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Interview with H. Eugene Stanley

Interview with H. Eugene Stanley (07:12)
So let me ask a first question. Can a physicist contribute to this sort of question, having the mind of a physicist by training? First of all, the proof is in the pudding. Anybody can have any idea of what a physicist might do, but until he or she does it, they have no certainty if it will work, because in principle, it should not work. There is no physics in economics. Economics is about money controlled by human beings who have free will, and physics is about electrons and protons and billiard balls and cannonballs and everything else, which does not have a brain or anything else. So in principle, nothing.
However, a part of physics deals with fluctuations, and the fluctuations in physical systems are very, very, very more complex than the fluctuations that are typically taught in a maths course, and there’s a reason for that. Maths courses like to teach things that can be calculated by maths students, and these things require assumptions. Everything requires assumptions. Whereas a physicist doesn’t worry so much if you can’t treat it mathematically. We want to know the answer. The answer may just be a table of numbers or it may be some approximate formula, but the most important thing is that we’re not striving for mathematical perfection.
We’re striving instead to find the right answer, and that answer is an answer that economists normally would not think about. So basically, this means you start with data, and over the last two decades, you and your group have produced a fantastic series of papers in terms of quantifying financial market fluctuations. So where did all of this start? Where did you get your first data sets from? That was 20 years ago. A post doctoral student named Rosario Mantegna from Palermo, Sicily, who was not an economist– in fact, he was an experimental solid state physicist, but he was curious about economy, and he wanted to analyse a big database.
I had no real appreciation why, but I began to realise that if you’re studying something like economics, where rare events do occur and they’re very important– if there is a sudden rare event, this can be devastating to an economy. So to find these rare events is not so hard, but to study enough data that they come naturally, they just come every now and then, is hard because you’ve got to analyse lots of data. So his first task was, in fact, big data, but big data was not a word, of course, or even a concept. He simply realised that somewhere, every trade was recorded. Every transaction in economics is recorded all the way back to prehistoric times.
So he was able to contact the Chicago Mercantile Stock Exchange and obtain from them for free three huge magnetic tapes carrying about a million lines of information, and not complicated code, just the time, every 15 seconds, that’s one column. And the next column is the value of a stock index called the S&P 500 that every economist knows and would have no reason to look at it on 15 second time intervals because what matters is what’s reported, the end of the day or even the end of the week.
People at the end of the week say, well, the market went up 1% or down 2% or whatever, but they don’t say, 15 seconds ago, it was a fraction of a percent lower, and now it’s up a fraction of a percent. And the reason they don’t do it is that doesn’t affect how much money you have, but it affected trying to understand these fluctuations. You’ve got to have something to hang on, and the traditional economic student way is to just to make a model, make a theory, but there are millions of theories you could make. Its better first to find out what the empirical facts are. That’s another difference of physics and economics.
It’s not that we bring physics to economics, but we bring the style of first finding the facts and then trying to interpret them. So Isaac Newton or Galileo dropped apples and cannonballs and began to appreciate what happens when gravity is at work. And then much, much later, hundreds of years later, people begin to understand gravity. But even now, almost no one knows how to derive a Newton’s law or anything else. They are simply facts, but these facts are terribly useful. And with the facts, as you know, we can wage a war with cannons. You know where to aim the cannon so the ball falls where you want the cannonball to fall. And it’s a little like that with economics.
I’m not even sure it’s possible to understand economics. As I said, it’s determined by human beings, and how am I ever going to know what a human being wants to do? But if you can see a pattern of behaviour, then you make progress because you can warn people what’s coming down the road, and that’s what we accomplished. We found a way to predict the chance, the probability, of any given bad event. For example, Black Monday, where markets fell 25% worldwide on October 19, 1987, more than 25 years ago, was unexpected and people said it should never have happened. But actually, what we showed is that it not only could happen but it should happen, but of course rarely.
There will be another Black Monday for certain. People want to think there’s a cause, but it’s not really true. That’s the way things are. It’s a little like a physiological system. Things happen. So this made us very excited and very happy, and the paper turned out to be a classic paper. It was the first paper that got a lot of attention by a physicist on economics, and it led to many other papers confirming if the results were even true for other markets than S&P 500, and it was true. And therefore, the work has assumed an importance that we did not anticipate at the time we did it.

We all know that large stock market falls can happen, and that when they do, they can impact the wellbeing of people around the world. However, such events are rare. How can we get new insight into such catastrophic but infrequent events?

In this interview, Gene Stanley explains how analysis of large datasets can help us better understand stock market crises. He describes how an approach from physics allowed him and his collaborators to improve our estimates of how likely such devastating stock market movements are.

H. Eugene Stanley is a Professor at Boston University and a member of the National Academy of Sciences. He has made seminal contributions to statistical physics and is one of the pioneers of interdisciplinary science. He is one of the founding fathers of econophysics.

You can watch the whole of Tobias’ interview with Gene on YouTube (25:29).

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Big Data: Measuring And Predicting Human Behaviour

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