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

Physics Hits the Street 
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year, Black turned his consulting project into a dissertation on deduc-
tive question-answering systems, which he successfully defended in 
June 1964.
But by this time, Black had had enough of academia — at least for a 
while. He had settled on a project long enough to write a dissertation, 
but this hardly signaled a lifelong devotion to artificial intelligence. 
He thought about becoming a writer, working on popular nonfic-
tion projects. or maybe he would go into the computer business. He 
considered applying for a postdoctoral fellowship to stay at Harvard 
and work on the interface between technology and society — a new 
subject, spurred by new postwar technologies. But ultimately nothing 
panned out, and so after graduating, Black returned to consulting. At 
least there, he could work on many different projects, and he had al-
ready discovered that solving concrete problems appealed to him.
Instead of returning to BBn, however, Black took a job with an-
other local firm, called Arthur d. Little, Inc. (AdL), in the opera-
tions research division. At first Black worked primarily on computer 
problems. for instance, MetLife had a state-of-the art computer, but 
the company still felt as if its computation needs weren’t being met. 
MetLife hired AdL to see if a second computer was needed. Black, col-
laborating with two others at AdL, discovered that the problem wasn’t 
the computer, which was working at only half capacity, but rather the 
way in which the computer stored data: instead of using thirty avail-
able drives, it used only eight drives in everyday tasks. So Black and his 
team worked out an optimization scheme for using all of the available 
drives.
Black worked at AdL for about five years. the experience changed 
his life. When he arrived, he was an operations research and computer 
science guy. He had unusually broad interests, but there’s no evidence 
to suggest that finance was among them. When he left in 1969, he had 
already laid the foundation for the Black-Scholes model. He was recog-
nized, at least in some circles, as an exciting, if radical, up-and-coming 
financial economist. Wells fargo immediately hired him to develop a 
trading strategy.
this transformation began shortly after Black arrived at AdL, 
where he encountered a slightly older member of the operations re-

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search section named Jack treynor. treynor had gone to Haverford 
college intending to major in physics but decided that the department 
wasn’t very good, and so he switched to mathematics. After college 
he went to Harvard Business School and then joined AdL in 1956, a 
decade before Black would arrive. treynor and Black didn’t overlap at 
AdL for long: in 1966, treynor was wooed away by Merrill Lynch. But 
the two practically minded mathematicians made fast friends. Black 
liked treynor’s way of thinking and quickly became interested in his 
work, primarily on risk management, hedge fund performance, and 
asset pricing. Although treynor didn’t have a formal background in 
financial theory either, his business school background had exposed 
him to a set of problems that he was well suited to work on, and so 
much of his work at AdL involved financial institutions. Meanwhile, 
he worked on more theoretical research projects on the side, often mo-
tivated by the kinds of problems AdL clients encountered.
By the time Black arrived at AdL, treynor had already developed a 
new way of understanding the relationship between risk, probability, 
and expected value, now known as the capital Asset Pricing Model 
(cAPM). the basic idea underlying cAPM was that it should be pos-
sible to assign a price to risk. risk, in this context, means uncertainty, 
or volatility. certain kinds of assets — U.S. treasury bonds, for in-
stance — are essentially risk-free. nonetheless, they yield a certain rate 
of return, so that if you invest in treasury bonds, you are guaranteed 
to make money at a fixed rate. Most investments, however, are inher-
ently risky. treynor realized that it would be crazy to put your money 
into one of these risky investments, unless you could expect the risky 
investments to have a higher rate of return, at least on average, than 
the risk-free rate. treynor called this additional return a risk premium 
because it represented the additional income an investor would de-
mand before buying a risky asset. cAPM was a model that allowed you 
to link risk and return, via a cost-benefit analysis of risk premiums.
When Black learned about cAPM, he was immediately hooked. He 
found the simple relationship between uncertainty and profit deeply 
compelling. cAPM was a big-picture theory. It described the role of 
risk in making rational choices in a very abstract way. Later in his 
career, Black would point to one feature of cAPM in particular that 
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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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