Category: 2016

UK-based analytics provider OTAS Technologies has extended its Lingo Suite of analytics tools with the release of Lingo for Microstructure, which produces intraday reports containing up-to-date analysis of all standout and unusual stock market activity.

The Lingo Suite, originally launched last June, encases the vendor’s Lingo natural-language reporting technology, and leverages the OTAS Core market intelligence platform to generate easy-to-interpret, on-demand analytical reports detailing calendar events and highlighting the most significant changes in price performance, short interest, options, credit, valuation, yield and technical factors, as well as recent director dealings.

The new on-demand Microstructure reports include the latest developments in the markets and their impact on users’ portfolios, allowing clients to keep up to date with market activity and make more informed risk management decisions, while also providing an audit trail of their activity, officials say.

Clients will have access to an intraday narrative on single stock behaviour to overlay with other transaction cost analysis metrics. Analysis will also cover all stocks in a trader’s universe, with a two-year record of daily stock behaviour.

Lingo for Microstructure is essentially “Lingo History” says Charlotte Wall, managing director and head of product sales at OTAS. It records on a daily basis what happened to individual stocks, and links this to a trader’s orders to provide an audit trail, and can be used to answer generic questions or provide a general narrative on the activities of every stock in a trader’s universe. Instead of having to “scramble together information and data” on something that happened to a trade months ago, traders can “click on that stock on that day, and immediately get a full narrative,” Wall says. “That’s very powerful… in a live trading environment, because you don’t normally have that information in one place.”

The reports are available to current OTAS clients and to users of trading systems vendor FlexTrade’s EMS platform—with which OTAS partnered last year—and a number of other third parties. Wall says the main appeal of partnering with Flextrade was its work with startup secure messaging provider Symphony. In February, FlexTrade integrated Symphony’s chat function to allow users to communicate and distribute content to portfolio managers and brokers directly from their trading blotter. Wall says Symphony works “very well with the Lingo narrative,” which is why they’re the first third party to gain access to it. “You can take the natural-language text and automatically be able to message that through to an end user without any human intervention… instead of having to write it out manually or copy and paste” she says.

Now that OTAS has begun to establish metrics around trade execution, it will continue to link Lingo to other products to create bespoke offerings, Wall says, adding that the vendor plans to take “a lot of the narrative and really analyze more trading behaviour in more depth. This could be to do with the schedules that are calculated in the market, it could be analyzing algos that exist and their behaviour that you can actually then write a narrative around.” This can then evolve into other products, such as intraday TCA, using the narrative to make TCA reports more robust, or more in-depth alerts on certain trades. “We also see requirements from regulators and… about market surveillance. When you have this type of audit trail and narrative, it becomes quite powerful for others to be able to have an insight without any human bias at any point in time, and to be able to reference that,” she says.

The rise of artificial intelligence has raised questions on its uses and possible regulation – The Trade investigates “robo rules” and we ask industry experts the question on everybody’s lips: will there still be a place for human traders?

Artificial intelligence has dominated headlines recently, highlighting the best and worst of its capabilities and suggesting there is still work to be done and improvements to be made.

News of Microsoft’s Tay, an artificially intelligent bot which was created to mimic the personality of a 19-year old woman, quickly turned sour as it seemed to transform into a ‘bitter racist’ on the social media website twitter.

When Microsoft was asked to confirm whether the bot had been shut down, it responded: “The AI chatbot Tay is a machine learning project, designed for human engagement.

“As it learns, some of its responses are inappropriate and indicative of the types of interactions some people are having with it. We’re making some adjustments to Tay.”

A more successful venture into AI was seen in Google’s AlphaGo artificial intelligence after it defeated Go world champion Lee Se-dol twice.

Se-dol said after the second defeat: “I am quite speechless… I feel like AlphaGo played a nearly perfect game.”

Both scenarios outline that machines are rapidly becoming more intelligent, and in some cases, outsmarting their human creators.

The use of AI in the financial sector has already been implemented in some cases, and investment from major players like Goldman Sachs and JP Morgan are pouring into the technology in the fight to get ahead of each other.

AI machines possess capabilities to evolve, adapt and search for patterns so asset managers can use them to enhance their investment and trading strategies.

Algorithmic trading, for example, is the most widely used form of AI and its uses complex mathematical models to make transaction decisions on behalf of humans.

