Jim Simons had a natural curiosity for math. Once, he couldn’t understand why his dad needed to stop the car for gas.
“You don’t have to run out of gas,” he told his father, “You can use half of what you have, and then you can use half of that and then half of that, and you’ll never run out of gas.”
Even at an early age, he knew slicing something in half multiple times over never quite gets you to zero. Of course, the car never gets you anywhere but there’s always some gas left.
Simons’s math abilities led him to MIT, graduating in three years. He stayed for graduate studies. He crossed the country to Berkley for a doctorate, which led to the Veblen Prize in Geometry in 1976. Yet, despite the math awards, he’s most recognized for his success on Wall Street.
Simons caught the investing bug in college. A trip to Bogota with two college friends led to an investment in a company that made vinyl floor tiles. His share was 10%. The outlook was promising. The results: less so. Until the company pivoted to making pipes. Sales took off. The group sold half the business and Simons was sitting on a small fortune.
The question was what to do with money? He reached out to an old math friend, now a commodity trader, who put the money to work. Nine months later, thanks to a spike in sugar prices, he had made ten times their money. Simons called it a fluke.
But the fluke gave Simons the capital to go into the investment business. His first attempt was a hedge fund called Monemetrics in 1978. He hired a couple of smart guys. They created a half-baked trading model for currencies but manually traded the portfolio.
Sometimes they overrode the model. They traded on instincts. They got lucky at times. It was a trial by fire but the results were promising. So they tried the model on other things like commodity futures, treasuries, stocks…if it was liquid and could be traded, they tried it.
Monemetrics became Renaissance Technologies in 1982. Gradually, their models got better. The Medallion Fund was born in 1988. It was a rough start at first. The models were better but not “perfect.”
The fund experienced a 30% peak-to-trough loss in 1989 and finished the year with a 4.1% loss. Not huge but the decline was enough for Simons to shut down trading. Something wasn’t quite right. For six months, they studied what went wrong and rebuilt the model.
The Fund was up and running again in 1990. It returned 56% after fees that year. The new model worked and they never looked back. It returned 39% in 1991, 34% in 1992, and 39% in 1993. That same year they closed the Medallion Fund to new money.
The Fund averaged a 66% return before fees — 39% after fees — from 1988 to 2019. And nobody knows how they really did it. It’s the greatest hedge fund performance ever.
Simons died last week at the age of 86. He never sought publicity. He rarely did interviews but the few he did do, hint at the broader reasons behind his success.
On Renaissance/Medallion Fund
The secret sauce is hiring great people, providing a great infrastructure, collaborating across the board, and sharing profits with everyone.
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Renaissance…manages what are termed quantitative funds — funds whose trading is determined by mathematical formulas designed to predict market behavior. Individual trades are generated by computers, based on work continually developed by our researchers. Naturally, human beings carefully monitor the trade execution process, making sure that all parts of the system are behaving properly. We operate in only highly liquid, publicly listed securities, such as stocks, bonds, currencies, and commodities, and do this on exchanges throughout the world. This means, for example, that we do not trade in credit default swaps or collateralized debt obligations, neither of which satisfies the above criteria. In the stock trading of our Medallion Fund, we hold balanced portfolios in each country, i.e. portfolios very close to being equally long and short. Our trading models tend to buy stocks that are recently out of favor and sell those recently in favor.
***
We never hired anyone from the financial world at Renaissance. We never did. Because they didn’t have anything to add, I didn’t think. There were people who wrote papers, or departments of finance or something at business schools, and some of these people write papers about predicting the stock market or stuff like that. We looked at a bunch of these papers. They were all wrong. Every paper was wrong. So we stopped bothering looking at these papers because they were wrong.
***
I have one guy who has a Ph.D. in finance. We don’t hire people from business schools. We don’t hire people from Wall Street. We hire people who have done good science.
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The fees that we charged were… First it was 5 and 20. And then it was 5 and 36. And then 5 and 44. So there was a fixed fee of 5% and the managers got 44% of the profits. And some of my outside investors, while we were raising the fees, said, “That’s outrageous. Okay. Can I get more?”
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What motivates me? I’m ambitious and I like to do things well. I love to create something that really works. We have lots and lots and lots of strategies, and each new one gives me a lot of pleasure, to see something new that works.
