January 18, 2019

Jim Simmons: The Mathematician Who Cracked The Markets

Jim Simmons is a mathematician and a hedge fund manager (founded Renaissance Technologies in 1982). He is considered by many to have the best investment track record in the whole industry. 

In this interview Jim explained how he got started in trading in his late thirties when he got tired of mathematics (who can blame him?):

"When I started doing trading, I had gotten a little tired of mathematics. I was in my late 30s I had a little money I started trading and it went very well I made quite a lot of money how it with pure luck I mean I think it was pure luck. simply wasn't mathematical modeling."

And how starting that venture ended up in forming one of the world's biggest hedge funds:

"But in looking at the data after a while I realized hey this looks like there's some structure here and I hired a few mathematicians and we started trying to make some models just the kind of thing we did back at eye-dea you design an algorithm you test it out on a computer does it work doesn't it work and so on. "

Simmons also stated how commodities and currencies used to show trending characteristics back in the day but that is. not the case any longer:

"(in) the old days commodities or currencies had a tendency to trend."

"The trend-following would have been great in the 60s and it was sort of okay in the 70s by the 80s it wasn't such." 

Jim stressed the importance of staying ahead of the competition by looking for shorter term approaches to trading and hiring very smart individuals:

"We stayed ahead of the pack by finding by finding other other approaches and shorter term approaches to some extent. But the the real thing was to gather a tremendous amount of data and and we had to get it by hand in the early days. We went down to the Federal Reserve and copied interest rate histories and stuff like that because it didn't exist on computers. We got a lot of data, very smart people and that was the that was the key."

"In a certain sense what we did was machine learning you you you look at a lot of data and you try to simulate different predictive schemes until you get better and better at it it doesn't doesn't necessarily feed back on itself the way we did things but it worked."