Microsoft’s Tay however, has proved there is still work to be done and AI has the potential to go ‘AWOL’. So how do we control the use of AI? How do we ensure it cannot be manipulated, like Tay has been?

Robo Regulation

With AI at the forefront of discussions, questions of its uses and how it will be regulated in the financial world have been raised.

“There are likely to be layers of regulation around artificial intelligence, some sandboxed for low risk and low value trading,” said Jet Lali, head of digital at consultancy Alpha FMC.

A regulatory sandbox allows FinTech firms to test new products without “incurring the normal regulatory consequences”, according to the FCA.

The FCA says its sandbox will provide better services for users, further innovation and an increased range of products and services to market.

The scheme is part of the FCA’s plans to expand Project Innovate, with proposals on how it can work with the government and the industry “to further support businesses.”

Industry participants agree that AI will be regulated, and human “oversight” will be imperative to being compliant.

Chief executive officer at financial services firm, OTAS Technologies, Tom Doris, told The Trade: “What we see emerging is sophisticated behaviours but with human oversight and the ability to override the machine at all times.”

Alpha FMC’s Lali resonated Doris’ thoughts, and said: “Some will require human co-pilots to sign off, where more scrutiny or risk is required. Organisations will still need to indemnify retail customers for losses due to bad advice (rather than bad decision making).”

Aside from regulating AI itself, it could help regulators with implementing and enforcing rules across the financial market, as Josh Sutton, global head of AI practice at Sapient stressed.

Sutton said: “From a compliance and monitoring standpoint, AI is a game-changer. It can be deployed for policing markets and ensuring illegal activity is flagged quickly to regulators – creating a more level playing field.”

Doris at OTAS Technologies echoed Sutton’s view and explained that AI could be used to ensure a safer and less volatile marketplace.

He said: “AI systems can help with exceptional market conditions by automatically recognising when the market isn’t operating normally and alerting traders proactively and removing orders from the market while the traders assess the situation.”

Regulating AI is, however, a complex task as Henri Waelbroeck, director of research at EMS provider Portware, told The Trade.

Waelbroeck agreed with Doris and Sutton, and explained it is useful for monitoring markets: “Regulating AI itself is really an unrealistic concept.

“It may however, have a place in reducing the risk of manipulation of markets by not opening doors to practices which are misleading or incorrectly price stocks.”

The complexity of AI leads some to suggest that regulators need to learn more about its processes before setting rules on its uses.

Doris explained the concept of AI can often be confused with popular culture, and regulators need to be fully aware of its capabilities.

He said: “Regulators should know more about the route of developing autonomous entitles, with clear specifications that describes the behaviour of the machine.

“People can be confused about the capabilities of AI, on both sides, people think AI can do things it can’t and can’t do things it can. The general awareness isn’t well correlated with reality.”

Human or machine?

What does the future hold for AI? Will human traders still have a place on the trading floor?

The Trade asked industry participants whether they thought AI, with its mass of capabilities, could replace the human aspect of trading altogether.

The consensus was clear – it’s unlikely.

Henri Waelbroeck at Portware explained this would depend on the size of the company, but human traders would be facilitated by the implementation of AI on the trading floor.

He said: “It realistically depends on the firm, as some may outsource their trading to machines internally.

“Larger firms will always want to have people on the floor to watch over things, but AI will enable traders to be productive.”

Josh Sutton at sapient believes AI will, instead, shift the role of those in the industry, possibly leading to less human traders on the trading floor.

He explained: “AI could possibly replace traders, but portfolio managers will be empowered.

“The role of a portfolio manager or analyst will shift dramatically, as they understand the recommendations of AI systems.”

So it’s good news for portfolio managers, but traders may face the chop?

Not necessarily…

Jet Lali at Alpha FMC explained that even though AI ‘s capabilities are game changing, particularly in the financial markets, humans will always have a role in trading.

He said: “Despite its huge potential, AI can only take us so far; when transaction costs, large data sets and speed are not the most important factors for decision making, there will still be a role for a human trader.”

Sutton at Sapient made an interesting point when asked this question, drawing similarities from the rise of computers in the financial sector.

He explained: “The financial world will indeed become more AI driven, but computers, for example, didn’t replace traders, instead they shifted the way the financial sector operates. It’s exciting and terrifying at the same time.

