S. Donald Sussman Fellowship Fireside Chat with Dr. James Simons — Chat 2 (Finance, moderated by Andrew Lo)

Welcome, everyone. I think you're in for a real treat. First of all, I'd like to welcome you all to MIT Sloan. We're here today because there's been an award made for the Sussman Fellowship, which is given every couple of years, and it was funded to honor the achievements and the opportunities Donald Sussman has given to a bunch of people in the fund management business. He's also somebody who was very much a pioneer in the whole hedge fund arena. Donald, for those who weren't here last week, runs Paloma Partners, a pioneering private equity investment firm, and is very much involved with all things investing to this day. This year we have awarded the fellowship to Jim Simons, when in fact the award really is almost ours for Jim having agreed to show up. Jim is an extraordinary man, genuinely extraordinary. It's very hard to describe how extraordinary because, if you think about this, you know, every day he goes to work, or when he did go to work...

It's a discovery and a battle. It's a competition. It's like an athlete winning the Olympics pretty much every day. That's probably the best analogy. It's a fiercely competitive environment, and anybody who knows finance knows how unbelievably difficult it is and how many people would like to eat your meal. Jim not only is a great mathematician—and again, those who were here last week will know about this—but he's also what I describe as a real mensch, an extremely nice person who, when I first met him, which would have been around 1990 or '91, his fund was maybe 200, and Jim will correct me, maybe 250 million, which by then was fairly large, but by today's standards, it's very small, had this certain irreverence and confidence and directness. And one of the things, features that makes Jim so very special, and you'll notice it today, he gives extremely concise, direct, and unambiguous answers to any question you ask him.

The other thing to note about Jim is, for those who know some of the people who work for Jim or know some of the history, it's unbelievably difficult to manage intelligent people. And I don't know many people who do it as well as Jim does. And it's worse when there's a lot of money involved. Jim—one other thing to say, there are a few things, there are so many things to say, but one of the things to say about Jim is that his track record is so extraordinary that, to most academics, it's inconceivable. And it's somewhat ironic that we're here at MIT, and this is probably the best finance faculty in the world, at least some of my friends tell me. And there's a paradox here, because Jim never hires finance guys or MBAs or such.

He never used to. And it's quite wonderful to have him speak to this audience. And I'm sure there are other departments here as well. But it is interesting that you have departments studying all kinds of features of the financial markets, and Jim does away with publishing papers, instead just cracks them. So Jim, Andrew, over to you. Thank you.

Well, I want to start by joining Andre and my MIT colleagues in thanking Jim for joining us today and being here. And I have to say that this is a real pleasure and an honor for me, because I think it's fair to say that Jim Simons and Renaissance Technologies is certainly the most successful quantitative investor in the history of investing. But perhaps, actually, you can drop the qualifier quantitative. And so there's a really interesting set of issues that we want to get to today. Before I do that, though, I need to lower your expectations of the interviewer, because the three fireside chats that Jim has agreed to, mathematics, money, and making a difference, only one of those M's is proprietary and confidential.

And that happens to be today's topic of money. So Jim and I agreed on some ground rules. I get to ask all sorts of nosy questions. And he gets to say, 'pass,' because it's confidential. And I'm sure that the audience will have a chance to ask those questions too.

And I'll have a chance to say, "No, I won't answer." Right?

So Jim, I'm going to ask you, if you don't mind, to recount a bit of your biography the way we did last week. But instead of Tom's focus on your mathematics career, I'd like to turn it around and focus on your business and finance career. So I'm going to start at the very beginning. You were a very precocious mathematics student. You mentioned last week that when you were two or three years old, you were already doing the powers of two. Were you also precocious from a business perspective? Did you think about any of these issues as a child?

As a child, I thought I had no interest in business, which is not to say I had no interest in money. But I had no interest in business. And I was a little kid. I had a friend who was very rich. It's nice to be very rich. I observed that. But I just focused on math for quite a while.

So unlike Warren Buffett, who had a newspaper route business, or Bob Merton, who I think was trading stocks when he was 10, you had nothing to do with finance.

Nothing to do with finance.

OK, so when did you first get interested in business? When you were at MIT, you mentioned something about that last week. I'll have to call you back. What can I say? I hope that wasn't a margin call.

If it had been, I would have said the same thing.

So when did you start thinking about business? You mentioned as an undergraduate, you had some friends who were doing some business in Colombia.

Well, I made friends at MIT with two Colombian boys. And they, at a certain point, started a business. And in fact, it was my encouragement that they started that business. And my father and I invested a small amount in that business, which turned out eventually to be a big success.

So what possessed you to think about that? I mean, that takes a certain amount of initiative to actually help.

Well, what possessed me to think about that particular investment was that I had, when we graduated MIT, three of us, one of whom was the Colombian boy, and his friend was in Bogota, three of us rode motor scooters from Cambridge to Bogota. Now, we'd expected to go all the way to Buenos Aires, but by the time we got to Bogota, we were exhausted. So we stopped in Bogota, and I stayed there a week or so, and I saw this country, Colombia, and it was really a place that you could do anything. I was told if you start a business a manufacturing business and you're making something that was imported, previously imported to Columbia, the government would shut off those imports and give you clear road to run.

So I thought my friend should start some kind of business like that, which they did. But that was my first interaction with money—was when I went out to Berkeley to finish my PhD. I spent two years there. And the first year, early on, I got married. And I got $5,000 worth of wedding gifts. So my wife and I decided—well, I decided, but she was willing—that we should invest this. And I had a couple of stocks, which, for no good reason, I thought might do well. And so I opened an account in San Francisco with Merrill Lynch. I bought these two stocks. I went home. And for months, they did absolutely nothing. So they didn't go down. They didn't go up. So I went back and I said, 'Do you have anything that's a little more exciting?'

