 years ago,” said Leon.  “Yes, you can have a specialist on leverage loan, but on the big credit market or medium and small-cap you cannot have all that information on one guy. We are trying as well to solve problems that we used to do a long time ago. For the more vintage traders it used to be that the trader would know the market and what’s traded for one month, what happened last week, they had information and that’s what typical trading used to be.

“But now we have to gain efficiency, we have to trade so many bonds that you can’t ask one trader to remember everything, to know that this sector last week had this event. We have to reconstitute the experience that the trader used to have: What has traded, what was the liquidity and what was the market impact. You can’t do that on a comprehensive basis.”

BlackRock’s global head of trading, Supurna VedBrat, echoed Leon’s sentiment on the importance of AI and data for the future of the industry at this year’s US Fixed Income Leaders Summit in Philadelphia. Focusing on fixed income markets, VedBrat told delegates that not only will AI be a key element in the next evolution of buy-side trading operations, but it will likely morph the role of the buy-side trader in the process.

“Data science and AI give us the ability to truly augment human intelligence with computing power, and you are able to do that at scale. I think it is going to materially change trading strategies that the buy-side uses. You don’t need human intelligence to pick trades, so you can automate a lot of that flow and the trader is now much more of a risk manager overseeing that the market is working the way we expect, and if not, they have the ability to step in and correct it,” VedBrat said.

Ahead of the curve

Research from TABB Group earlier this year has, in fact, suggested that the buy-side is slightly ahead of the curve in terms of AI adoption compared to the sell-side and exchange operators. Over 80% of asset management respondents stated that they were at least in the planning or research phase of implementing AI, compared to 73% of their sell-side counterparts and exchange operators. At the same time, more than 60% of buy-siders said they expect spending on AI to increase over the course of this year.

According to the research, the majority of asset managers agree that actionable insight is the biggest benefit of deploying AI technology, followed by increased efficiency and automation, strategy selection and risk management.

However, there is a false perception can sometimes be that AI and ML are relatively new to institutional trading; the truth is that both buy- and sell-side organisations have been exploring, developing and implementing such technologies for many years now.

“The key takeaway from all of this is that most capital market participants are bullish on the use of AI and big data in the near future. It is high on the change agenda at most firms, with the main use case being around the investment process, but also in trade execution and operations,” the research from TABB Group concluded.

As with most technology trends though, hyperbole has a way of dominating the discussion. Similarly to the way blockchain exploded into the financial markets’ consciousness in 2016, AI and ML have become industry buzzwords, or at the very least a misleading shorthand, that risks overstating practical applications.

Ian McWilliams, investment analyst at Aberdeen Asset Management, detailed how the understanding of what ML technologies are capable of is being distorted by a lack of understanding and exaggeration, during a panel discussion at TradeTech FX Europe at the end of last year.

“I joke that when you are advertising externally you say AI, but inside you say machine learning and actually you are just doing logistic regression and things like that,” he said. “I don’t think that’s disingenuous, maybe it’s a bit of hyperbole, but it’s not wrong in terms of definitions, because when we talk about machine learning it really is anything where you are getting an algorithm to learn from data.

“We’re taking a lot of market signals and sentiment signals, forecasting what markets are going to do in the future and using those to build trading strategies.”

McWilliams explained that the hype around elements of ML such as deep learning, image recognition and natural language processing (NLP) are distorting expectations around what are essentially tools to better model data for trading strategy decisions, particularly when it comes to conversations with fund managers.

“The interesting thing we need to think about as an industry and maybe where attitudes need to change is around interpretability of the models, which is a big question in a lot of areas, not just finance,” he said.

“Whenever we come out with a trade a question we get asked by the traditional fund managers is ‘Why is it making that trade?’ and they generally expect a very causal, A to B explanation, but that often defeats the point of these very complex algorithms. The middle ground is not good enough to just say that the algorithm says to do it, so we are doing it, but there needs to be more conversation between the quant people and more traditional people to understand there is a trade-off there.”

Beyond the middle-office

As asset managers continue to experiment with AI and ML, the goal has always been to automate manual and often repetitive tasks for greater efficiency and cost savings, freeing up time for traders to focus on more pressing tasks or complex order flow.

But, according to market participants and technologists, the use of AI and ML elements are now permeating into more intricate parts of the business. AI and ML are beginning to show value when it comes to pricing and seeking liquidity, challenges that are often highlighted by buy-side traders in the current market conditions.

“The simple trade automation, the idea of creating rules to take some of the more liquid or easier to trade orders off the books, makes sense,” said Ian Mawdsley, head of buy-side trading for EMEA and APAC at Refinitiv, during a webinar hosted by The TRADE in March.