“The impact of computers happened over decades, but the early stages of AI suggest the impact will be over years rather than decades.”

A combination of human and AI capabilities has the potential to shift the financial landscape spectacularly, just as the rise of computers once did.

AI is still in the early stages, as Microsoft’s Tay has exposed, but as Josh Sutton at Sapient explained, AI is being developed and implemented rapidly.

For now, it seems the job of a human trader is safe, but who knows where AI could take the trading world in the near future.

OTAS clients will be able to analyze Estimize’s data and identify where the crowd-sourced consensus diverges from traditional estimates.

London based analytics provider OTAS Technologies is to deliver crowd-sourced Estimates provider Estimize’s earnings estimates data via its OTAS Portfolio Analytics App suite.

The new “Estimize Stamp” will cover a universe of 2,150 US stocks in the OTAS Core summary, and will allow users to quickly visualize where divergences occur in consensus between Wall Street estimates and those of other market experts. When used collaboratively with other Core Summary stamps, the Estimize Stamp will provide insight into the potential impact on equity prices.

OTAS clients will also have the ability to contribute their own estimates, which will enhance and deepen the coverage of the Estimize dataset in the long term.
“By integrating Estimize estimates into OTAS Core Summary, we are able to provide another unique data offering directly into our clients’ workflow for idea generation and risk management,” says OTAS chief executive Tom Doris in a statement.

Burberry shows multiple insider transactions after warning in 2012, including CEO & Chairman. The stock mean reverted and made new highs.

Machine learning algorithms can warn investors when a particular stock is going to fall by predicting likely instances of insider trading (when information that’s not in the public domain is capitalised upon by people in the know). This can be done by analysing previous occasions when company insiders did apparently well-timed trades in their own stocks, and recognising these patterns.

This might seem deceptively simple, but it isn’t, explains Tom Doris, CEO of OTAS Technologies, a London-based market analytics and machine learning trading system. While company executives are required to file details of transactions in their company’s stock, most insider trades are few and far between: a needle in a haystack.

Doris told IBTimes UK: “We look at all of the insiders, the directors of companies, and we see all of their historical transactions in their own stock. If you are the chief financial officer of Vodafone, any time you buy or sell Vodafone stock, you’re obliged under regulation to file details of those transactions. So that would include the amount that you bought or sold, when you did it and what price you got and the reason, if any, for the transaction. There is an enormous database of all of these transactions for all of the world’s listed stocks and we go and we basically back-test all of the insiders and we find the ones that are apparently good at timing their own stocks.”

G4S at the Olympics

A good example of insider trading activity detected by OTAS prior to a big price fall was when insiders at security firm G4S all started selling their stock right before the Olympics announcement that was very damaging to the company’s share price – detectable in advance because of historical precedents.

“Normal back-testing techniques wouldn’t really have uncovered those kinds of signals,” noted Doris. “But the new techniques that we apply now can take a very small number of data points and identify when there’s some suspicious activity going on or some people seem to be trading off material inside information that’s not yet public. So the next time this person buys or sells stock, we can say, ‘hey Mr portfolio manager, this guy at G4S, or at Tesco or at Pandora, or any of these other companies – every time he sold stock in the last five years it’s been very bad for the stock, therefore you should be paying attention to this transaction.’

“It’s deceptively simple in some ways. But the problem is that an insider typically will not make a lot of trades so if you use old style statistical techniques on a guy who has had two or three trades in his lifetime, it will just come back as inconclusive, even if he has gotten all three of them right. It’s not enough of a data set to say conclusively that he’s trading off inside information. Whereas, if you or I look at the price charts and see that the three times over the course of five years that he has traded the stock, it has jumped up significantly within a month of him trading and he caught three out of the five major jumps in the stock over that five year period. You and I know immediately that it looks suspicious and it looks like he got information.

There were a number of sizeable sale transactions in Lloyds shares through the summer of 2015 by various ranked insiders. The Government was in the process of disposing with part of its RBS stake in early August and it was thought that Lloyds shares (having significantly outperformed its peer) would be the funding trade.

“So a lot of it is taking things that humans are quite good at and translating that back into a new statistical methodology and using new techniques to kind of embed that domain expertise that comes very naturally to us on a single basis. When you systematise it and put it into an algorithm obviously you can run it on thousands of stocks and hundreds of thousands of transactions.”