And he said, "Yes." He said, "You should buy soybeans. Merrill Lynch thinks that $2.50 now, they're going to go up to $3.50." "What are you talking about?" "Soybeans." I knew about stocks. I didn't know about soybeans. He says, "Yes, you could buy a contract that's 5,000 bushels. You could buy two contracts. You get a lot of leverage," and so on. All right. So I bought two contracts of soybeans. And within a week, it had gone up quite a lot. And I'd made several thousand dollars, maybe two or three. Now, that was exciting. I came back to the math department and I said to one of the older guys, I told him what happened. I said, "Do you have any idea what I should do?" He said, "Absolutely. Sell it immediately."

Which was extremely good advice because within a day or two, it had gone back down. And it was bouncing around and actually had a little loss. I closed out the position and then I thought, "Well, I should have taken a smaller position, and then I could have held it more." And I did. I bought one contract of soybeans and was going back and forth early in the morning to watch the opening in Chicago because it was early in the morning in San Francisco when Chicago opened to trade these things. And I was going back and forth across the Bay Bridge watching the board. And I had a little profit at a certain point, and I realized, "I am either going to trade soybeans or write a thesis." I was in the middle of starting to write a thesis, and I could see I can't do both at the same time.

So I sold that one contract for, I think, a very small profit. And that was the last time I traded anything for a number of years. But I did write a thesis and got a job here at MIT as a result.

So just for clarification, your first investment was a couple of stocks. And then the second investment was soybean futures contracts. And these contracts, as I recall, are leveraged like 25 or 50 to 1. Is that right?

It's very highly leveraged. High-octane kind of investment.

And your broker felt that you were a suitable investor for that as a graduate student?

Well, he didn't ask any questions. Oh, great. He was just doing his job. I came in. I had enough money to buy this stuff. So he figured it was all OK. And so you had never invested before that?

You didn't have to take a course in finance or business? No, never.

Nothing.

OK, great. So now you're an assistant professor at MIT. And it's pretty clear, based on your thesis and the early work that you did, that you were going to have a very good career in math. And you did. So what got you interested in business at that same time? Because you continued to have an interest in it, didn't you? Continue having an interest? An interest in business.

Well, when I came back to teach at MIT, the first intersession, I went down to Bogotá to visit with my friends and told them I was coming and I won't leave until we have found a business. And they found one while I was there and decided to partner up. And I knew they were very smart guys, and they had a very good sense of business, which I don't think I ever had. And so they started this business. My father and I invested a small amount. And I had to borrow from everybody, but I did. So that was the first thing. And then there was not much I could do about it, so I kept doing math. But I actually, since I borrowed some money, I needed to pay it back. And it was... There was a place in Princeton called the Institute for Defense Analyses, which was a very highly classified joint.

And it specialized in cracking Russian codes and protecting our own. So it was under the auspices of the NSA. And they paid a lot for mathematicians. So I applied to them and got a job and enjoyed the job, and was able to start paying down some of my debts because it paid maybe double what I was getting at MIT. And I liked that place. It was interesting.

You talked last week about some of the work that you did there. But one of the things that I wanted to ask you—and I didn't think it was appropriate to ask last week because the focus is on mathematics—did any of the work that you were doing there, any of the mathematical tools that you were developing, have any applicability to some of the work that you did later on in finance?

In a general sense, yes. Now, I didn't get into finance for 10 years after that. I left there in '68 and really didn't get into finance until the late-ish '70s. But I learned about computers. I learned about the fun of coming up with some algorithm which might crack a code. Most of the time it didn't, but once in a while you were lucky. And I didn't know how to program at all and never did learn how to program, but they had programmers. But I liked the idea of developing algorithms, seeing them put on the computer, and seeing if it's going to work. So that experience was very influential when I went into the hedge fund business and then gradually started to make it systematic as opposed to fundamental trading, which we did at the beginning.

So anyway, I was a mathematician. I was getting frustrated with some of the research I was doing. I worked on a problem for two years, didn't get anywhere. And it's never been solved. So you could see it was a hard problem. It's a good one, too. And the South American business was beginning to throw off some money. So I had some money. And I thought I would start investing. And I had an interest in foreign currencies. I don't know why, but I did. And I read a lot about that. So we started. I got a partner investing in foreign currencies. And that did very well.

Was this before you left for Stony Brook? Oh, no. It was after I left for Stony Brook.

I'd been in Stony Brook for six years by then. Okay. I went to Stony Brook in '68, and it was '76 or '77 that we started doing this. But I thought we could. I looked at the charts, and they looked like there was some structure to these historical charts that one could perhaps exploit. So I hired the best cryptanalyst in the world, a guy named Lenny Baum, who—you may have heard of the Baum-Welch algorithm, the EM algorithm, expectation-maximization—he discovered that. So he came to work with me. And we built a little system, even though I was trading fundamentally at the time, you know, seat-of-the-pants sort of thing, which way the wind is blowing. We developed this sort of primitive currency trading system.

But we didn't actually put it into practice because one day, Lenny didn't show up for work until the middle of the afternoon. And I should say that Lenny loved to read the broad tape. There was this tape—we called it the Doomsday Machine because it just clicked. This broad tape would roll all day long, giving the financial news of the world. And he liked to study that. He was supposed to be studying making systems, but he liked to read that tape. So he came in late, and I said, 'Where you been?' And he said, 'Margaret Thatcher has been sitting on the pound, and it has to go up.' I said, 'Oh, well, I wish you'd come here this morning.' He said, 'Why?' I said, 'Because Margaret Thatcher just stood up.'

And Margaret Thatcher just stood up. And the pound is way up. He said, how much is it up? I said, well, it's up at nickels, $0.05 so far. He said, it's going to go up $0.50 a dollar. Buy pounds. We should buy pounds. I said, OK. Buy pounds. Sure enough, it went way, way up. And that was the last time Lenny wanted to look at any systems. He just felt his good intuition would be suitable and we'd make a lot of money. And we did. We did, doing fundamental trading. We started a fund called Limeroy. And the fees were... 25% of profits, no fixed fee, which was sort of a reasonable thing. And with Lenny as my partner, the first year the fund doubled after fees, and the next year it multiplied by six after fees.

