The Human Element in Quantification

I enjoyed Felix Salmon’s piece in Wired titled “Why Quants Don’t Know Everything.” The premise of the piece is that while what quants do is important, the human element cannot be ignored.

The reason the quants win is that they’re almost always right—at least at first. They find numerical patterns or invent ingenious algorithms that increase profits or solve problems in ways that no amount of subjective experience can match. But what happens after the quants win is not always the data-driven paradise that they and their boosters expected. The more a field is run by a system, the more that system creates incentives for everyone (employees, customers, competitors) to change their behavior in perverse ways—providing more of whatever the system is designed to measure and produce, whether that actually creates any value or not. It’s a problem that can’t be solved until the quants learn a little bit from the old-fashioned ways of thinking they’ve displaced.

Felix discusses the four stages in the rise of the quants: 1) pre-disruption, 2) disruption, 3) overshoot, and 4) synthesis, described below:

It’s increasingly clear that for smart organizations, living by numbers alone simply won’t work. That’s why they arrive at stage four: synthesis—the practice of marrying quantitative insights with old-fashioned subjective experience. Nate Silver himself has written thoughtfully about examples of this in his book, The Signal and the Noise. He cites baseball, which in the post-Moneyball era adopted a “fusion approach” that leans on both statistics and scouting. Silver credits it with delivering the Boston Red Sox’s first World Series title in 86 years. Or consider weather forecasting: The National Weather Service employs meteorologists who, understanding the dynamics of weather systems, can improve forecasts by as much as 25 percent compared with computers alone. A similar synthesis holds in eco­nomic forecasting: Adding human judgment to statistical methods makes results roughly 15 percent more accurate. And it’s even true in chess: While the best computers can now easily beat the best humans, they can in turn be beaten by humans aided by computers.

Very interesting throughout, and highly recommended.

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.

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(Hat tip: Paul Kedrosky)