OTAS sees opportunity in applying machine learning techniques to filter, highlight and push concrete evidence to human decision makers where there are big gaps between a historical situation; where it would not be feasible for a portfolio manager to stay on top of all of the news and all the research for a given company from 20 years ago. Doris said this is an area not really policed in any way; a lot of companies have lock up periods ahead of an earnings announcement where there is material non-public information and they prevent their employees from trading their stock. “In terms of systematic ways to identify people who appear to have an uncanny ability to get their trade timing right, we are not aware of any other system in existence that does that other than the one that we have invented.”

High Frequency Trading

Doris, who has worked for hedge funds and holds a PhD in neural networks, points to misconceptions about how AI and machine learning is used to assist people at hedge funds, or quants in general, to predict future price moves. This is partially the case on a very short time scale, but actually the people who make the most money out of high frequency trading, for instance, are more interested in predicting if somebody is going to buy stock aggressively in the next couple of seconds. “The idea of predicting what the price is going to be, or predicting what the next three months will unfold, is not really something that anybody in finance really spends a lot of time on. The ones who are very good at quant and high frequency can have predictions that go out for a couple of seconds or a few minutes at the most.

“The markets are reasonably efficient and are very good at pricing in information and HF traders have a role to play in that. It’s a very good way of getting information about a stock into the public domain; that’s how they function and as a result material information doesn’t really stay private for the length of time that it would need to stay private in order to have a prediction that would last for anything more than several minutes. It’s by virtue of the markets actually functioning correctly that those opportunities simply don’t exist.”

He also pointed out that global investment houses with billions under management have strict investment mandates which must be adhered to. They tend to be more interested in flagging risk and volatility and avoiding the torpedoes in their portfolio, than some brand new black box signal guaranteed to make money. “Somebody running a portfolio at a big fund manager with tens of billions of dollars in equities might have 100 to 200 positions in stocks and two or three of those positions in any given year are going to be down 25%, and they are the ones that really hurt the performance. The other 100, they are going to track the market more or less and you might have a little bit of skill in terms of loading up on some of the better ones, but in the long run it’s limited. It’s all about trimming the losers. So if you have better systems that can identify when there is a risk of those kind of draw downs, that’s really where the juice is and the value added.”

Hedge funds and AI PhDs

There has been plenty of ink recently about hedge funds secretly incorporating AI divisions. Doris takes a philosophical view: “I think there has always been more hype than action around this stuff. It’s a great way of building your assets and asset allocators are possibly the root cause of this; they are prone to being disproportionately impressed by a roomful of PhDs claiming that they are doing ground-breaking work.

“It’s something that allocators look for and therefore the hedge funds respond to that – whether they are doing it or not, they will say they are. And usually you can find some area of your operation which is doing something that can be characterised as AI or ML and they roll those out whenever the allocators or the investors come to town. But these days you probably need a room of PhDs just to stay on top of your risk and compliance requirements, never mind managing portfolios.”

The system also works for megacaps in the DJ industrial average

LONDON, 07 March 2016 – OTAS Technologies (OTAS), a specialist provider of market analytics and trader intelligence, today announced an enhanced product offering with 2iQ, a behavioral finance research firm that provides insider transaction data for over 45,000 stocks worldwide. OTAS Core App will redistribute on a real time basis 2IQ insider transactions combined with their unique proprietary insider star ranking to give OTAS clients real-time information on insider dealings to help drive intelligent investment decisions and focus their attention where it is needed most.

2iQ provides a variety of insider transaction data, from analyst researched transactions, filing footnotes and information on transactions related to company events. Using its in-house team of analysts, the firm captures data across over 100 data sources on more than 45,000 companies in 50 countries globally. Available immediately, OTAS clients will have access to real-time insider information through a 2iQ insider stamp, including optional alerts on insider deals that provide live updates on insider transactions.

Tom Doris, CEO of OTAS Technologies, said: “Traders today require a new depth of market intelligence and analytics to maintain a competitive edge in the market, including fast access to data on insider transactions and holdings. Our partnership with 2iQ gives our clients access to this unique data set in the most efficient and automated way and as quickly as the data is available so they can make the best trading decisions possible.”