So it had, well, two times six is 12. So everyone had 12 times as much money as they started with. And it was fantastic. And it was all fundamental training. Still, I felt that OK, we can't. We were lucky in certain ways. I'll tell you one good story about luck. Gold, which was illegal to trade, had become legal to trade. And the gold market, gold prices were going up. And we bought gold in the firm. We bought gold. We had a pretty big position. In fact, Lenny and I split the position. Half of it belonged to him in some sense, and half belonged to me. And it was at $200.50, $250, $300, $400, $500, $550, I think. I said, Lenny, I think we should sell this already. He said, no, no, you don't know how far it'll go.

You don't know how far it'll go. So I sold my half. And it kept going up. And one day, it reached $800. And that day, I happened to be speaking to a friend of mine who was a stockbroker, but I was just chatting with him over the phone. And I said, what's new? He said, well, what's new is this. My wife went into my closet this morning and cleaned it out of all my old gold cufflinks and tie clasps. And she's now down selling it. I said, well, Dick, I mean, are you having financial difficulties? He said, no, no, she's a jeweler, which she was, and she only had a stand in the short line. I said, the short line? He says, don't you know there's lines and lines of people selling gold? I said, no, but I'm very glad you told me.

I hung up with him. I picked up the phone, which went right to the floor of the exchange, and I got Lenny to come over. And I said, "Lenny, sell the gold." He said, "No, you don't know how far it's going." I was the boss, and I said, "Sell the effing gold." He said, "Okay, okay." And he sold the gold. It was $810 or something like that. The next morning, we came in, and it was $820. And he was so mad. By the end of that day, it was $650. The market collapsed and went nowhere but down after that until it got back to $250 or $300. Not in a week, but it just collapsed. Now, that was totally good luck. I mean, it was good that I realized if everyone is selling something, it may be a time to sell it yourself.

But it was... It was luck. It was just luck. So we did well, but I felt that this should be systematized. There should be a way to systematize it. And I brought in another mathematician, a very strong mathematician named Jim Axe, to do fundamental trading. But he knew that we had made this currency system. And he looked at it, and he got a good programmer into the firm. And he realized this system could work for all commodities, really. It was a pretty good system, sort of. So we started trading that system. And it did pretty well. And he did research and improved it and improved it. We were still fundamental trading, but that wasn't even going so well. I had gotten interested in venture capital to some extent.

So this Limroy company was also starting to invest in startup companies. And Axe was running this trading. And at a certain point, the investors in Limroy, they didn't like this illiquid venture capital. They liked the trading. And so I decided to break up the company, Limroy, and make a fund called Medallion. Jim Axe would run that fund. And we put the venture stuff into a liquidation-only fund. And actually, it ended up doing pretty well. So now we had the Medallion Fund, and everyone invested in the Medallion Fund from Limroy. And it did very well for about six months, and then it started losing money. And it was losing money steadily. Now, he and his team had developed a very complex system, very complex system.

And it had, I don't know, many dimensions in it, one thing or another. And I said, you know, I have to understand what this system is actually doing. And he said, "Oh, it's too tough. I can't explain it to you." It had this bell and that whistle. So I said, "Come on. I'm just going to project this into the two principal dimensions and see what it looks like." And it was nothing but a trending system, plain and simple, trending. It had these other little geek things, whatever they were. But it was basically a trending system. And trending, which in commodities and currencies too, which historically was a very strong thing, had in the last several years just sort of gone away with no reason to think it would ever come back.

So I said, "We're closing the fund." And he was very annoyed. But I was the boss. So we closed the fund. And I told the investors, "We're going to spend, we're going to do a study period. And we're not going to trade at all. Of course, we're not going to charge any fees." Oh, at that point, it was 5 and 20. It was a 5% fixed fee and 20% of profits. And everyone stuck with us. A few people redeemed, but everyone stuck with us. And for six months, and we brought back someone who had left the firm, and it's a long story, but we brought back this other very good guy, Axel Eft. And he and I, especially he, he had some ideas of much shorter-term trading. Not high frequency in and out in five minutes, but trading on a much shorter term.

And he developed a pretty good system. And together, I helped him, and it got better. And after six months, we went back in business. Only systematic trading. And from then on, we never looked back. It just went from strength to strength. I hired a lot of scientists, bought a lot of computers. And over the years, the system got better and better and better.

So Jim, we're going to focus on the Renaissance Medallion Fund in a few minutes. But I want to bring you back a little bit, because there are some interesting precursors that I think speak to the success that you enjoyed. One is that when you were at IDA, is that where you first met Lenny Baum? Was he there?

And as I recall, his early work, the Baum-Welch algorithm was really designed to estimate hidden Markov models. That's right. Which for many of you, I think you know that it's the precursor for a lot of the techniques that are used today, including deep learning. So it's an interesting history to that in terms of what you encountered there.

Yeah. He developed that algorithm with this guy named Lloyd Welch, which was supposed to estimate hidden Markov models, whatever that is. But there are a lot of parameters. And it was an algorithm which just kept climbing. It kept re-estimating and re-estimating. And with each re-estimation, the expectancy of these particular parameters, whatever they were, got better and better. And it changed the parameters, and it got better. No one could prove that it worked. No one could prove that it worked. It clearly did. You could start at any place and it definitely worked.

But how did you prove it? How could we prove it?

So actually, I worked on that a little while while I was at IDA. And trying to prove that this thing actually works, that it climbs at every step. But I couldn't. And anyway, I left IDA. And Lenny and his friend Petrie finally figured it out. And they wrote a long paper. It may have been two or three papers. Now today, it turns out, I'm told you can prove that in just a couple of pages. Because there was some theorem of which they were unaware, which would have made it short. But anyway, there was the algorithm. Speech recognition—it was very good for speech recognition. A whole lot of things.

Yeah. Yeah. So I'll get to Medallion in a minute, but I want to just ask you two more things that lead up to the Medallion Fund. One was you left IDA to join Stony Brook's math department. And at the time, Stony Brook's math department wasn't nearly as strong as it is now. Can you tell us about that and what motivated you and what your experiences were there?

Well, I got fired from IDA. I got fired. Did I tell this in the last talk?

Last time, but I think it would be worth repeating, because not everybody was here.

