The Gateway to Algorithmic and Automated Trading

Alpha in the machine

Published in Automated Trader Magazine Issue 35 Winter 2015

Marco Fasoli started using artificial intelligence and machine learning systems while completing a PhD in natural sciences at Cambridge University. He went on to become a co-founder of Titian Global Investments, applying advanced predictive technologies to financial markets as the firm's managing partner. And he's since acquired another title: co-founder of QuantBridge, a joint venture between Titian and Thayer Brook aiming to become a strategic hub for quant talent. Automated Trader talks to Fasoli about the systems that guide QuantBridge's trading decisions and finds out about his future plans for technology development, which he believes could cause a major shake-up in the retail investor space

QuantBridge's Marco Fasoli talks to Automated Trader at the Royal Automobile Club in London, UK.

AT: How did your scientific background lead you to financial innovation in markets?

Marco Fasoli: Doing my PhD was really the first time I became exposed to artificial intelligence and machine learning, primarily in the context
of looking at large bodies of data and trying to identify certain kinds of signals from a lot of noise.

Subsequently, I spent a number of years in the investment banking world and realised that what was artificial intelligence and machine learning in my university and academic days at that time had become mainstream and was increasingly being applied in industry to look at a variety of mission critical phenomena - including weather forecasting, real-time information retrieval, the management of very complex systems, like for example electricity systems.

I came across a large number of software companies that had developed very interesting applications that were being successfully rolled out in the world of industry and, having become familiar with capital markets, it became obvious to me that there seemed to be a gap in the financial industry in terms of some of these innovations that had been very successfully deployed.

So I got together with a group of people who I have known and worked with for many years, who had experiences ranging from computer science, operational research, financial engineering and capital markets and what we wanted to do was to try and see if we could refine some of those technologies and apply them to financial markets.

AT: How do you compare to the other few companies in this space?

MF: One of our characteristics is that our ideas and thinking process really comes from outside the financial industry, from science, operational research and computer science.

In that sense, we think that is quite an important distinction relative to many other systematic or quantitative-based approaches that have emerged more from the world of finance. We do think it is important to have a clean-slate mentality when you are looking at these opportunities with less of the baggage that can be typical of people that have been steeped in the financial industry for many years.

For example, when we look at managing risk, we look at risk in a completely different way than has been traditionally used by finance professionals. When we started to first look at opportunities

of deploying these technologies in the financial industry, we were struck by the fact that the most prevalent way of managing risk in financial markets is by looking at variations of returns, of standard deviation and volatility-based measures.

AT: Why was that a surprising discovery?

MF: If you think of real industry, say an electricity network or grid, what really matters for them, and ultimately reflecting what matters for the end customers, is the risk of a blackout. You don't really care how the volumes or throughput of electricity changes over time. What you
really want to do is minimise to the maximum extent possible the risk of a blackout. We look at financial markets in a similar way.

From a risk perspective, the thing that is most important for investors is: how much money am
I going to lose? Investors do care about variations with respect to the downside, they want to make decent money out of the strategy they are invested in, of course, but as importantly, they don't want to lose money.

AT: How is that reflected in the design of your risk systems?

MF: The way we constructed our risk management systems is that they minimise capital loss risk as opposed to what most risk management systems in financial markets do - minimise the variations of returns or the volatility. We have taken a completely different approach. In other industries where there are some mission critical applications - how is risk managed, what are the best ways of reducing that risk and what is the risk that really matters for the customer? That is why the example of an electricity blackout makes sense, because in the end that is what investors or clients care for. They want to be able to put the kettle on when they need and don't want to lose power...

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