"It's super-exciting to be a quant in 2018-2019. Everyone is hiring"
If, in late 2018, you are contemplating going into quantitative finance, then keep contemplating. Panelists at the recent Quant Conference in London say you’re unquestionably on the right track.
“It's super-exciting to be a quant in 2018-2019,” said one panelist (a hedge fund researcher who formerly worked for a large U.S. bank). “Everyone is hiring.”
Quants’ enthusiasm for being quants might be dismissed as self-love. Except, they're not the only ones in the zone. Goldman Sachs has proclaimed its pursuit of strats (its own personal brand of quant technologists), while at another conference the week before, the head of recruitment for another major U.S. bank told us the bank plans to double its London strats team within a few years.
Laurent Laizet, CIO of Qube Research and Technologies, the systematic hedge fund that spun out of Credit Suisse last year, says the demand for quants is being driven by the, "explosion of alternative data." Both hedge funds and banks are mining datasets looking for an edge. "It's become standard to have a data factory and you need the big data expertise to manage it," says Laizet. "There are now a lot of IT people and quants just managing the stats."
The upshot is that trading floors which were becoming devoid of humans are being repopulated. “Ten years ago, you had trading floors filled with people,” said a hedge fund researcher at the conference. “Then you had trading floors filled with machines, and then today we’re back to having a lot of people again – except the people are now an army of quants looking at data."
There are no stats for quant employment globally, but Qube alone has recruited 55 people globally this year, of whom 15 are based in a new Mumbai office, with the rest split between London, Paris and Hong Kong. J.P. Morgan now runs an internship and a full-time programme for data science and machine learning students with degrees in maths, science, engineering, and computer science. HSBC has begun offering a training programme in data science and engineering in London and Toronto. And hedge fund Two Sigma is targeting students at Imperial College, Stanford and elsewhere for its quantitative internships in London and New York.
In the next few years, banks' demand for quants has the potential to increase further as more fixed income trades are placed electronically. “On an equities trading floor nowadays, you might find 10 traders and 90 quants,” one ex-derivatives trader told the conference. On fixed income trading floors, she said quants have yet to proliferate to the same extent, but that this will change with electronification and the related increase in trading data. "We will have more and more people with a quantitative background working in sales and trading," agreed the head of one U.S. bank's quant group. "They may not have a PhD, but this trend is going to accelerate massively."
Unfortunately this does not mean that you can walk into a quant job. At Qube, Laizet says they're "inundated with high quality CVs," as up to 20 people a week apply. Most of the successful candidates have PhDs and are "creative," says Laizet - they have original ideas on how to analyze and apply data.
If you're reading this article, you might be inclined to rush out and get a quantitative qualification. However, the danger is that quants' time in the sun will be fleeting. Just as traders and brokers were displaced by algorithms and electronic trading systems, so human quants stand to be displaced by machine learning programs.
The good news is that there's little sign of this so far. "Machine learning [to drive investments] is a huge bubble," one senior quant told the conference. "It's been around since 1992 and the return on investment is poor...The most useful thing for quant funds is the opportunity to use alternative data."
The managing director of another quant fund said they've tried using machine learning programmes for big data analysis but that the results were less "robust" than with human research techniques and therefore needed to be treated with care. "Machine learning is more about aiding the velocity of research – we are in a race with our competitors and we need to be able to move faster and to generate signals faster than anyone else," he said. Machines are part of this, but humans are irreplaceable.
"If you want to look at esoteric asset classes from big data vendors, you need a lot of people to do it," agreed the hedge fund researcher at the conference. "We're not in the days when you just had two Russian scientists any more."
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