OK. So I always say, getting fired once could be a good experience. You just don't want to make a habit of it. But I did. I got fired. The head of this place, IDA, who was in Washington, DC, which was a big organization and one of its units was this small unit in Princeton. His name was Maxwell Taylor. Some of the older folks in the audience might remember that name. And he wrote an article, lead article in the New York Times Magazine section about how we're winning in Vietnam, we're doing great, we have to stay the course and so on. This was 1968. And I did not have the same opinion as he. And I wrote a letter to The Times, the first sentence of which was, 'Not everyone who works for General Taylor subscribes to his views,' or something like that.

And I gave my views, which was we should get out of there as fast as we can. And nobody said anything. Nobody said anything. They could have tried to lift my security clearance, but there would be no reason for it. A few months later, a guy claiming to be a stringer for Newsweek magazine said he's doing an article on people who work for the Defense Department and are opposed to the war, and he's having trouble finding anyone in that category. Could he interview me? I was 29 years old. No one had ever asked to interview me before, so I was very excited. And he said, 'Well, OK, so how are you responding to this?' I said, 'Well, at IDA, you're supposed to spend at least half your time on their work.'

But you could also spend up to half your time on your own work. And I had been doing a lot of math in that period. So I said, 'So my attitude is, my policy is, until the war is over, I'll do only my own work. And then when it's over, I'll do an equal amount of time doing only their work. And so that'll all balance out.' So that's what I said. Then I went back to the office and I decided I better tell my boss that I gave this interview. It would have been more intelligent if I had told him before I gave the interview, because he would have said, 'Don't give any interviews.' But he said, 'Well, what did you say?' I said, 'Well, I said about half and half.' And so he said, 'OK.' He went into his inner office and called Maxwell Taylor.

And he came out in five minutes and said, 'Well, you're fired.' I said, 'I'm fired? I'm fired?' I said, 'You can't fire me. My title is permanent member, permanent member.' And he said, 'Well, you know the difference between a permanent member and a temporary member?' I said, 'No.' He says, 'A temporary member has a contract.' But I was a permanent member, and I didn't have a contract. So I left. Of course, I had to leave. And I had to look for a job. I had three kids. But I was certain I would get a pretty good job, because I had just done some actually quite important mathematics, and I'd been giving talks and so on. So I knew I'd get a good job, but as a professor somewhere. But Stony Brook came along and offered me the position of being chair of their math department.

And it was a weak department, with one or two exceptions, and they wanted us to build it up. And they'd been trying for a long time to find an older, distinguished person to come as chair, and they couldn't find anybody. But they found me, and I thought, 'This would be really fun. I'd like to build something.' And so I took the job. And the university had a lot of money at that time, which it doesn't have so much today. They had a lot of money. Rockefeller was the governor, and he loved the state university. So I hired a lot of great people. It was a wonderful experience. I did a lot of mathematics during those first few years myself. It was a very, very productive time.

So then what led you to start doing your currency trading? Because at some point you left Stony Brook to do currency trading full-time.

Yeah, I left Stony Brook. First I went half-time, and then I left altogether and was in the trading business, yes.

And so what led you to do that?

Well, because as I said, I was stuck on a problem. I had come into some money. I was trying that out. I liked it. And I thought, well, I'll just have a new career. My father was very opposed to it. He said, 'Look, you have tenure. You have this wonderful job. They can't take it away from you.' I did have a contract in that sense. 'And why do you want to take this risk?' But I thought it would work. I thought it would work out. And I was pretty confident.

And so in your fundamental trading for currencies, can you share with us how you did it? I mean, it was totally non-quantitative, would you say? Totally. And so did you use, for example, technical analysis? People argue, the charting.

No, I didn't do any technical analysis. I read all the newspapers, The Economist. There was a lot of writing. I just paid a lot of attention to, to currencies, and in those days, currencies had just been tradable in the open market because some countries still had fixed currencies, fixed to the dollar, and you couldn't—well, you could trade it, but it was fixed. So it was just fundamental stuff, and it worked. It worked reasonably well, I would say. It worked reasonably well. But that was it. But the problem with a business like that is I'd walk in one day, everything was going my way. 'Oh, I'm a genius.' The next day I'd walk in, everything was against me. 'Oh, I'm a dope.' It was a very stomach-wrenching business.

Whereas with a system that you can develop, okay, you have a system. You do what the computer says to do. You made a historical study of the system that you're using, and it worked; with a very high probability, this system was going to work. And so I was much more satisfied with that approach. And we hired scientists and so on to build these systems and improve them.

Okay, so now let me talk about the Medallion Fund. So when I teach introductory finance, I usually start with a single equation on the board. And the equation is: mathematics plus money equals finance. And I would argue that the Medallion Fund pretty much epitomizes that, because the system, as you described, has yielded just extraordinary returns. And at this point, the track record is confidential. But you did give an interview, one of the very few interviews that you gave, in 2000 to Hal Lux. And so I want to just read to you what was written at that time about the Medallion track record: 'Simons, by contrast, just keeps getting better. Consider his performance over the past decade.' And this is between 1988, when it was launched, and 2000.

Since its inception in March 1988, Simons' flagship $3.3 billion Medallion Fund has amassed annual returns of 35.6%, compared with 18% for the S&P during that same time. And that was after fees. That was after fees. And at that time, the fees for the Medallion Fund at its peak was 5 and 44. So 5% fixed fee and 44% of the profits. So that track record yielded 2,478.6% return over the 11 years from 88 to 99. And the next best fund in the hedge fund databases at the time was the Soros fund, the Quantum Fund, which was only 1,710%. But that was as of 2000. So first question: how's the track record been since then? Because nobody knows for sure. I know.

And a few other people know. The track record has continued good. I don't know if at that time we'd already raised the fees to 5 and 44. First we raised them to 5 and 36. And then the investors all complained. They just wanted to have more. They said, 'How can I get more?' And then 5 and 44, and there was still a very good return at 5 and 44, so no one wanted to redeem. But we realized that there was a limit to how much we could manage. We understood the system, and it could manage a certain amount, but it couldn't manage a huge amount. You know, trillions, hundreds of billions—it certainly couldn't manage that kind of money. So we decided to—and because we were making so much money, the fund was growing internally.

