A School for Quants

This is an interesting piece in The Financial Times about a “school for quants” (at the University College London). There is a general profile of the quant business, and then the articles profiles a few students working at the center and what they’re doing with their skills (not all of it is finance related):

The Financial Computing Centre at UCL, a collaboration with the London School of Economics, the London Business School and 20 leading financial institutions, claims to be the only institute of its kind in Europe. Each year since its establishment in late 2008, between 600 and 800 students have applied for its 12 fully funded PhD places, which each cost the taxpayer £30,000 per year. Dozens more applicants come from the financial industry, where employers are willing to subsidise up to five years of research at the tantalising intersection of computers, data and money.

As of this winter, the centre had about 60 PhD students, of whom 80 per cent were men. Virtually all hailed from such forbiddingly numerate subjects as electrical engineering, computational statistics, pure mathematics and artificial intelligence. These realms of knowledge contain concepts such as data mining, non-linear dynamics and chaos theory that make many of us nervous just to see written down. Philip Treleaven, the centre’s director, is delighted by this. “Bright buggers,” he calls his students. “They want to do great things.”

In one sense, the centre is the logical culmination of a relationship between the financial industry and the natural sciences that has been deepening for the past 40 years. The first postgraduate scientists began to crop up on trading floors in the early 1970s, when rising interest rates transformed the previously staid calculations of bond trading into a field of complex mathematics. 

An example of how Ilya Zheludev, one of the students profiled, is applying his skills:

Ilya Zheludev, one of the students from the meeting, showed me his study of 500,000 internal Enron emails, which were released following the collapse of the energy company in 2001. Zheludev’s sentiment analysis showed a spike in emotion among employees – both positive and negative, a massive, contradictory shiver – in April 1999, a few months before the company’s stock began to take off on its exponential (and fraudulent) trajectory.

Picking up on such bubbles of emotion as they emerge (around a company, for instance, or a government) even in such murky waters as Twitter, or Facebook, or the website of the Financial Times, has an obvious allure to individual investors trying to stay ahead of the market. At least one London-based hedge fund, Derwent Capital, now trades purely on social data, mined in this way.

Read more here.


(Hat tip: Paul Kedrosky)

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