Data Race Accelerates
Matthew Hodgson, chief executive and founder of Mosaic Smart Data said the pace of change is so aggressive that the fintech, which provides real-time data analytics for capital markets, needs to form partnerships and has hired a scientific advisor.
Last week Mosaic said in a statement that it had hired Rama Cont, Oxford University professor of mathematical finance, as scientific advisor. Cont was appointed professor at St Hugh’s College Oxford last year. He previously held the chair of mathematical finance at Imperial College London and has 20 years of experience working with the financial markets.
We’re proud to announce that the renowned Oxford University Professor of Mathematical Finance, Rama Cont, is joining Mosaic Smart Data as Scientific Adviser. Together, we’re utilising the latest academic research to unlock new insights in financial data https://t.co/boAp1zp4CX pic.twitter.com/UtjERhuWMg
— Mosaic Smart Data (@MosaicSmartData) June 5, 2019
Hodgson told Markets Media: “The pace of change is so aggressive that it is essential for us to form partnerships and collaborate, such as we have done with the European Space Agency.”
Last year Mosaic Smart Data and ESA Business Applications, the European Space Agency’s commercial arm, announced an alliance for the fintech to explore applying machine-learning models developed and used by ESA to fixed income, currency and commodity markets.
MSX, Mosaic’s platform, can generate real-time analytics on any set of fixed income, currencies and commodities from both voice and electronic trading. Hodgson launched the company after finding it impossible to get a consolidated view of data on all trades with one client across all products in his previous roles at Deutsche Bank and Solomon Brothers.
“Rama has deep experience and provides access to the newest techniques in academia and the best talent to bring into the company, so it is a natural fit,” he added.
Cont said that in the last two decades financial services firms have been flooded with a large quantity of messy, heterogeneous data and they may not have yet mastered the tools needed to extract useful information from this data. For example, datasets have multiplied 1,000 times in the past decade which was not expected.
“Combining market data with other sources such as internal data or text from newsfeeds on a large scale requires automation and is very challenging,” Cont added. “Firms know they may be sitting on a goldmine, but the gold is not easy to extract.”
The professor continued that in the past decade there has been enormous progress in machine learning and deep learning.
“Mosaic has expertise in data and we will focus on applications that are realistic and meaningful,” said Cont. “Anomaly identification in financial data allows firms to avoid risk, monitor behaviour and also provides sales opportunities.”
Hodgson added that extracting value from data is becoming a key competitive advantage. He said: “The collaboration with Rama will focus on transaction cost analysis and anomaly detection which also provides use cases for exchanges, ECNs and custodians.”
In April Mosaic Smart Data completed a $9m (€8m) investment round co-led by CommerzVentures and Octopus Ventures and which includes JP Morgan, an existing investor and client. Hodgson told Markets Media at the time that the funds will be used for product development as it expands into equities, for R&D into machine learning to provide more analytics, and to expand in the US and Asia.
“Mosaic initially focussed on Tier 1 sell side firms and is now expanding into regional firms and the buy side,” added Hodgson.
Buy-side firms also need real-time analytics to examine their interaction with brokers and clients in order to improve service and profitability as margins are under pressure.
A new survey found that European fund managers have decided that, rather than just cost-cutting, they need to grow by gathering more assets and providing more alpha than their competitors. The primary approaches to achieving strategic priorities were achieving automation, 49%, and the creation of a golden source of data / investment book of record (42%) .
David Weaire, head of operations, investment at Investec Asset Management said in the report: “Having accessible, standardised data available to those making daily investment decisions is a key tool to optimise how these choices are made and improve the chance of finding alpha.”
WBR Insights and SimCorp, an asset management technology provider, surveyed heads and directors of operations from 100 buy-side firms across the UK and Europe with more than €10bn in assets under management in the second quarter of this year.
Hans Otto Engkilde, managing director and senior vice president SimCorp UK, Northern Europe and Middle East, said in the report that the buy-side front office needs to be able to work from uniform, accurate data, which can be understood and evaluated by every link in the investment chain.
“Once centralised and standardised, this data can be used to carry out increasingly technical performance measurement analytics that can show managers the true value of their trades – either good or bad,” added Engkilde. “Having total control of the design and access of these programmes is something asset management chiefs now realise can add to performance as well as smooth internal operations.”
The study continued that this data hub can help managers explore how artificial intelligence, machine learning and other emerging technologies can open up new opportunities.
“In a market that has rediscovered its will to win, the agility and quick response times that can be achieved by using automation and sleek, unified systems will separate the winners from the losers,” said the survey.
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The aim is to help the buyside identify addressable and non-addressable liquidity.