First, we prevented any outsiders from—no new investments from outsiders, except for the employees. And then we decided to buy out the outsiders. That was in '03, I think. '03, '04, and '05. By the end of '05, we had bought out all the outside investors, and it was just owned by the employees. And it did grow to some extent, but because it did, and it could manage that much, but at a certain point it's been capped off. And we started in that same year, 2005, we started some funds for the public, which have done very nicely. And they have no clash with Medallion. They're much longer-term expectations, but those funds have done very nicely. And so at the moment, there's $45 billion in those funds being managed.

But the Medallion Fund has always stayed—

The Medallion Fund has stayed at a certain size, which I won't share, but it's not as big as 45 billion. Yes.

Can you share with us how many employees you have?

Yeah, we have 310 or 20 or something like that, counting everyone. We have a lot of scientists. We really, you know, you have to, in a business like this, just keep making things better. Keep improving the system. because other parts of it are going to wear out after a while. People will catch on to this, or they'll catch on to that. So you just have to, like in any business, in any business, you just have to make things better and better and better, because that's what everyone else is trying to do. And so we hire the best scientists we can. People have said to me, oh, you're not doing the world a favor. These people could be doing great. They'll make all this money, and then they'll give it to charity.

I'm not worried that it's going to ruin the world by having good scientists working at Renaissance. But we do have good scientists working there. And that's been the model. The model has been, first, hire the smartest people you possibly can. That's a sensible principle. Work collaboratively. Let everyone know what everyone else is doing. Now, some firms that do have these systems, they have little groups of people: 'This is ours, and this is theirs,' and they'll get paid accordingly and so on, to how their system goes.

We have one system.

And once a week, there's a research meeting. If someone has something new to present, it gets presented. It gets chewed up and looked at. Everyone has a chance to. The code is there. They can run the code and see what they think does really work and so on. So it's a very collaborative enterprise. And I think that's the best way to accelerate science, is people working together. And so that's that. And we have great infrastructure, wonderful infrastructure, so people can get right to work. We've had people come in and start to work and say, 'My God, I'm doing this after three days. I've never been in any place where you could get up and running so quick.' So it's well-organized, and we have great people.

So obviously, much of what Renaissance does is confidential. And in particular, even the people that you have are confidential. But I think it's fair to say that if you looked at the quality of the colleagues you have, they are probably among the top scientists in their field in many different fields. Is that fair to say?

Well, I don't think there's anyone who would. Okay, I'll tell you a funny story. We have a Renaissance colloquium every week. Someone comes and gives a talk, a scientist. And it's open to the public. And one day, a young astronomer came in. A friend of his already worked at Renaissance. And this guy came, and he gave a very good talk. He gave a very good talk. And I took him aside afterwards and says, 'You know, your friend is here, and you would like working here. You would like working here. We would like to have you work here.' And he said, 'Well, it sounds very appealing. But right now I'm in a project, a science project, that I really want to complete before I think about doing anything else.'

So he won the Nobel Prize. He won the Nobel Prize. He was one of the two teams that learned that the universe, instead of decelerating, was actually accelerating. And it was big news. So I think he made the right decision. Most people would rather have a Nobel Prize. So he's the only scientist of Nobel Prize quality that we almost got. And I don't think anyone else in the firm is probably that good, although some of them—

I've been terrific.

Some of them, I don't know. They don't give Nobel Prizes in mathematics. But they do in physics, of course. And we have a lot of people who are physicists. Experimental physicists do well. Astronomers do well. They look at a lot of data and analyze it. And that's what we do, is analyze data.

So that leads me to my next question. How do you manage all of these incredibly talented people, often with really huge egos? You talked about collaboration. But having been a chair of a department, and you having been a chair of a department, it's not always easy to get big egos to collaborate.

Well, a department chair does not have that much power. Right, and I'm sure—and any professors in the audience know that—you don't have to do what your department chair says. 'Oh, he says you have to teach this class. Okay, you teach this class.' But as far as your research goes, you can do what you want. But we did, at Renaissance, say, 'We'd like you to work over in this area,' or, 'work over in that area.' But nonetheless, so there are groups. There are groups that work on different things in the research area. But because they see what's going on every week in everyone else's group, they can sometimes, and often do, make a suggestion: 'Hey, you know, what we're doing over here, I think, could affect what you want to do over there.'

The way people are paid, everyone gets a piece of the profits. And—but they're judged. It's not, 'What did you accomplish this year?' You know, I'd have every year people come in—would come in to me and say, 'You know, I made so much money for the company. My work made so much money for the company last year. I deserve a big raise.' I said, 'Oh, yeah, well, that was good work. Didn't it derive from so-and-so's work?' He says, 'Yeah, yeah, but I really made it better.' And I said, 'Well, and didn't you work with Joe and Susan on this?' 'Yes, yes, I agree. I did. I did that.' So I said, 'You know, if I added up all the money that everyone who comes in here tells me they made for the company this year, it would be five times as much as the company made.'

But we look back on three years, four years, five years, how they've done. They'll get raises accordingly. And that's the way it works. And people—well, no one's perfectly happy with everything. And I can't say there's no one who thinks he should be paid more, which is human nature. But everyone's pretty happy. It's a very happy place.

It's a very happy place. So this leads me to the final point that I wanted to make about the Medallion Fund and what you built over the years. So you must know that you and your colleagues at Renaissance have been an inspiration to many, many quantitative investors, many students here, many faculty, myself included. And the favorite topic among quants getting together for beer or stronger is, 'How do you do it?' And, 'Why is it the case that even to this day, there's nobody close to Renaissance?' And so I have my own conjecture that I'd like to run by you and get you to react to it. And my conjecture is a little different. It's not about the systems. It's not about any particular magic formula or algorithm.

But rather, being at a management school, I guess I'm biased. I actually think it's about the management. Specifically, I think it's the combination of the fact that you actually ended up being a very good prop trader. First, before you even thought about the mathematics, you actually became a good trader. And then, with that intuition of what it means to make money and lose money, you ended up being a good people picker. And you ended up building around you an extraordinary team. And that team has grown based upon the culture that you created. You just mentioned that at the end of every year, you have these awkward conversations with people. Who can adjudicate among these very big egos except somebody that commands the respect of anybody?

