"I CALL IT the revenge of the nerds," says Andrew Lo, a professor at Massachusetts Institute of Technology, and director of the university’s Laboratory for Financial Engineering. Lo is referring to the growth in demand for statisticians, mathematicians, physicists and astrophysicists from hedge funds, asset managers and banks. As one hedge fund quant employee advises a student enquiring about a future career in finance on a US website: "Forget taking economics. Stick to mathematical modelling and programming."
It is an interesting transformation. "In the US, Harvard and Yale have tended to be the suppliers to the finance employment market but now universities such as MIT and Caltech that produce technologists are beginning to have an advantage," says Lo. He is bound to be a little biased, but his statement is supported across Wall Street – quantitative investment strategies, often involving advanced computer programming and mathematical modelling, are becoming an increasingly common feature of the financial landscape.
The demand for staff who can develop and monitor complex algorithms in the search for alpha, and the attractive fees it can bring, is probably at its fiercest among hedge funds. Kyle Ramkinssoon is a principal at US-based recruitment consultant IJC Partners. Most of the firm’s clients are hedge funds recruiting for quant-oriented talent. "There is a huge shortage of talent and it is very common for candidates to receive multiple competing offers, as well as counter-offers," Ramkinssoon says. "Our client looks for candidates with degrees in computer science, mathematics, statistics and physics. Some other degree categories are considered, but they have to come from top schools and the applicants must have high GPAs and high standardized test scores.
"On the entry side for the quant developers and quant analysts, there is a lot of programming involved, hence the demand for the IT skills – they develop the models, work with the traders, portfolio managers and other quants to really drill down on these models. They are finding staff by going directly to the schools at the freshmen level and earlier and staying in touch with these stellar candidates. Others are doing on-campus recruitment, tapping their vast alumni network or using multiple recruiters to find them talent.
"There is a tremendous amount of competition as the big funds are looking to add staff at a huge clip. We’re working with a fund managing over $20 billion and they’re looking to add 43 additional head count – four traders, and the rest quant developers and pure IT development."
The rise of quant
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Did you know?
It is estimated that 40 exabytes (4x1019) of unique new information will be generated worldwide this year. That’s estimated to be more than in the previous 5,000 years
The amount of new technical information is doubling every two years. For students starting a four-year technical degree, this means that half of what they learn in their first year of study will be outdated by their third year of study.
That information is predicted to double every 72 hours by 2010.
Predictions are that by 2013 a supercomputer will be built that exceeds the computational capability of the human brain.
By 2023, it will only take a $1,000 computer to exceed the computational capabilities of the human brain.
While technical predictions beyond 15 years are hard to make, forecasts are that by 2049 a $1,000 computer will exceed the computational capabilities of the human race.
Source: Did you know? a presentation by Karl Fisch |
Industry participants point to the increase in the number of equity market neutral strategies being run as evidence of the growth in quantitative hedge funds. It serves as only a rough guide, and can encompass funds using quantitative screening only, up to the high-frequency traders with developed buy/sell models executed by computers. The high-frequency traders that are considered pure quants, such as DE Shaw, Renaissance Technologies and AQR, are estimated to number only about 20 worldwide. But given that nearly all quantitative managers need to be in the equity market neutral space or long/short equity space for liquidity purposes if relying on models, it is a good litmus test.
According to data from HFR, the assets under management of equity market neutral funds increased by almost 50% from the beginning of 2005 to the end of 2006. These figures do not include some of the largest quant funds. There are several reasons for the increase, at the heart of which lies investor demand. The greatest attraction for investors is that quantitative-based investing can eliminate manager emotion. Lo says: "Investors want a degree of stability, and putting portfolio management into the hands of a process rather than a person can remove some potential errors. For institutional investors with a fiduciary responsibility, there is going to be more comfort with a process than with allocating to an individual."
Investors also appear to be becoming less sceptical about the so-called black-box approach of quantitative strategies. Although they may not fully understand how an algorithm is developed, they can at least understand the principle behind it. Lo says that large quantitative houses have made strides to provide more transparency and to educate the investment community, so that "black boxes are becoming glass boxes". Alex Greyserman, CIO at hedge fund Hite Capital, and an adjunct professor at Columbia University, says: "I think people are beginning to see that a manager who is developing or selecting a quant model to pick stocks is no less transparent than a manager that picks stocks based on proprietary research or experience."
The strong performance of quantitative hedge funds has naturally aroused further interest. Sunil Pai, co-founder of ST Capital Partners, launched a quantitative hedge fund in February. "With 70% of the world’s money being invested with humans using fundamental analysis, there is a lot of identifiable alpha out there so long as you can see into the data," he says. "With the tech boom of the last 10 years, there are now machines fast enough to crunch this data which, combined with the right mathematics, can find patterns off which managers can trade using a quantitative approach."