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

From Coastlines to Cotton Prices 
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included hints that simple random walk models were not the whole 
story. Around 1965, financial theorists had a choice, though it surely 
didn’t feel that way to them at the time: they could follow osborne 
and others who showed how traditional statistical methods, developed 
largely in the context of physics, could be used to analyze and model 
stock market returns; or they could follow Mandelbrot, who showed 
that despite this remarkable power, there was reason to think the tradi-
tional methods had shortcomings. Weighing in on the traditionalists’ 
side was the fact that the older methods were better understood and 
simpler. Mandelbrot, meanwhile, had some highly suggestive data on 
his side.
the field chose osborne. cootner made the argument this way at a 
1962 meeting of the econometric Society,
*
in response to Mandelbrot’s 
work on cotton prices:
Mandelbrot, like Prime Minister churchill before him, promises us 
not utopia but blood, sweat, toil, and tears. If he is right, almost all of 
our statistical tools are obsolete. . . . Almost without exception, past 
econometric work is meaningless. Surely, before consigning centuries 
of work to the ash pile, we should like to have some assurance that all 
our work is truly useless.
Much of the field took a similar view. At this point, the (mild) random 
walk hypothesis was still young, but a growing number of research-
ers, cootner included, had already staked their careers on it. It is easy 
to see cootner’s remarks as a reactionary attempt to fend off a young 
researcher who had caught out the errors of the (recent) past. Surely 
Mandelbrot saw it this way, and perhaps we all should now that many 
practitioners and theorists alike have recognized the importance of 
fat-tailed distributions. for instance, some people — most notably, 
nassim taleb, a hedge fund manager and professor at Polytechnic In-
stitute of new York University who wrote an influential book called 
The Black Swan, as well as Mandelbrot himself — have recently argued 
that finance took a wrong turn in 1965 by continuing to assume mild 
randomness when really financial markets are wild.
* econometrics is the statistical study of economic data, including but not limited to finance.

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But that argument misses an important point about the way the sci-
ence of finance has developed. In the 1960s, traditional statistics was a 
mature field with an enormous toolbox. Mandelbrot was coming for-
ward with little more than a suggestion and a few pictures. It would 
have been essentially impossible to do the kind of work that osborne, 
Samuelson, and many others working in finance and econometrics did 
during this period without the tools of traditional statistics. Mandel-
brot’s project simply wasn’t well enough understood. It would be like 
telling a carpenter that screws are much stronger than nails, when the 
carpenter has a hammer and no one has yet invented the screwdriver. 
even if the house would be stronger if built with screws, you’d still get 
much farther working with a hammer and nails, at least for a while.
for this reason, pushing forward with the simpler available tools 
while Mandelbrot and his early converts worked out the consequences 
of his work on fractals and self-similarity was the only sensible choice. 
What the field implicitly understood is that you need to start with the 
simplest theory that works, get as far as you can, and then ask where 
the theory you’ve built has gone wrong. In this case, once you have es-
tablished that stock market prices are random (at least in some sense), 
the next step is to assume that they are random in the simplest possible 
way: that they just follow a random walk. this is what Bachelier did. 
osborne then pointed out that this couldn’t be right, since it would 
mean that stock prices could become negative, and so he complicated 
the model ever so slightly by suggesting that market rates of return 
follow a random walk. He then showed that this suggestion explained 
the data much better than Bachelier’s model.
then came Mandelbrot, who said that osborne’s suggestion wasn’t 
quite right either, because if you looked at price data in detail, you 
would see a different pattern from the one osborne thought he had 
found. not dramatically different, though; the pattern Mandelbrot 
identified doesn’t say that prices aren’t random, but that prices are ran-
dom in a slightly different way from what osborne had believed. the 
differences between osborne’s model and Mandelbrot’s can hardly be 
dismissed, but they become important only in the context of extreme 
events. on a typical day, there aren’t going to be any extreme events 
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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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