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

participants in the conference — as well as other commentators from 

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economics and finance—didn’t even agree that a concentrated effort 
to improve the sophistication of economic modeling was called for. In 
the background were questions about funding — if the project were 
funded, how would money be doled out to the participants? — that 
made the individuals involved cautious of supporting the larger proj-
ect, for fear they wouldn’t receive their cut. And so with regard to the 
larger goal of creating a new community of interdisciplinary research-
ers devoted to tackling problems in economics from new directions, 
the conference failed. After a few months, Smolin gave up on econom-
ics and turned his attention back to physics. now, when he finds him-
self with a few free minutes, he works on climate science. economics, 
he has decided, is intractable — not for the subject matter, but because 
the field does not seem open to new ways of thinking. Weinstein was 
right: economics is ten times worse than physics.
today, Weinstein and Malaney continue to work on expanding the 
mathematical foundations of economic theory. Sornette continues 
to develop his predictive tools. farmer is back at the Santa fe Insti-
tute, developing new connections between complexity science and 
economic modeling. despite this brainpower, the world economy is 
in pieces, still bloodied by the 2007–2008 collapse. can anything be 
done?
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I began thinking about this book in the fall of 2008, in the midst 
of the financial meltdown. At the time, I was about eight months away 
from a Phd in physics. After a few weeks of researching, I mentioned 
what I had uncovered to my dissertation advisor. His reaction sur-
prised me. He was convinced, from my examples of how ideas from 
physics had been used to understand financial markets, that there was 
a strong connection between the fields. (this, I have found, is the case 
with most physicists.) But this didn’t move him. Instead, he responded 
by saying that no matter how many physicists had influenced finance, 
it was impossible to do science on Wall Street.
this idea can be put in different terms. Science isn’t a body of 
knowledge. It’s a way of learning about the world — an ongoing pro-
cess of discovery, testing, and revision. My thesis advisor’s reasons for 
thinking this process couldn’t occur on Wall Street were mostly socio-
logical: investment banks and hedge funds are usually very secretive, 
which means that new ideas developed by such firms are rarely aired 
and debated the way that new developments in scientific fields are. 
When a physicist or biologist develops some new insight, he submits 
a paper on it to a professional journal, where it then undergoes peer 
review — a process by which new scientific ideas are vetted by other 
scientists before appearing in print. If a paper passes this first hurdle, 
it is then scrutinized by the larger community of scientists. Many ideas 
don’t survive this ordeal — they are either never published, or else they 
Epilogue: Send Physics, 
Math, and Money!

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languish in obscurity. even the ideas that are taken up by the commu-
nity, the ideas that prove most useful, are not accepted as sacrosanct. 
Instead, they form the starting point for the next generation of theo-
ries and models.
In other words, thinking like a physicist is different from (merely) 
using mathematical models or physical theories. It’s how you under-
stand the models that counts. In early 2009, emanuel derman, the for-
mer physicist who worked with fischer Black at Goldman Sachs dur-
ing the eighties and nineties, teamed up with Paul Wilmott, founder 
of oxford University’s program in quantitative finance, to pen the “fi-
nancial Modelers’ Manifesto.” their point was in part to defend math-
ematical models as essential to thinking about finance and econom-
ics, and in part to chide “the teachers of finance” who have forgotten 
that no model states laws by which markets must abide. As they put it, 
“Models are at bottom tools for approximate thinking.” they are never 
the final word — they rely on assumptions that never hold perfectly, 
and that sometimes fail entirely. Appropriate use of models requires 
a good dose of common sense and an awareness of the limitations of 
whatever model you happen to be using. In this way, they are like any 
tool. A sledgehammer may be great for laying train rails, but you need 
to recognize that it won’t be very good for hammering in finishing 
nails on a picture frame.
I believe the history that I have recounted in this book supports 
the closely related claims that models in finance are best thought of 
as tools for certain kinds of purposes, and also that these tools make 
sense only in the context of an iterative process of developing models 
and then figuring out when, why, and how they fail — so that the next 
generation of models are robust in ways that the older models were 
not.
from this perspective, Bachelier represents a first volley, the initial 
attempt to apply new ideas from statistical physics to an entirely dif-
ferent set of problems. He laid the groundwork for a revolutionary way 
of thinking about markets. But his work was littered with problems. 
Most obvious, from the point of view of Samuelson and osborne, 
was that the normal distribution he described for stock prices worked 

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