This 1 hour MIT lecture by Jim Simons (Quant King) will teach you mor...

Well, that was a heck of an introduction for which I thank Is because I was worried that my talk would be too long. And Is just gave about half of it. So, I can concentrate on the second half and I'll fit the whole thing in in the time allotted. Well, actually it's a great pleasure to be here. I've been in this room before, I believe. It looks familiar. But everything else about the institute's changed a great deal. I always wanted to come here. I lived in the area and I wanted to come here and study mathematics when I was a kid. And I'll tell you about an amusing bump on the road. So, when I was 14, I had a job at Christmas time in a Brecks Garden Supply place. I don't know if maybe it's still here or not. Anyway, I worked in the basement in the stockroom putting away stock. That was my job and I was terrible at it. I couldn't remember where the hell anything went and there seemed to be no system and they were not pleased with my work and demoted me, if you can imagine a demotion from that level, but I was demoted to floor sweeper. Which I loved because it was easy. It took no brain work and I could think and I like to walk and think and what was nicer than that and you get paid for it and push a broom. Anyway, Christmas came and the season was over and the basement area was run by two men and a woman. They had the job of the stockroom and so in the course of saying goodbye to me, they tried to be nice and said, "Well, what's your future plans?" I said, "Well, I want to study mathematics and go to MIT." Well, they thought that was the funniest thing they had ever heard. The guy who couldn't remember where to put the sheep manure, he's going to be a mathematician at MIT. Well, I fooled them. I applied to MIT and I was accepted. But then I got a call from Wesleyan University. I'd never heard of Wesleyan University. I was a high school student. I didn't know everything. And they said, "Well, we've heard about you and we'd really like you to apply to Wesleyan." So, I made a few inquiries. It sounded nice. I said, "Okay." So, they said, "Come down for the weekend. We'll do this for you and that and go to a class." I must have come down on a Friday. Whatever it was, I had a lovely time at Wesleyan. It was a beautiful place. And I was swept off my feet by their interest and the prettiness of the place and so on. So, I applied to Wesleyan and I was rejected. So, there was no choice. I was destined for this place. So, anyway, I did come and I did study mathematics and it went all right. And one of the clinchers for a career in mathematics occurred when I first saw... Now, there was a professor named Warren Ambrose who was a very inspiring mathematician. Some of the older folks here probably remember Ambrose. Singer I didn't know at that time. But there was a joint called Jack and Marion's which was at Coolidge Corner. And I've learned it disappeared in 1971. But this was 1956 or 57 and it was open until 3:00 in the morning and we used to go there sometimes late at night to get a sandwich, I and my friends. And one night I saw Ambrose come in at midnight, 1:00 in the morning, and this other older fellow Singer I would later discover and he must have been in his 30s by then. And Ambrose was maybe 50. Anyway, they came in sort of dressed like kids, sat down at a table, and were busily doing mathematics. And then on several occasions after that, I'd see them come in. And I thought, "This is the coolest thing in the world. What a life just to go out at 2:00 in the morning with your friends and do mathematics over coffee and probably cigarettes in those days." I don't remember. But that was the clincher. It seemed like the world's greatest career. And I pursued it. I did play a little poker, it's true, along the way. And along with Ambrose and Singer, I made two other good friends at MIT, boys from Colombia. And when we graduated, someone asked me out in the meeting room there, was it true that I drove a motorbike to Brazil. Well, it was almost true. We did with my Colombian friends ride motor scooters to Bogota. From Boston to Bogota we did. And that was miraculous that I survived that excursion. But we did get to Colombia, and that had a big impact on me because I'd never been... I don't even think I'd step foot in Canada. And now here I am in Colombia. And in those days, it was an undeveloped country, but it looked to me like it could do anything. I mean, any business could flourish in Colombia because they didn't have them. And these boys that I went to MIT with, they were very bright boys. And I knew, since they always beat me at poker, that they probably be good businessmen, which it turned out that they were. We'll get back to that in a moment. Anyway, that was graduation. I went out to Berkeley. I'm delighted to see my thesis advisor right over here, Burt Coston. And he taught me a lot. And then I came back to teach at MIT. But on passant, I convinced my friends in Colombia that they really should start some kind of a business because they were cut out for it and I told them I was going to come down, which I did, and not leave until we found something that we could start. I had no money, by the way. I had no status in this... the threat that I wouldn't leave was not well... maybe it worked, now that I think of it. Anyway, maybe they did want to get rid of me, but in those 2 weeks we did find a business. They did start that business and I scraped up some money and my father invested in it and so on and we had a little piece of that business, which later provided the basis of a career change. But I was teaching at MIT. I had invested in this business by borrowing money. A couple years were going by. I needed to start paying off these loans and like many a business that you start, we expected dividends within 18 months and we had a remarkably puerile view of how a business works. We did eventually get dividends about 12 years later in good amounts, actually, finally, but I needed to pay back some of these debts. So I went to the Institute for Defense Analyses in Princeton, New Jersey. This was a joint on the Princeton campus at that time where they did secret government work and paid quite a lot and you could spend half your time doing your own mathematics and half your time doing their work, whatever their work was. It involved computers. And I well, you know, it was secret. I don't want to talk about it. They knew and I knew. And I loved it all. It was great. I turned out I wasn't bad at