Patrick Hable, Managing Partner and Founder at 2iQ, adds: “As a leader in the trading analytics space we chose to partner with OTAS as a comprehensive source for gauging insider sentiment at the company, industry or country level. Being integrated on their platform has created increased market visibility, greater awareness and recognition for our solution and the partnership gives us access to an intuitive analytical tool that allows our data to be displayed in the best possible way whilst also expanding its reach to OTAS’ growing client base. We look forward to building on this relationship over the next few months.”

New partnership integrates TIM Investor intelligent sentiment into the OTAS Apps

London, 1 March 2016 – TIM Group, provider of the world’s leading network for broker trade ideas, and OTAS Technologies, the global leader for next-generation market analytics and trader intelligence, today announced the integration of TIM Group’s intelligent broker analytics within the OTAS Apps. The partnership will provide mutual clients with unique, predictive, and actionable market sentiment immediately at the point of decision making.

TIM Group’s analytics have been seamlessly integrated into OTAS trade decision support services. Combining TIM Investor’s proprietary insights with OTAS open architecture means intelligent content and insight on global markets can be shared easily to the OTAS Apps in real-time, with users immediately able to take advantage of the broker information.

“OTAS clients are some of the most progressive global institutions, who expect unique information and leading edge analytics that deliver enhanced trading decision support,” said Tom Doris, CEO of OTAS Technologies. “We are committed to having an open application that integrates with our clients’ workflows and their preferred third parties. Partnering with a unique content provider like TIM Group is one of the ways we are able to do this. We are excited to deliver TIM Investor’s intelligent sentiment to our global client base.”

Colin Berthoud, Co-Founder of TIM Group added: “TIM Group has 4,000 broker sales trade idea contributors at over 300 firms globally. By partnering with OTAS we are now able to provide our shared clients with unparalleled insight and analysis, that will significantly and positively impact their decision making. We are looking forward to working with the OTAS team to deliver best-in-class services to our clients.”

The era of artificial intelligence means machines could conceivably be better than fund managers at investing clients’ money. It may also level the playing field between large and small firms, finds Kit Klarenberg.

Last year, mainstream commentators declared the era of artificial intelligence (AI) had arrived. Major investments in AI were announced by Facebook, Google and Microsoft, with the promise of ground-breaking consumer applications to follow.

Financial professionals may have wondered what the fuss was all about. The technology is nothing new to financial services – US institutional investors were using embryonic AI algorithms to reduce their workloads in the 1980s and tech giants have long targeted the industry with innovative analysis software.

Josh Sutton, global head of AI at technology consultancy Sapient, sums up the appeal of AI. “Rather than relying on an army of analysts to locate, digest and curate relevant data, AI software can identify and assimilate all pertinent information, and store it for future access,” he says.

A computer can analyse data faster and with a greater degree of accuracy than humans can, for a fraction of the cost.

“The breadth of information that can be considered by asset managers is also greatly increased,” adds Sutton.

PREACHING TO THE CONVERTED

While the opportunity to lower costs is obviously attractive, AI is also favoured as a way to help asset managers overcome serious informational challenges.

In the 21st century, asset managers face a daily barrage of thousands of financial news stories, hundreds of emails and research documents and miles of transaction data.

Not only can this data overload produce a confusing and impenetrable picture, it theoretically advantages the biggest asset managers, as they can afford more (and perhaps better) analysts. The growth of AI may spell the end of this imbalance.

“The universal adoption of AI across the industry will level the information playing field – it’ll be difficult for any asset manager to inhabit a position of privilege, and will make clear which active strategies do outperform,” says Tom Doris, chief executive of financial AI platform OTAS.

Modern AI can be used to recognise patterns, weigh probabilities and make predictions, and do so consistently and objectively. While it’s impossible for humans to apply investment principles unfailingly across the entire universe of potentially investible securities, machines can.

Moreover, while there’s always the risk of humans falling prey to ‘gut feelings’ and other irrational impulses, machine thought processes aren’t bedevilled by such deficiencies.

There’s growing recognition of the opportunity presented by AI at the highest echelons of the industry. In the past 18 months alone, Goldman Sachs has invested heavily in financial analytics firm Kensho, BlackRock has purchased digital investment manager FutureAdvisor, Invesco has bought AI advisory platform Jemstep, and Charles Schwab and Vanguard have both launched AI offerings.

