The mathematician who cracked Wall Street | Jim Simons

Jim Simons' Early Career

In this section, Jim Simons talks about his early career as a mathematician and code-cracker for the National Security Agency (NSA).

Working for the NSA

• The NSA hired mathematicians to attack secret codes.

• Simons knew about the operation at Princeton and was attracted by their policy of allowing half of one's time to be spent on personal mathematics.

• Simons was fired from the NSA because he wrote a letter to The New York Times expressing his opposition to the Vietnam War.

Stepping Up His Mathematical Career

• After being fired from the NSA, Simons went on to work with Shiing-Shen Chern at Stony Brook University.

• Together, they published a famous paper that started a sub-field in mathematics.

• Their work also had applications in physics, which surprised them both.

The Unreasonable Effectiveness of Mathematics

In this section, Chris Anderson and Jim Simons discuss how mathematics can be applied in unexpected ways.

Applying Mathematics to Physics

• Chern-Simons invariants have spread through many areas of physics despite neither Chern nor Simons knowing much about physics when they did their work.

• Eugene Wigner wrote an essay on "the unreasonable effectiveness of mathematics" in describing the physical world.

Introduction to Algebraic Topology

In this section, Jim Simons talks about the Euler characteristic and its importance in algebraic topology. He explains how vertices minus edges plus faces always equals two for any shape, and how this is a topological invariant.

The Euler Characteristic

• The Euler characteristic is a topological invariant that was first observed by Leonhard Euler in the 1700s.

• Vertices minus edges plus faces always equals two for any shape, which is what's called the Euler characteristic.

• This applies to different shapes such as a torus or the surface of a doughnut where vertices minus edges comes out to be zero.

Renaissance Technologies

In this section, Jim Simons talks about his work at Renaissance Technologies and how he achieved high returns with low volatility and risk compared to other hedge funds.

Assembling a Great Team

• Jim Simons assembled a great team of mathematicians when he started trading.

• After realizing there was some structure in the data, they made models using algorithms and tested them on computers.

Trading Commodities

• In the old days, commodities or currencies had a tendency to trend.

Trend-Following and Data Gathering

In this section, Jim Simons talks about trend-following and how they stayed ahead of the pack by finding other approaches. He also discusses how they gathered a tremendous amount of data in the early days.

Trend-Following

• Trend-following would have been great in the '60s, and it was sort of OK in the '70s. By the '80s, it wasn't.

• To stay ahead of the pack, they tested a bunch of lengths of trends in time to see which ones were predictive.

• They found other approaches to trend-following that were shorter-term.

Data Gathering

• They gathered a tremendous amount of data by hand in the early days.

• They went down to the Federal Reserve and copied interest rate histories because it didn't exist on computers.

• They hired very smart people who helped make their models better over time.

Building a Culture at Renaissance

In this section, Jim Simons talks about building a culture at Renaissance that attracted talented scientists who were motivated by doing exciting mathematics and science.

Building a Culture

• Jim Simons is credited with building a culture at Renaissance that attracted talented scientists who weren't just motivated by money.

• While some people did come for the money, many came because it would be fun to work there.

Machine Learning at Renaissance

In this section, Jim Simons talks about machine learning at Renaissance and how they used it to simulate different predictive schemes.

Machine Learning

• What they did at Renaissance was machine learning in a certain sense.

• They looked at a lot of data and tried to simulate different predictive schemes until they got better at it.

Data Analysis at Renaissance

In this section, Jim Simons talks about the types of data they analyzed at Renaissance and how they looked for anomalies.

Types of Data Analyzed

• They analyzed weather, annual reports, quarterly reports, historic data itself, volumes, and anything else that was available.

• They took in terabytes of data a day and stored it away for analysis.

Looking for Anomalies

• They looked for anomalies that were persistent over time because the probability of them being random was low.

• Anomalies can get washed out over time so you have to keep on top of the business.

Hedge Funds Industry

In this section, Jim Simons talks about the hedge fund industry and its recent performance.

Hedge Fund Industry

• The hedge fund industry as a whole has not done especially well in the last three or four years.

• A lot of wealth created in recent years has not been created by hedge funds.

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