their work. It was extremely interesting to model things and see them programmed up, God knows not by me, but someone else could program them up and test out theories of models, would it work, would it not work. And at the same time mathematics was going extremely well and I did do the work there that subsequently got this Veblen prize, solved a kind of a major problem in geometry. And it was going swimmingly. It was a great experience. I was there. But then this was the time of the Vietnam War and the president of this organization, two steps up from my local boss, a guy named Maxwell Taylor, some of the people with gray hair in the room might remember General Maxwell Taylor. Anyway, he ran this outfit and he wrote a crazy article. It was... Well, I thought it was crazy. In the New York Times magazine section saying how we were going to win the Vietnam War and it was just, you know, victory was days away or whatever it was that he was touting. And I didn't agree with that. Now, the work we were doing had nothing to do with the Vietnam War, but it annoyed me that the head of my organization could take such a silly point of view and I wrote a nice letter to the New York Times expressing that view. Which they kindly published. In that same Sunday edition. Several weeks later. So now I was on the watch list. I didn't know it but I was on the watch list for this place. And then a guy came along a few months later, three or four. And he announced that he was a reporter for Newsweek magazine. And he was doing an article on people who work for the Defense Department but were opposed to the war. And he was having a hard time finding anyone to interview but he heard about me. He read my letter. Could he interview me? And I said, "Sure. What did I know? Of course you could interview me." You could see how sophisticated I was at that time. So he said, "Well, what do you do?" Well, I blathered on about this and that but I said, "But my algorithm has been... since they say you can do 50% of your work on mathematics and the other 50% on their work. Right now I'm doing only mathematics and I'm keeping careful records and when the war is over then I'll do an equal amount of their work. And that's my approach." So... No, I mean, it's not unreasonable. So I went back to tell my local boss. It was the first intelligent thing that had occurred to me. A little late perhaps but I decided I better tell my local boss that I gave this interview. And he said, "You did? And then what did you say?" And I said, "Well, I said what I said." He said, "Hmm." He said, "I better call Taylor." He picked up the phone. Called this General Maxwell Taylor. There was a little silence at the other end and he couldn't hear what Taylor was saying and then he hung up. He looked at me and said, "You're fired." So, I'm fired? I'm fired. He said, "You're fired." And well, it was the first time and happily the last time I was ever fired, but I was fired summarily. And I said, "Well, I'm a permanent member." That was my title, permanent member. He said, "I'll tell you the difference." And then when I started, I was a temporary member. I was a temporary member and then I became a permanent member. So, he said, "I'll tell you the difference between a temporary member and a permanent member." I said, "Yes." He says, "A temporary member has a contract." Well, I guess it was true. I had a letter when I was a temporary member, but when I became a permanent member, I didn't have a contract. And so, there I was out on my ear, but not especially worried. And I did go against Izzy's advice and I did take the job as chairman at Stony Brook. I figured it was better to be a fire-roarer than a firee. And there was a fair amount of firing, regrettably, that had to be done. The department was very weak. But we hired a lot of people. We had a good time and we did build a good department and I did do some mathematics there that ended up with Chern-Simons that ended up being pretty useful in physics. And it was there that I did learn about the Bohm-Aharonov experiment and the relation, the apparent relation between what we mathematician called bundles with connection and what the physicist called gauge theory. And I did run off to MIT and sit... not MIT, to see Is. He was at Rockefeller that year, sat in some place over coffee and explained this connection. And it was a very exciting time and I think there were conversations like that going on perhaps in lots of coffee shops about how physics and the way it was evolving and the geometric side of mathematics were really becoming quite intertwined and now they're inextricably intertwined as far as I can see. So, those were all good times, but then as Is said, as he's been giving my talk, I did get frustrated. I was stuck. I was really stuck on a problem. I was trying to prove a certain number was irrational. I guess you all know what an irrational number is. It might not seem so important that a number be rational or irrational, but in this case it seemed to have a lot of implications. And I was wholly incapable of solving this problem. And it's still an open problem. This whole area is a great set of problems about volumes and whether they're rational in some sense. So, anyway, I was very frustrated. I had been on the wrong side of a divorce. I was on turned out on the right side of my next marriage. I did have a new girlfriend who's sitting right there, my new girlfriend. And my South American business at long last started paying some dividends. In fact, they started paying a lot of dividends and I had some money. And I invested that money and found out that I wasn't bad at it. And all of this made me think, okay, it's time for a change. And so, 1976, I was 38. I felt I'd been a mathematician all my life, but really sort of since I was 18, I think I was. So, I spent 20 years in that game and decided to go into business. And never occurring that I would apply mathematics to the business. I did subsequently, but I just felt, you know, you read the newspapers, you think you'll do pretty well. And we did okay, but after a little bit, I started collecting some data and I figured there's something to be modeled here. There's something to be modeled here, like we used to do in the old IDA days. So, I brought in the best modeler in the world. A guy named Lenny from IDA and persuaded him to come and we're going to make these models and do great. And Lenny started making models with me and we were fooling around. But I kept trading and then Lenny seemed to get less and less