Quant Insight Secures Funding to Expand in US and Develop Macro Analytics Platform

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Quant Insight (Qi), a London-based macro research firm, has completed a funding round that will support expansion in the US and Asia, and stimulate product development. Over 80% of the multi-million dollar funding came from Qi’s clients, including sizeable investments from Alan Howard (Brevan Howard Asset Management) and Jens-Peter Stein (Stone Milliner Asset Management).

Qi founder Mahmood Noorani says: “Our number one priority is to expand marketing in the US and increase our presence there. A third of our clients are in the US, our sales lead times are much shorter there, things generally move faster and we’ve sensed a greater openness to machine learning delivered data than we have in Europe. We will also be adding additional resources to cover our clients in Asia. The second use of the funding will be for a pipeline of innovative products coming out over the next three to four years. We will always look into improving our product, UI and functionality. As for now, we are in expansion mode.”

Aiming to untangle the web of macro factors influencing asset prices, Qi rejects conventional managerial correlation analysis, which Noorani describes as ‘often misleading’ and ‘not rigorous. Instead, the Qi platform uses machine learning models and proprietary algorithms for a fact-based approach to analytics that is able to isolate the factor that affects a certain asset in a specific way, with specific models created for each asset class. Qi has also developed solutions for equity baskets and index construction.

“There is a huge universe of constantly shifting macro information, with a huge number of factors at play,” says Noorani. “The problem is that all these macro factors are themselves highly correlated. You can’t untangle them to strip out the independent effect of one variable with the standard analytical technique.” The Qi platform’s capabilities include the ability to empirically reveal key market drivers, optimise trade selection, quickly spot regime shifts, build portfolios with specific macro characteristics and identify valuation anomalies. “Essentially what we are saying is this, we know it’s difficult to predict asset price, but if you understand what the price is reacting to, you stand a better chance,” comments Noorani.

He adds: “The other thing you can do is turn the whole thing on its head and allow a portfolio manager who, for example, believes global growth will be weak, to say ‘go and find me the asset that’s most sensitive to global growth’. Qi can then screen all asset factors to show securities that will be most affected by global growth and show the manager how to convert his view into specific investments.”

Matt Frame, a trader and Qi client at 3G Capital, says: “Qi’s analytics are a breakthrough in constructing thematic equity baskets and in defining the macro characteristics of single stocks as well as overall equity portfolios. As first-to-market for quant macro analytics, I see rapid growth potential for Qi across the industry.”

The Qi approach to macro analytics has pushed its paying subscriber base to over 150 clients, including the Royal Bank of Canada, several asset managers with more than a trillion dollars in AUM, and around 25 other hedge funds in the three to 20 billion AUM range. Noorani says about 100 more companies wanting to use Qi services are in the pipeline.

Alan Howard, founder at Brevan Howard Asset Management, says: “Qi helps untangle complex markets and identify what is driving asset prices. I can see many applications for Qi’s technology and am pleased to support the company in its expansion.”

Qi investor Jens-Peter Stein, co-chief information officer and founder of Stone Milliner Asset Management, concludes: “With so much attention on alternative data sources, Qi focuses on better understanding the data we already have - the data that really matters for markets.”