It’s perhaps surprising that more asset managers haven’t embraced the technology. After all, what asset manager wouldn’t want to enhance its analytical skills, make better informed and more varied investment decisions, deliver better returns and reduce costs?

SELF-IMPROVEMENT OR SELF-DESTRUCTION?

This reticence may be attributable to technophobia on the part of asset managers. For, despite the many positive benefits generated by innovation, tech proliferation also presents challenges.

Professionals in many sectors fear machines will render their jobs surplus to requirements in the not-too-distant future, and these concerns may be well founded.

For instance, a 2015 Boston Consulting Group report forecast that 25% of manufacturing roles will be replaced by AI systems within the next decade. Should asset managers also fear for their jobs? Could AI eventually negate the need for humans in asset management entirely?

Frances Hudson, global thematic strategist at Standard Life Investments, thinks not.

“Humans and machines bring different strengths to the table – humans score highly on innovation, interpretation, adaptation and judgement, while machines are consistent, quick, agnostic and able to cope with complexity,” she says.

“We try to combine, rather than replace, human expertise and judgement with advanced computer applications. AI has enhanced our understanding and analysis of market behaviour, adding to our range of predictive tools – and we find digital analysis of markets complements traditional stock selection, asset allocation and risk management methods.”

Doris of OTAS likewise doesn’t foresee AI superseding humans in asset management.

“At most, I envisage AI in asset management reaching the stage of the self-driving car – while predominantly automated, humans will always need to be there to take over at some stage or other, and will always be a necessary component in the investment process,” he says.

This is not to say that some don’t have cause for concern. Hudson concedes that AI does pose a threat – to poor active managers. But while few will lose sleep over the departure of closet index trackers from the market, staff at the lower end of the asset management matrix may also have reason to fret.

“People should think about the functions they perform on a daily basis, and what fraction of those responsibilities genuinely demands human input,” Doris says. “If 90% of your work consists of following rules, and could easily be replicated by a machine, then it’s highly likely you’ll be replaced by a computer in future.”

Nonetheless, the vision of human and machine working in harmony is commonly held. It’s a view espoused by Steve Young, chief executive officer of investment consultancy Citisoft. “While it’s inevitable that AI will play an increased role in asset management going forward, it’s much more likely to be a supplement to existing investment processes than a replacement,” he says.

In any event, while AI technology is developing apace, the point at which it could feasibly supplant traditional managers entirely is a long way off from a capability standpoint.

This lack of ‘way’ is matched by a similarly non-existent ‘will’ – Young believes conservative attitudes in the asset management sphere could mean the pace of change is slow, and total replacement of humans remains off the agenda completely.

MANAGING CHANGE, NOT CHANGING MANAGERS

However, internal resistance to change could present an issue in itself. Sutton says managing the integration of AI will be a major challenge in years ahead; firms that don’t modernise quickly enough risk losing a competitive advantage, but those moving too quickly risk stoking staff fears of being usurped.

The best response to such hostility, Sutton says, is to reframe the debate in terms of promise, rather than prospective threat. AI may take away some functions from an employee’s remit, but it frees them up to pursue others.

For instance, once liberated from onerous analysis duties, asset managers are free to consult with clients at length – a significant benefit at a time when retail and institutional investors both demand asset managers to be more accessible, and quality of service can be critical to retaining and gaining clients.

Furthermore, Sutton points to the stratospheric rise of taxi app Uber as an example of how disruptive tech can serve to broaden customer bases.

“Many cabbies fear Uber, as it means greater competition and lower revenues – but, ultimately, the growth of Uber has also led to a significant net increase in the number of people using car services,” he says.

“There’s no reason to think AI technology won’t make investing, and investment funds, more accessible in the same way.”

The future promises more innovation to come. While financial AI is primarily quantitative at present, a select few firms have begun to develop and use qualitative ‘machine-learning’ technologies, which can make and implement investment decisions.

It appears that for asset managers using AI effectively, market share is up for grabs. Although, if Doris’s forecasts are correct, analysts should perhaps consider expanding their skill sets post-haste.

ComputerworldUK visited Thomson Reuter’s London office in Canary Wharf to see how it is working on the user experience (UX) of its increasingly popular trading platform Eikon, and how it’s opening up to app developers.