“The reality is that we have been using both of these processes [AI and ML] for some time. If we look at algo trading supplied for the sell-side in particular, much of that was formed in the first place to automate some of the more menial tasks sales traders were performing. That has now been taken to the next level where people are looking at price discovery and liquidity discovery.”

Further to this, looking at the practical applications of AI and ML, an area that has been of particular interest to the buy-side is the algo wheel, or broker selection processes. While an algo wheel is technically a form of AI, it is on the more basic, rules-based end of the spectrum, but it does provide a solid foundation to build upon.

JP Morgan Asset Management has homed in on this space and produced a framework, known as STARS (Systematic Trading Algorithm Recommendation System), which aims to optimise the way in which traders choose algorithms using ML technology. According to the firm’s global head of equity trading automation and execution, Ashwin Venkatraman, the vast amounts of data now accessible in the market underpins and is at the heart of implementing these new tools on the trading desk.

“We’ve had [STARS] since 2017, we’ve had 90% of our algorithm placements, even back then, going through the framework and we are rolling it out globally as well,” Venkatraman said on the webinar alongside Refinitiv. “In many ways we have been there and we’ve been optimising that wheel over time. We are trying to think about this more holistically. It’s really about being data-driven in the sense that wherever we look at all functions of trading, there are different aspects to it and it is about trying  to leverage that data in the most appropriate way.”

Taking part in a keynote discussion at this year’s TradeTech conference, Antish Manna, head of execution research at MAN GLG, said that the firm went live with a machine learning-based framework for order flow and broker allocation last year.

“This framework effectively takes away the need for human to set an arbitrary target for ‘my first three brokers are going to get this amount of flow’ and continuously updating that target to having a machine that automatically does that”, Manna explained.

“The beauty of it is that it becomes a very clean conversation with our brokers; they know how we are doing things and that they will get more flow, and this machinery also adapts to changing market conditions.”

Man and machine

Despite all of the potential benefits that may be realised, there are significant obstacles when it comes to deploying AI or ML processes, mainly in the form of compliance hurdles, transparency concerns and the build vs. buy dilemma that most firms will consider at some point during implementation.

Firms are urged that they must engage with compliance departments when undertaking any technology project, and the importance of continuously assessing the model to overcome some of those transparency barriers is paramount.

The adoption and successful use of AI or ML comes with a significant resource cost attached, and as such, firms that expecting to realise quick results will be sorely disappointed unless they are prepared to play the long game.

Addressing these unrealistic expectations, MAN GLG’s Manna said that the majority of time spent on machine learning projects is used to clean data before research and development can take place, and that those firms that are only now starting their journey with machine learning should be not expect to see results in the short-term.

“The truth is, it is a fallacy and it takes a huge amount of time to build a framework where you can deliver things at scale that work,” he said. “On the machine learning and AI side of things, problems are best solved by teams of people, because you need the challenge, rigour and time to learn and fail, learn and try again; that process takes a lot of time.”

Another significant challenge is in finding the right balance between man and machine, as market participants and technologists attempt to dispel the myth that AI and ML is even close to replacing the human buy-side trader.

“Technically there is a bit of truth that everything could be automated, there is no doubt that most processes could be run without a human being, certainly within our industry,” Mawdsley explained. “The point is that the world isn’t that flat and there are certainly unusual things that happen in life every day that don’t follow the patterns and I am not sure that we are 100% there, where the AI is able to interpret all of those black swan events and build them into a model.

“There is an element that says the human brain is trained to deal with these outliers, and the machine giving help to follow those patterns is probably where you want to be… It’s about using technology to make more informed trading decisions and as such that means not fully automating everything at all and ensuring that we are bringing in an element of human intelligence, but we are presenting people with options.”

As data becomes more readily available and the size of that data continues to grow, there is little doubt that asset managers implementing AI and ML technologies are at the forefront of the future of buy-side trading, and this is happening now. As some funds struggle to adapt to the changing trading landscape, others are ready and willing to seize the opportunity using AI and ML to overhaul traditional trading processes.

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Speaking to Billy Hult, president of Tradeweb, as part of a fireside chat at this year’s Fixed Income Leaders Summit US conference, VedBrat opined that due to the changing nature of the fixed income ecosystem, these differentiators would only grow in importance in the near future.

“Data science and AI gives us the ability to truly augment human intelligence with computing computer, and you are able to do that at scale,” said VedBrat. “I think it is going to materially change trading strategies that the buy-side uses and even on the sell-side.”