So do you agree or disagree with that characterization?

More or less. I mean, it was certainly good to have done fundamental trading to just understand the mechanics of markets and so on. Of course, we don't do that. People don't do that. And I have to say, I left Renaissance when I was 72. So that was almost nine years ago. And the management there just carried on. We had some great leaders. And we haven't missed a beat. They've done just as well, maybe better, than they would have if I had stuck around. But I felt it was time for the younger people to take over. I had started spending more of my time with our foundation, which is a topic of next week's encounter, and so I thought, 'Okay, what...' It was two people who were co-executive—I don't know, I don't remember what their title was, but they had a very high title—and gradually I had given them more and more responsibility.

So when I left, it was just fine. And I always keep pushing them to hire very smart young people. And that's, I think, my biggest contribution. I'm the chair, and we meet every month and so on. But just hiring great young people into the business is the best thing you can do.

And your tenure as chair of Stony Brook's math department prepared you for that in some ways.

Yeah, sure.

So I want to turn to a few miscellaneous topics now. And again, feel free to tell me that you're not interested in them. As early as 2003, Renaissance Technologies raised concerns about the Bernie Madoff Ponzi scheme. How did you get wind of that, and what motivated you to even say anything to the SEC?

I had had money invested with Madoff for a long time—not the firm, but some relatives of mine. Our foundation had an investment with Madoff, and I knew him a little bit. And he was really amazing. He kept coming out with these very, very steady returns, very steady returns, come rain or shine. So at a certain point, I said, 'This guy has to know something that we don't know.' In fact, he certainly knew something that we didn't know. I had all the tickets—what do you call it, the confirmations—going back years. So I asked one of the guys at Renaissance—well, in the company, I was at Renaissance then—to look, analyze these trades that he was doing and tell me what you learned. 'What's his secret?'

So this guy went to work, and here was his conclusion: 'Well, when they put on a position, if they're buying something, they generally get a very good price—maybe the low of the day if they're buying, maybe the high of the day if they're selling. But most of the time they're not putting on positions. They stick with the position. That accounts,' he said, 'for maybe 10 percent of their profits. They claim they have T-bills sometimes, and so there was interest. But 80 percent of the profits was a complete mystery. It was a complete mystery.' Now, what they did was—let's see—they would put on a big position, according to the tickets, with stocks which would, the collection of which would be approximately the S&P.

And then they would buy a put or a call to protect themselves against outsize moves. Well, from what we understood, they had a huge amount of money under management. So you would think when they put on these puts or calls or whatever it was, it would move the market actually in those things. But we could see no evidence of that. They said they were putting on these puts and calls. But you look at the put and call market, there was no evidence of any such activity. So I thought, 'Well, let's get out of this thing.' Even Medallion had a little bit invested in it. Medallion had extra cash at that time, and we had put it with Madoff. So we sold it, and then nothing happened. And several years went by.

One of my relatives called me and said, 'Do you still like Madoff?' And I said, 'Well, I can't tell you to take your money out of it because he's been going for a long time and he keeps on going and he must know something.' I said, 'I took my money out.' I couldn't advise someone to take their money out. It never dawned on me that it was a Ponzi scheme. I didn't know what the heck he was doing, but I just didn't like the looks of it. So we couldn't understand what he was doing. So that's why we got out. Five years later, the crap hit the fan and he was outed. And it was—well, everyone knows what happened next. And actually, they looked back six years for any profits that you may have made. So our foundation had to give back some money to people who had lost it.

It was just the craziest thing, the craziest thing in the world, Madoff.

The irony is that the fake track record that Madoff posted was actually not as good as the real track record the Medallion Fund posted.

That's true. That's true. Well, it was pretty steady. I have to say that. It was pretty steady. But it was... And then the... I don't know, the SEC started investigating us because some people had said, "Look, these Renaissance people, we don't know what they do either." Because of course, no one knew exactly what Madoff did. And of course, we didn't tell people what we were doing. They couldn't see our portfolio. They couldn't see anything. By that time, I think we had already given all the money back to the investors. So I could say, "Well, look, we can't be doing anything wrong because it's all our own money. We've already given back all the money to the investors." But they did study us and work us over for a while.

And of course, they couldn't find anything bad. And then they went home. But it was as a result of Madoff that we were so examined by the SEC.

So right around that time, of course, was the financial crisis. And that probably precipitated Madoff's unraveling. What do you make of the financial crisis and the aftermath?

You're talking about 2008? Yeah. Well, it should never have happened. It should never have happened. There were these mortgage-backed securities had been created. They'd always existed, mortgage-backed securities, but very fancy ones were getting created. And they had all kinds of this and that and so on and so forth. And in the old days, the rating agencies, their customers were the buyers of bonds, the bond rating agencies. So they wanted to do right by their customers. But at a certain point, you'd get a report every week or a newsletter or something like that. But with the internet coming along, people were sharing this who didn't subscribe. So the rating agencies decided, okay, we're not going to charge the buyers of the bonds.

We're going to charge the sellers of the bonds. Now, if you think about it, that's a conflict of interest because they really want to have the bonds sold so maybe they won't be so tough in rating them. And that's what happened. The stuff was sold, which you'd have to be a moron to stamp Triple-A. And people were getting mortgages, no-doc. You'd walk in, you'd get a mortgage. "How much money do you have?" "Oh, I have $100,000." "And how much money do you make?" "Oh, I make $200,000." "Okay, fine. We'll give you this much of a mortgage." Well, they didn't even ask for documents in many cases or your income tax forms. And why were the banks being so lenient? They could sell them to people who would package up these mortgages and put them—they would ultimately end up as a mortgage-backed security, stamped AA, Triple-A.

