Hunger for excess returns drives systems development
With unprecedented volatility in financial markets, funds and hedge funds are battling for additional returns to attract business, increasing the buyside’s hunger for new algorithms. This in turn is driving a search for technology that can assist in the selection and creation of portfolios.
Traditionally, alpha generation, returns above an index, was the domain of quantitative analysts who would research and test the theories and pass them on to portfolio managers. Today the quantitative analysts are moving into trading, where their technical skills and mathematical know-ledge can be applied.
John Jay, an analyst at consultancy Aite Group, said: “Today’s quants are no longer development geek-types who remain hidden in the background. They are increasingly being pushed into the front office, taking on the roles of traders and portfolio managers.”
As these front-office duties fuse with quantitative analysis, the demand for a platform that can reduce development errors and save time to market is expected to grow. A niche of technology companies is responding to this demand with platforms designed to help quantitative research analysts design and test theories.
Aite Group puts the market for these alpha-generation platforms at $120m (€76m) by the end of 2011; this is 10 times the estimated spending in 2006. Quantitative investment analysis accounted for 12% of all global assets under management last year, representing $6.65 trillion, and Aite expects this to rise to 14% by the end of 2010.
Given certain short-term strategies remain effective for only three months, rapid construction and implementation of alpha models becomes more urgent, said Jay.
Louis Lovas, technical fellow at algorithmic platform vendor Progress Apama, said: “Strategies have a fairly short life in the market. They are like the fashion industry where fads come and go so quickly. Once everyone does it – like smart order-routing or FX aggregation – it is not fashionable any more and it loses its alpha.”
Using a quant platform can help to speed the process. Until the advent of these platforms, programmers and researchers had to use a “patchwork of Excel and brute force”, according to Jay. “It was very difficult, especially on the data side.”
One of the most important functions of the quant platform is the collection, cleansing and presentation of market data. Qi Zeng, a portfolio manager for Boston-based Acadian Asset Management, said it was time-consuming but necessary to find, cleanse and normalise data for internal consumption. ”
Zeng uses ClariFi, one of the first quant platforms to enter the market. Gioel Molinari, managing director and founder of ClariFi, said: “Using quantitative analysis in fund management was becoming more mainstream. We thought it was the way money management would go, because it is more efficient and augments the process.”
Zeng said: “ClariFi is one part of the toolset. I use it to visualise the complexity of the data flow into my procedures. I can drag and drop data into an iconised object and see immediately. It is often very efficient in prototyping ideas.”
Zeng said the visual approach can help her to catch errors in her programming. “Sometimes it is nice to step away from dense code, drag and drop the data and do some error checking. I can look at a slice of data over time.”
But Steve Smith, chief executive of California-based technology firm 4th Story, said: “The more sophisticated ideas cannot always be expressed in a drag-and-drop environment.”
del.icio.us
Digg
Newsvine
reddit
StumbleUpon
