Interviewer: So you started with some of your dad’s money and that got you a taste for, an interest in it?Simons: Yes, some family money, and then some other people put up some money. And I did that. No models. No models for the first two years.
Interviewer: So what were you doing then? You were just using cunning and, you know, just like normal people do?Simons: Like normal people do. And I brought in a couple people to work with me, and we were extremely successful. I think it was just plain good luck, but nonetheless we were very successful. But I could see this was a very gut-wrenching business. You know you come in one morning, you think you’re a genius. The markets are for you. We were trading currencies and commodities and financial instruments and so on, not stocks, but those kinds of things. And the next morning you come in, you feel like a jerk. The markets are against you. It was very gut-wrenching. And in looking at the patterns of prices, I could see that there was something you could study here, that there were maybe some ways to predict prices, mathematically and statistically. And I started working on that, and then brought in some other people. And gradually, we built models. And the models got better and better and finally the models replaced the fundamental stuff. So it took awhile.
Interviewer: I would have thought with your background as a mathematician, this would have almost occurred to you immediately. Like you would have straightaway seen this. What was the two year delay?Simons: Well two things. I saw it pretty early, but, and I brought in a guy from the code cracking place. And he was, I thought, together we’ll start building models. That was fairly early. But it wasn’t right away. But he got more interested in the fundamental stuff. And he says, “the models aren’t going to be very strong,” and so on and so forth. So we didn’t get very far. But I knew there were models to be made. Then I brought in another mathematician, and a couple more, and a better computer guy. And then we started making models which really worked. But you know, the general, there’s something called the efficient market theory which says that there’s nothing in the data, let’s say price data, which will indicate anything about the future, because the price is sort of always right, the price is always right in some sense. But that’s just not true. So there are anomalies in the data. Even in the price history data. For one thing, commodities especially, used to trend. Not dramatically trend, but trend. So if you could get the trend right, you’d bet on the trend. And you’d make money more often than you wouldn’t, whether it was going down or going up. That was an anomaly in the data. But gradually we found more and more and more and more anomalies. None of them is so overwhelming that you’re going to clean up on a particular anomaly. Because if they were, other people would have seen them, so they have to be subtle things. And you put together a collection of these subtle anomalies and you begin to get something that will predict pretty well.
Interviewer: How elaborate are these things? Because in my head I’m imagining, you know, some equation. Like Pythagoras’s equation. You put a few numbers in and something spits out. But are these giant beasts of equations and algorithms, or are they simple things?Simons: Well the system as it is today is extraordinarily elaborate. But it’s not a whole lot of, you know it’s, it’s what’s called machine learning. So you find things that are predictive. You might guess, oh, such and such should be predictive, might be predictive, and you test it out on the computer and maybe it is and maybe it isn’t. You test it out on long-term historical data, and price data, and other things. And then you add to the system, this, if it works and if it doesn’t you throw it out. So there aren’t elaborate equations, at least not for the prediction part, but the prediction part is not the only part. You have to know what you’re costs are when you trade. You’re going to move the market when you trade. Now the average person can buy 200 shares of something, and he’s not going to move the market at all because he’s too small. But if you want to buy 200,000 shares you’re going to push the price. How much are you going to push the price? How are you going to, you know, are you going to push it so far that you can’t make any money because you’ve distorted things so much? So you have to understand costs, and that’s something that’s important. And then you have to understand how to minimize the volatility of the whole, of the whole assembly of positions that you have, and be, so you have to do that. That last part takes some fairly sophisticated applied mathematics, not earth-shattering, but fairly sophisticated.
Interviewer: What discipline of mathematics, or disciplines — is it multi-disciplinary? Or are we talking…Simons: It’s mostly statistics. It’s mostly statistics and some probability theory. And, but, I can’t get into what things we do use, and what things we don’t use. We reach for different things that come, that might be effective. So we’re very universal, we don’t have any, but it’s a big computer model. For one thing there is a capacity to the major model. It can manage a certain amount of money, which is rather large. But it can’t manage an enormous amount of money because you’re pushing, you’re going to end up pushing the market around too much, so it was kind of a sweet spot as to how much it’s reasonable to manage. Therefore it would never grow into some behemoth, which would, you know, take everybody out and you’d be the only player. I mean, well of course, if you were the only player there would be no one to play against. There are limitations, at least the way we see it. But we keep improving it. We have about 100 PhDs working for the firm.
Interviewer: That’s what I mean, I mean how did you get to that point? Did you start to think, we need this we need that. What did..?Simons: We just hired smart people. My algorithm has always been, you get smart people together. You give them a lot of freedom. Create an atmosphere where everyone talks to everyone else. They’re not hiding in a corner with their own little thing. They talk to everybody else. And you provide the best infrastructure, the best computers and so on that people can work with. And make everyone partners. So that was the model that we used in Renaissance. So we would bring in smart folks and they didn’t know anything about finance, but they learned. Thanks for the advice, Jim. Good stuff!