Following a shaky start to life, Thomson Reuters’ Eikon trading data platform is steadily growing in popularity. Now that the teething problems have apparently been solved the financial and risk department is focusing on smoothing out the user experience through extensive user testing and a more open approach to third party development.

App development suite
Albert Lojko, global head of desktop platform (Eikon) financial and risk at Thomson Reuters told ComputerworldUK that companies have been building apps on top of Thomson Reuters’ APIs for years, but only now are firms starting to really see the benefit in doing so.

“When you think about the financial technology overall it’s been kind of very closed and proprietary bundled,” he explained, “and that at some level stifles innovation.” Lojko’s solution was to create “an ecosystem for the financial markets where we could bring a lot of our intellectual property to bear”.

The software development kit is simply a case of Thomson Reuters opening up its internal tools and APIs to clients and third parties like fintech firms, who can develop apps that display as native on the Eikon screen, either publicly or to specified employees or departments.

“There’s this explosion of data, regulation, more of an appetite to do higher level analytics and machine learning. Those were all things we are investing in quite heavily and the idea of externalising that to help our customers get better insights into the marketplace we thought was really valuable,” says Lojko.

Case study
Lojko explained how one customer has utilised the app development capabilities: “One particular client was generating all of their internal research and they had built an independent portal for users to access that. Then they had a different desktop for day to day trading and traditional market data activities and what was difficult was that there was no contextual linking between that internal research and benchmarking data externally. So what they were able to do very quickly was build and integrate their own proprietary research and forecast data with Eikon in a seamless way.”

As you would expect from a customer that Lojko wouldn’t even name, there was a concern that this proprietary data becomes less secure if it was to be integrated in this way, but the hybrid nature of the development suite means data never leaves the client’s data centre.

OTAS Technologies is one of the first firms to publicly utilise the app studio. Vanraj Dav, head of development operations at OTAS Technologies said: “Developing using App Studio in Eikon was painless and has resulted in seamless access to OTAS data and analytics for our customers – all without having to leave their Eikon workflow.”

Design Lab
Jesse Lewis heads up the user experience (UX) team at Thomson Reuters and has been busy testing user satisfaction with the platform, both in the design lab in the Canary Wharf office and at the client using a mobile lab.

As Lewis puts it: “We try and create an environment that a trader might have, without all of the distractions and noise and abuse and we integrate that with various sensors and systems that allow us to elicit as much information as possible from every integration we have.”

“The challenge for us is to take all of the content and data analytics that we are responsible for presenting to the customer and presenting it in a consistent format. So the idea of this space is to help us to do that. To keep, through iteration and consistent upgrades, improving that engagement and experience.” Lewis will test multiple employees using a variety of sensors, including eye tracking, across a range of devices and modalities to ensure a consistency of approach.

Lewis is seeing a change in the way a trading floor looks and the way people engage with their display: “It’s not surprising that the urban myth is you go onto a trading floor and and someone hasn’t changed their display for 12 years,” Lewis observes, “but you have the new trader that has come through education and they are so keyboard savvy, they are using tab commands to navigate every window and they never touch the mouse.”

Eikon has been working hard on its UX since 2013, when it introduced better visualisation and natural language search capabilities. The aim now is to develop the platform with an agile methodology, moving “from big monolithic changes, to small discrete moves,” says Lojko.

Playing catch up
The traditionally opaque financial sector is still playing catch up when it comes to open API strategies and UX though. As Todd Latham, VP for marketing at fintech startup Currency Cloud, told Techworld last year: “Banks have not traditionally been ‘sharers’ when it comes to technology, but as the financial services industry continues to evolve, they will become increasingly reliant on the flexibility that comes with use of APIs.”

Thomson Reuters still appears to be lagging behind Bloomberg in the race to dominate traders’ desktops, with Eikon reporting 123,000 customers back in 2014, compared to Bloomberg, which claims to have 320,000 users worldwide. However, as analyst Rik Turner of Ovum sees it: “Clearly initiatives like this one, facilitating app development on their platform, is a positive one.”

Bloomberg has been engaging with the developer community since November 2012 with its App Portal, bringing together its own trading data APIs within the Bloomberg Terminal platform. Thomson Reuters will be hoping that its own all-in-one approach within Eikon will help drag traders away from its rival platform.