So everything had just become very lax. And Bear Stearns, for example, which was a firm that we had always had great confidence in, they were a very conservative outfit. They almost went down the drain because of this. Fortunately, they didn't. We had money with them. And as soon as it looked like they were going to be in trouble, we bailed out and got out three days before they folded. Then we were working with Lehman Brothers. And we had a lot of money with Lehman Brothers. But this is Medallion and so on. We had a lot of money with Lehman Brothers, and some of our outside funds also did. But it was beginning to look not so good for them. And I called up the head of Lehman Brothers and said, "You know, Dick, we're going to have to take some of our money out."

We're going to have to take half of it out. I'm uncomfortable with that much being with you. And he said, 'Okay, fine.' So we did that. And then things were looking worse and worse. And we had some insight into their balance sheet, and we knew it was stuffed with these—a lot of the assets were these mortgage-backed securities. And I called him, and I remember I was driving, and I said, 'Dick, we're gonna have to take out the rest of the money.' And he said, 'Oh,' he said, 'I thought you called me to buy these new bonds that we're issuing. They're oversubscribed. But for you, I'll give you a piece.' And I said, 'I don't want to buy your bonds. But I'll wait a few days and see how they sell before I take the rest of the money out.'

A few days went by, then the list of buyers of these bonds came out. And it was the most unsophisticated group of—an obscure teachers' retirement fund. No reputable or big outfit was buying these bonds. And so I called them and said, 'Okay, we're taking the rest of the money out.' And that was three months, I think, before Lehman collapsed. But if the rating agencies had done their job, this would not have happened. But no one wanted to blame the rating agencies, because who's ever heard of rating agencies? I mean, the newspapers want to blame the banks. They want to blame the big players. But it was maybe not quite as simple as I'm saying. But it was a mortgage-backed collapse, and these bonds were rated improperly.

That's what happened.

So let me now, since we're getting short on time, I want to make sure there's plenty of time for audience questions. So maybe we can open it up. And while we're looking for our questions, raise your hand, and then Kelly and Lily will pass a mic to you. While we're getting our first question, maybe up there.

That guy was the first one with the white shirt to hold up his hand.

So, yeah.

Hi, I'm Miles. I'm a junior at Harvard. I was wanting to ask, so over the years, obviously, the general markets have changed with the advent of more computer technology. Has that shifted your view on fundamental versus quantitative investing? I mean, earlier, you seemed to kind of point to the fact that at the end of the day, fundamental investing is very wishy-washy and based on intuition. Do you think that that is always true? Or do you think there are people that truly have an advantage in fundamental investing?

But people have, in doing—is it possible to do?

Yes. I mean, obviously, Renaissance is quantitative. But are you always pro-quant over fundamental? Or do you think there's room for fundamental investors?

Look at Warren Buffett. He's had a great career. I don't think he has a computer on the premises, except maybe to count his money. But no, it's a perfectly legitimate way to invest.

Then I guess, what are the skill sets that differentiates a good fundamental investor from a good quantitative investor? Say it again? What are the different skill sets that separate a good fundamental from a good quantitative investor?

Oh, I think it's a world of difference. I think a good fundamental investor, let's say in a company, he wants to evaluate the management, have a sense of the human beings that are running this thing. He wants to have a sense of where the market might be going. And it's a set of skills. And some people are very good at it. Quantitative stuff is a different set of skills, which suited me. And so, does that answer your question? Close enough, yeah.

Mr. Simons, as quants with increasingly powerful tools seek out inefficiencies in the markets to exploit, and we keep exploiting them until there's nothing left to exploit that'll overcome transaction costs, are we destined to slowly drive ourselves out of business? And if so, how long do we have? Who is "we"? We quants.

Oh, "we" quants.

As we keep seeking the inefficiencies to exploit and thereby diminishing them.

Well, that's a good question. Yes, inefficiencies do eventually get traded out if they're discovered. But the market is not static. It's dynamic. Things change. And therefore, there's room, I think, for new inefficiencies to materialize. And so I think it's never going to be all inefficiencies are out of it, there's nothing to discover. On the other hand, so far, we've managed to—our returns have been more or less stable for a long time. We keep finding new things and throwing out things that are no longer working.

Do new things emerge as quants are looking for new things so that quants are exploiting other quants?

I have no idea. Okay, well, she's giving out the microphone.

Hi. What's your favorite algorithm?

What's my favorite algorithm? I'll tell you my favorite algorithm. My favorite algorithm is something that I worked out when I was at the Institute for Defense Analyses. And it has to do with solving a certain classical problem in the field. And I solved it. But it's classified. It is. I solved this problem. And they made a special-purpose machine at NSA. And I heard that 30 years later, they were still using this special-purpose machine to implement this algorithm. So that's my favorite algorithm. And it's classified. So there's a guy right there. Well...

Hi.

You'll get your turn.

Hi. Right here. I was wondering what you did over time to kind of protect your intellectual capital. You had a lot of people working for you. How did you keep everybody rowing in the same direction? And how did you protect kind of the special sauce?

Yeah, it's a good question. It's a good question. Well, everyone signs a forever non-compete—no, not a forever non-compete, a forever non-disclosure. And after you've been there a couple years, there's a non-compete agreement that you're invited to sign, and pretty much everyone does because there's a lot of money that's—out of your bonus, a certain amount is held back for a while and then invested in Medallion, actually, and then you get it over time. But so there's always—you always have a lot of money on the table which you've not yet gotten, received, which keeps people from running off. We've only had one incident: a couple of Russian guys left and stole some of our secrets. And, well, we had a lawsuit against them and so on and so forth.

And well, they're not in business anymore. And the system that they had made off with is now pretty antiquated, so we're not worried about that. But it's a very good question. But the main reason people don't want to leave, it's a very nice atmosphere. It's fun to work there. People get paid a lot of money. There's no doubt about that. And it's fun. So we've had people retire. And, but they've, with the exception of those two Russians, they've never gone into any investment business. They've just retired and, I don't know, done this and that. One guy went up to the Broad Institute and became a terrific scientist, genetic scientist, working for Broad. So, I think if—turnover is very important in any company.

And if a company has a great deal of turnover, there's something wrong. And you know there's something right if turnover is very low. Of course, one thing that could be right is you're paying too much. But it's good to have low turnover. And that's what Renaissance has.

