These past few weeks, I’ve revealed to you my involvement with Long-Term Capital Management (LTCM) — the hedge fund that collapsed in 1998 after its derivatives trading strategies went catastrophically wrong.
My use of complexity theory in understanding risk in capital markets arose as a direct consequence of my involvement with LTCM.
After the collapse and rescue, I chatted with one of the LTCM partners who ran the firm about what went wrong. I was familiar with markets and trading strategies, but I was no expert in the highly technical applied maths that the management committee used to devise its strategies.
The partner I was chatting with was a true ‘quant’ with advanced degrees in maths. I asked him how all of our trading strategies could have lost money at the same time, despite the fact that they had been uncorrelated in the past.
He shook his head and said, ‘What happened was just incredible. It was a seven-standard deviation event.’
Once every billion years
Statisticians symbolise a standard deviation with the Greek letter ‘sigma’. Even if you’ve never taken a statistics course, you would understand that a seven-sigma event sounds rare. But I wanted to know how rare.
I consulted some technical sources and discovered that for a daily occurrence, a seven-sigma event would happen less than once every billion years…or less than five times in the history of planet Earth!
I knew that my quant partner had the maths right. But it was obvious to me his model must be wrong. Extreme events had occurred in markets in 1987, 1994 and now 1998. They happened every four years or so.
Any model that tried to explain an event as something that happened every billion years could not possibly be the right model for understanding the dynamics of something that occurred every four years.
Solving the deepest market mysteries
From this encounter, I set out on a 10-year odyssey to discover the proper analytic method for understanding risk in capital markets. I studied physics, network theory, graph theory, complexity theory, applied mathematics and many other fields that connected in various ways to the actual workings of capital markets.
In time, I saw that capital markets were complex systems and that complexity theory — a branch of physics — was the best way to understand and manage risk and to foresee market collapses.
I began to lecture and write on the topic, including several papers that technical journals published. I built systems with partners that used complexity theory and related disciplines to identify geopolitical events in capital markets before those events became known to the public.
Finally, I received invitations to teach and consult at some of the leading universities and laboratories involved in complexity theory including the Johns Hopkins University, Northwestern University, the Los Alamos National Laboratory, Singularity University and the Applied Physics Laboratory.
In these venues, I continually promoted the idea of interdisciplinary efforts to solve the deepest mysteries of capital markets.
Ignored by the mainstream
I knew that no one field had all the answers. But a combination of expertise from various fields might produce insights and methods that could advance the art of financial risk management.
I proposed that a team consisting of physicists, computer modellers, applied mathematicians, lawyers, economists, sociologists and others could refine the theoretical models that I and others had developed. Such a team could suggest a program of empirical research and experimentation to validate the theory.
The scientists with whom I worked greeted these proposals warmly. But the economists rejected and ignored them. Invariably, top economists took the view that they had nothing to learn from physics. The way they saw things, the standard economic and finance models were a good explanation of securities prices and capital markets dynamics.
Whenever a ‘seven-sigma’ market event confronted prominent economists, they dismissed it as an ‘outlier’ and tweaked their models slightly…without ever recognising the fact that their models didn’t work at all.
Physicists had a different problem. They wanted to collaborate on economic problems, but were not financial markets experts themselves. They had spent their careers learning theoretical physics and did not necessarily know more about capital markets than the everyday investor worried about her pension plan. I was an unusual participant in the field.
Most of my collaborators were physicists trying to learn capital markets. I was a capital markets expert who had taken the time to learn physics.
Putting our knowledge to good use
One of the team leaders at Los Alamos, an MIT-educated computer science engineer named David Izraelevitz, told me in 2009 that I may be the only person he knew of with a deep working knowledge of finance and physics combined in a way that might unlock the mysteries of what caused financial markets to collapse.
It was a great compliment. And I’ve tried my very best to put that knowledge to good use.
In fact, alongside my Australian associate Tim Dohrmann — who himself has a strong background in both finance and physics — I’ve just launched a brand-new advisory service. We’ve designed Strategic Intelligence specifically to protect you from currency market manipulation and to show you how to profit from market imbalances.
I knew that a fully developed and tested theory of financial complexity would take decades to create, and that it would need contributions from many researchers. But I’ve been gratified to know that I’ve been contributing to the field with one foot in the physics lab and one foot planted firmly on Wall Street.
My work on this project, and that of others, continues to this day.
For The Daily Reckoning
James G. Rickards is the strategist for Strategic Intelligence, the newest newsletter from Port Phillip Publishing. He is an American lawyer, economist, and investment banker with 35 years of experience working in capital markets on Wall Street. He is the author of The New York Times bestsellers Currency Wars and The Death of Money. Jim also serves as Chief Economist for West Shore Group.