On Investment Process
We have three criteria. If it’s publicly traded, liquid and amenable to modeling, we trade it.
***
Any one anomaly might be a random thing; however, if you have enough data you can tell that it’s not. You can see an anomaly that’s persistent for a sufficiently long time — the probability of it being random is not high. But these things fade after a while; anomalies can get washed out. So you have to keep on top of the business.
***
Everything is grist for the mill… Weather, annual reports, quarterly reports, historic data itself, volumes, you name it. Whatever there is. We take in terabytes of data a day. And store it away and massage it and get it ready for analysis. You’re looking for anomalies.
***
Tumult is usually good for us. We don’t have credit lines of any significance. We don’t do a lot of leveraged-type financing… Generally, those kinds of times…when everyone is running around like a chicken with its head cut off, that’s pretty good for us because they seem to evidence the patterns that we know how to take advantage of.
***
We stayed ahead of the pack by finding other approaches — shorter-term approaches to some extent. The real thing was to gather a tremendous amount of data — 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. And very smart people — that was the key. I didn’t really know how to hire people to do fundamental trading. I had hired a few — some made money, some didn’t make money. I couldn’t make a business out of that. But I did know how to hire scientists, because I have some taste in that department. So, that’s what we did. And gradually these models got better and better, and better and better.
***
We search through historical data looking for anomalous patterns that we would not expect to occur at random. Our scheme is to analyze data and markets to test for statistical significance and consistency over time. Once we find one, we test it for statistical significance and consistency over time. After we determine its validity, we ask, “Does this correspond to some aspect of behavior that seems reasonable?”
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We like a reasonable amount of volatility. In our business we want some action.
***
Many of the anomalies we initially exploited are intact, though they have weakened some. What you need to do is pile them up. You need to build a system that is layered and layered. And with each new idea, you have to determine, Is this really new, or is this somehow embedded in what we’ve done already? So you use statistical tests to determine that, yes, a new discovery is really a new discovery. Okay, now how does it fit in? What’s the right weighting to put in? And finally you make an improvement. Then you layer in another one. And another one.
***
We don’t start with models. We start with data. We don’t have any preconceived notions. We look for things that can be replicated thousands of times.
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We look at anomalies that may be small in size and brief in time. We make our forecast. Then, shortly thereafter, we reevaluate the situation and revise our forecast and our portfolio. We do this all day long. We’re always in and out and out and in. So we’re dependent on activity to make money.
***
Brownian motion is a way of looking at data and ordering random activity, or activity that looks random. Models like that, and approaches like that are very useful. Some stuff comes out of math and physics, especially math where various optimization techniques are used. It’s more the sense and sophistication of doing science. We use very rigorous statistical approaches to determine what we think is underlying a phenomenon and really do explain that part of it.
***
Patterns of price movement are not random. However, they’re close enough to random so that getting some excess, some edge out of it, is not easy and not so obvious.
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One can predict the course of a comet more easily than one can predict the course of Citigroup’s stock. The attractiveness, of course, is that you can make more money successfully predicting a stock than you can a comet.
On Luck
Let’s suppose you have a coin that is 70/30 heads. Well, if you get to bet heads, you are going to win 7 times out of 10. Three times out ten you are going to lose, and that’s bad luck. So you need a measure of good luck to avoid a long run of tails when you have a 70/30 coin that’s heads. At a certain point the luck evens out. Of course there’s luck in our business, but so far we’ve had a nice edge.
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In this business it’s easy to confuse luck with brains.
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Luck is largely responsible for my reputation for genius. I don’t walk into the office in the morning and say, “Am I smart today?” I walk in and wonder, “Am I lucky today?”
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There’s no such thing as the goose that lays the golden egg forever.
***
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 with pure luck. I mean, I think it was pure luck. It certainly wasn’t mathematical modeling. But in looking at the data, after a while I realized: it looks like there’s some structure here. And I hired a few mathematicians, and we started making some models.
Sources:
AIP Oral History: Jim Simons
The Mathematician Who Cracked Wall Street
Hedge Fund and the Financial Markets
The Code Breaker
Seed Interview: James Simons
Simons Doesn’t Say
The Secret World of Jim Simons
Related Reading:
Notes: The Man Who Solved the Market