Hi. At the beginning of your talk, you mentioned that you wanted to understand how the system that was presented to you, you want to understand how it works. And at another point, you also mentioned that once you have the system, it's all about making it better and better. So my question is the balance between those two, because as you try to make your system better and better, there is the risk of making it more complex to a point that you don't understand it anymore? How do you balance improving your model and keeping it simple enough to understand it, actually?

Well, that's a good question. It's completely understandable because you can understand it if you wanted to spend a week doing nothing but understanding the system. It's all written down. So it's perfectly understandable. There are a lot of predictive signals. There's a lot of stuff going on. It is very complicated. But it's not ununderstandable. So we understand it.

I imagine I work with Andrei Stern.

Can you stand up?

I'm sorry. Yes. So you mentioned continuous improvements of the systems. I wonder, is Medallion of today, the core of it at least, similar to what it was 10 or 20 years ago? Or has Medallion sort of reinvented itself over that time to be completely different things?

You know, I didn't. My ears aren't so good. Could you understand what he asked?

Yeah, so is the Medallion of today pretty much the same as it was 10 or 20 years ago, or has it reinvented itself?

Oh, it's continuously reinventing itself. I think there are some parts of it that would probably have been there for 10 years or maybe even 20 years, but that's less and less as new things come along. So like I said, you just have to keep—you just have to keep running. People will discover some of the things that you've discovered, then they'll get traded out. And so you have to keep coming up with more and more things. And we have a great computer system, and great scientists—very good ones. So that's the answer.

Hi, Jim. So you mentioned during the beginning phases of the Medallion, there was a short period of time when you guys weren't doing so well. I'd like to ask, at any point in time, did you doubt yourself? And if so, how did you will yourself to continue and eventually succeed?

Well, in that period, well, we shut it down. I wasn't certain. But I did feel that we could improve it to the point where we were happy to continue trading. And I've never doubted that things would keep working reasonably well. I think we've been lucky to a certain extent. Luck plays quite a role in life, and a lot of people don't admit it. If a guy's business fails, he says, "Oh, it was bad luck." If a guy's business succeeds, he says, "Oh, I'm a hard worker, and naturally it succeeded." But there's luck, everyone, so far. We've been pretty lucky. But I haven't been very worried. You know, there are times when a month goes by, we don't make money one month. It's very rare we don't make money in a month. But once in a while, that happens.

But it's always come back.

OK.

There's a woman. Right there, I'm looking at you. Didn't you raise your hand? Well, we'll see if we can get you up.

What I find interesting is, Dr. Lowe, I know that you're big on behavioral finance. And Dr. Simons, it seems like when you talk about your past, you talk about your gut instincts. And you kind of just pass it off as, "I felt this way." But I was wondering if you had more, I don't know, if your internal compass is a little bit better than most in guiding you through tough decisions, like the last person asked.

Again, I couldn't understand too well.

I'm sorry, the acoustics in this room are not great. I think that she was asking about the role of human behavior in quantitative investing, that the fact is that you do have some kind of a gut instinct about when a system is working or underperforming. What role does that play, this intuition and judgment, in thinking about these strategies?

Well, I mean, if you see something that's steadily losing, that does not take intuition to determine that something is wrong. And you ought to stop doing that. But well, intuition, you know, scientists, some scientists have pretty good intuition, scientific intuition. How does that happen in math? You might say, "Hey, this operation worked over there. Maybe it'll work over here. I'll give it a try." So as people come into the firm, they learn what has worked. And sometimes they say, "Oh, if we perturb that a little bit, it could work even better," or stuff like that. Some have better scientific intuition. But I think it's scientific intuition. It's not market intuition that the guys who work there are using.

So we're just about out of time. So I want to have one last question, and then we're going to wrap up with a question.

Hi. Would you say that fundamental approach in your investment modeling is primarily inductive reasoning-based or deductive by nature? In other words, data-driven to come up with your models or more logic-driven to come up with your models?

Well, we certainly are logical. It's hard to work without logic. There's a lot of data. Sometimes one might come up with a number of things and just try them all out and see if one of them works. Now, the danger of that is if you try enough things, something's gonna work, but you have to be sure that the statistics are still in your favor. If there was so much data that even though we tried a million things and one of them worked, the probability of that was very, very slim. And therefore, you were probably OK. So we do try a bunch of stuff. And I don't know if that answers your question, but OK.

So Jim, in wrapping up, I'm going to ask you two quick questions related. One is that you moved from Stony Brook to doing trading because you were working on a problem that you were struggling with. And to this day, it's still unsolved. You went back to working on it. It's a tough problem, I imagine. Did you encounter any unsolved finance problems that you think about and struggle with? Unsolved finance problems? It doesn't seem like they're in any given track record of Medallion.

Well, I think there's a lot of people who will worry about how they're going to pay their rent, which is perhaps an unsolved problem as far as they're concerned. I don't know what an unsolved financial problem really means. OK. Have you had any financial problems? A whole bunch.

I would love to get access to the Renaissance research staff to have them working with us on it. But the last question is, for all of the future quants in the audience, any advice about how they ought to approach this field and career?

I think any potential quant should just not get into the business. We don't need to have a whole lot of people in this business.

OK. Well, on behalf of MIT Sloan...

Well, what advice could I give you? It's just work hard, hire good people, and it's not easy to get into the business because you need big databases and a lot of computers and stuff like that to even start up. But if you have an idea and you can test it out and think it's good, more power to you. That's all I can say.

Well, Jim, on behalf of all of us here at MIT, we want to thank you so much for sharing your wisdom with us. And I think that your career in finance is just extraordinary, and it's been an incredible inspiration to many, many people and will continue to be an inspiration. But what I want to tell everybody is what might be even more inspiring is what you're going to talk about next week, because not only have you made tens of billions of dollars for investors and billions for yourself, but you've also given away a tremendous amount of money for philanthropic purposes. And we're going to hear about that next Wednesday. So I urge all of you to come back and hear the third in the three M's of mathematics, money, and making a difference.

So thank you very much.