The Physics of Wall Street: a brief History of Predicting the Unpredictable

Tyranny of the Dragon King 
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nineties, technology stocks skyrocketed. In 1998 and 1999, the technol-
ogy sector of the S&P 500 index went up by a factor of four, while the 
index as a whole increased by just 50%. the technology-based nAS-
dAQ index increased by almost a factor of three between 1998 and 
early 2000. Analysts began talking about a so-called new economy 
consisting of computer firms and companies whose business strategies 
depended entirely on the Internet. for these companies, none of the 
old rules applied. It didn’t matter if a firm was making any money, for 
instance — earnings could be negative, but the company could still be 
considered valuable if there was a wide expectation of success in the 
future. In many ways, the boom echoed earlier periods of speculation: 
in the 1920s, for instance, investors also spoke of a “new economy,” 
though then the hot tech companies were At&t and General electric.
Sornette started seeing the log-periodic oscillations in nASdAQ 
data beginning in late 1999. By March 10, 2000 — the day the nAS-
dAQ peaked — he had enough data to say the crash was imminent, 
and to predict when it would occur. He put the date somewhere be-
tween March 31 and May 2. Sure enough, during the week beginning 
April 10, the nASdAQ fell by 25%. tech stocks had gone the way of the 
tulip bulb.
the methods Sornette has used to identify bubbles and predict 
when crashes will occur can also be used to identify a situation that 
Sornette has called an anti-bubble. these are cases in which stock 
prices are artificially low. on January 25, 1999, for instance, Sornette 
posted a paper on an online physics archive claiming that, based on his 
observation of log-periodic patterns in the market data, the Japanese 
nikkei stock index was in the midst of an anti-bubble. the paper in-
cluded quite precise predictions: Sornette indicated that by the end of 
that year, the nikkei would increase by 50%.
this prediction was all the more remarkable because the Japanese 
market was near its fourteen-year low, which it reached on January 5, 
1999. All indications were that the market would continue to fall — an 
opinion held by most economists at the time. nobel Prize laureate and 
New York Times opinion columnist Paul Krugman, for instance, wrote 
on January 20 that the Japanese economy was beginning to look like a 

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tragedy, and that there simply wasn’t enough demand for a recovery. 
But time proved Sornette right. By the end of the year, the nikkei had 
recovered, by precisely the 50% Sornette predicted.
Mandelbrot’s work gave some economists reason to think that markets 
are wildly random, exhibiting behavior that someone like Bachelier 
or osborne could never have imagined. even if Mandelbrot turned 
out to be wrong in the details of his proposal, he nonetheless revealed 
that financial markets are governed by fat-tailed distributions. there’s 
nothing special about extreme financial events. they are not excep-
tions; they are the norm — and worse, they happen all the time, for the 
same reason as more mundane events. Big market drawdowns, at their 
core, are just smaller drawdowns that didn’t stop.
If this is right, one might think that there is no way to predict ca-
tastrophes. Indeed, self-organization, one of the principal parts of 
the theory of critical phenomena, is usually associated with just the 
kind of fat-tailed distributions that make predicting extreme events so 
difficult. the three physicists who first introduced the notion of self-
organization, Per Bak, chao tang, and Kurt Wiesenfeld, took their 
discovery as evidence that extreme events are, in principle, indistin-
guishable from more moderate events. the moral, they thought, was 
that predicting such events was a hopeless endeavor.
this concern is at the heart of hedge fund manager nassim taleb’s 
argument against modeling in finance. In his book The Black Swan, 
taleb explains that some events — he calls them “black swans” — are 
so far from standard, normal distribution expectations that you can-
not even make sense of questions about their likelihood. they are es-
sentially unpredictable, and yet when they occur, they change every-
thing. taleb takes it to be a consequence of Mandelbrot’s arguments 
that these kinds of extreme events, the events with the most dramatic 
consequences, occur much more frequently than any model can ac-
count for. to trust a mathematical model in a wildly random system 
like a financial market is foolish, then, because the models exclude the 
most important phenomena: the catastrophic crashes.
recently, Sornette introduced a new term for extreme events. In-
stead of black swans, he calls them “dragon kings.” He used the word 
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t h e p h y s i c s o f wa l l s t r e e t

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