“You don’t need human intelligence to pick trades, so you can automate a lot of that flow and the trader is now much more of a risk manager overseeing that the market the working the way we expect, and if not, they have the ability to step in and correct it.”

VedBrat also touched on the proliferation of data, which she believes will soon result in clean data sets available to firms from the cloud that will significantly increase computing power to ultimately improve trading and investment decision-making, in a similar manner to trends already occurring int the equities space.

Considering how the evolution of the trader skill set may also play out, VedBret underlined the importance of proper talent management, particularly as changes to the fixed income ecosystem occur, such as the growth of exchange-traded funds and portfolio trading.

“I like having teams with diversity of thought and that comes in many different ways; people with different regional experiences, people who are representing different age groups, because that is the way we are going to be able to keep solving in an agile manner,” she said.

Considering how such teams can be deployed, VedBrat said that the traditional pyramid model did not necessarily result in the best ideas for firms, and that creating an environment focused on agility and innovation is better suited to an efficient and rewarding ideas process.

“It’s important that your passion, your purpose, is aligned with what the firm’s purpose is, because that is what is going to make a much better working environment. At BlackRock we want to give people multiple different experiences; it is there to take, we create the opportunity and they need to grab it.” 

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Prattle, a natural language processing (NLP) and machine learning specialist, was launched in 2014 and currently provides analytics on approximately 3,000 publicly traded companies and 15 central banks. 

Its analytics measure sentiment and predict the ‘market impact of publicly available content’ -including central bank and corporate communications, such as company earnings calls and press releases – designed to provide buy-side users with the ability to understand and anticipate relevant market movement, strengthen investment theses, and inform trading strategies.

The deal follows similar acquisitions by Liquidnet for institutional research marketplace and aggregator RSRCHXchange in May this year, and AI-based decision support and analytics provider, OTAS Technologies, two years earlier.

Prattle’s co-founders Bill Macmillan and Evan Schnidman, the firm’s chief executive, will continue to manage daily operations, reporting directly to Liquidnet president, Brian Conroy.

“Prattle is yet another powerful tool for asset managers to discover actionable insight from the enormous amounts of unstructured data that is produced throughout the capital markets,” said Conroy of the deal.

“This acquisition further supports our goal to help our Members generate better performance by providing them with the data they need to create greater conviction in their investment ideas, and then execute those ideas with speed and efficiency.”

Earlier this month, Liquidnet announced an expansion of its partnership with data and analytics specialist big xyt to monitor its execution quality and cross-check market volumes.

The deal means that Liquidnet has extended the use of big xyt’s Liquidity Cockpit platform, providing the firm’s execution analysts with an interactive application to monitor market data via the Liquidity Cockpit dashboard.

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The bank’s annual ‘Growth Readiness Study’, conducted by Longitude Research of more than 500 industry executives from 20 countries, found nearly half of buy-side institutions believe technologies such as blockchain, artificial intelligence (AI) and robotics present growth over the next five years, up from 18% a year ago.

“[The findings] represent a huge change in how investment institutions are thinking about routes to growth. Now, they must follow this through by embedding an innovation mindset,” said State Street in a follow-up report on the results.

One buy-side institution that is taking advantage of new technologies is APG, the administrative organisation of Dutch pension fund Stichting Pensioenfonds ABP. It has set up the Brightlands Smart Services Campus to test practical applications of blockchain, AI and other technologies to improve operational management.

However, the study explained technology could be a doubled-edged sword for buy-side firms, as the possibility of digital disruption and the integration of new systems and platforms present an ongoing struggle.

“While investment institutions express a clear desire to integrate emerging technologies, their current approach is keeping them in the slow lane,” the report added.

“Upgrading existing infrastructure on a case-by-case basis will only exacerbate siloed legacy systems and make it harder to integrate emerging digital solutions, and it can be costly in the long-run.”

The report highlighted how the lack of expertise to manage the roll-out process of technology upgrades were among the top challenges for asset managers, while insufficient budget and capital to spend on technology are impeding asset owners.

“Given the costs of an architectural overhaul. Many firms are selectively seeking tech partners as a way to gain scale,” the study said.

The study also showed pressures on buy-side firms have increased over 2018, with the majority of asset owners and asset managers taking a defensive investment strategy in response.

Sixty-eight percent of respondents said it has become harder to achieve growth, against 66% who said the same a year ago.

In addition, 72% of asset owners have adopted a defensive investment strategy in response, as had 64% of asset managers and 59% of insurance companies.

The political outlook on key markets, combined with regulation governing liquidity risk, investment behaviour and regulatory attention on investment fees were cited as the greatest factors holding back growth.

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