interested in models and more and more interested in reading the news reel. In those days, it was just a roll. It would tick away and you'd read the news. And Lenny was always reading the news instead of thinking about models. And then he started having opinions on what was going to go up and what was going to go down. This was all foreign currencies, bonds, that kind of stuff. And I started listening to him and he was right. He was right enough times to say, "Okay, the hell with the modeling. Let's just try to make some money." And we had a remarkable run in the two years since I said the hell with the model. We multiplied our investors' money by 12. Now, that sounds pretty good, right? For it was we were incredibly lucky. See, the last word up there is luck. Good luck. And we certainly had good luck. Had a lot of good luck in my career. So, but in the back of my mind was, "Well, okay, Lenny doesn't want to build models, but maybe we could build some. Someone else could come in and build models." And another fellow, Jim Ax, who was quite a famous mathematician, left Stony Brook to come and he did build some models. So, those next years was a combination of fundamental trading, I did venture capital, I invested in all kinds of things, but the models kept being built and working better and better. And finally, at the end of about a 10-year run, it was clear to me that this gut-wrenching business of fundamental... I mean, if you're doing fundamental trading, one morning you come in, you feel like a genius, your positions are all your way and you think, "God, I'm really smart. Look at all the money I made overnight." Then the next day you come in and they're going to get you and you feel like an idiot. We were pretty good at it, but it just didn't seem to be a way to live your life. So building models, go with the model. So, by 1988, I decided it's going to be 100% models. And it has been ever since in the business that we built. So, some firms, investing firms say oh, they have models. And what they typically mean is, we have a model and it advises the trader what to do and if he likes the advice, he'll take it, and if he doesn't like the advice, he won't take it. Well, that's not science. You can't simulate how you would do... how were you feeling when you got out of bed 13 years ago when you're looking at historical simulations. Did you like what the model said, or didn't you like what the model said? It's a hard thing to back test. So, if you're going to trade using models, you just slavishly use the models. You do whatever the hell it says, no matter how smart or dumb you might think it is at that moment. And that turned out to be a wonderful decision. So, we built a business 100% based on building computer models starting in those areas that I mentioned, currencies, financial instruments, gradually moving to stocks, and finally to anything that moved. Well, it had to be tradeable. Had to be liquid. We were always liquid, but bringing in data... in those days we sent gals down to the New York Federal Reserve to copy histories of interest rate numbers. I mean, they didn't exist in the '70s. You couldn't go and buy data, and there was certainly not online deliveries of all this stuff. So, to build the original models, we needed to collect a lot of data by hand, which we did. But gradually we got smarter, the models got better, and we brought in more and more people. It said we have the best math and physics department in the world at Renaissance. I don't think that's quite true. On the other hand, it ain't bad. We have a lot of smart guys, and we certainly hired people who were good at that kind of work. So, we started this thing called the Medallion Fund in 1988. In 1993, we closed it to any new investment by outsiders. Just employees could invest. By 2002, we started taking out the outsiders all together, buying them out. And by 2005, there weren't any left. So, since then, last 5 years, the Medallion Fund has been owned 100% by the employees, which today are about 300 employees. And well, people would always ask me, "Well, what's the secret? How come?" Because we're not the only quant firm in the world. We're not the only guys who have made models. I disparage some for whom model making was sort of a part-time thing. But there were certainly other outfits. But we seem to have done better than anybody. We had really established a remarkable record. So, people always ask me, "Well, what's the secret?" What are the secrets? Not question. I'm not going to tell you the various predictive signals and so on and so forth. Unless... No, I'm definitely not going to. We'll keep that. That's even bigger secrets than that stuff down there at IDA. These secrets are... We try to guard them. But the real secret sauce is that we start with great scientists. We start with first-class people who've done first-class work or we believe have reason to believe will do first-class work. And because I was there at the beginning and a few other people who had been pretty good at math and science, we had good taste and that stayed with the company. The second thing is we provide people with a great infrastructure. And I've had people come to us from all over; when they come to work they said they never saw it's more easy to get to work here than any place else. The data is easily shared. We have an alumnus here that I saw earlier who could have testified to that. I'm not going to ask him to do so but he would. So the infrastructure, give people a good infrastructure, and I think the most important thing that we do is have an open atmosphere. So my belief is the best way to conduct research on a broad scale is to make sure as much as possible that everybody knows what everybody else is doing. At least as quickly as possible. Sometimes you got an idea you want to keep it to yourself for a little while. You don't want to look like an idiot but the sooner the better. Start talking to other people about what you're doing because that's what will stimulate things the fastest. No compartmentalization. We don't have any little groups that say this is our system and we developed it and you run it and we get paid based on that and so on. None of that. Everybody meets once a week. All the researchers meet. Any new idea gets brought up, discussed, vetted and hopefully put into production. So it's an open atmosphere. And people get paid based on the overall profits. You don't get paid just on your work. You get paid based on the profits of the firm so everyone has an interest in everyone else's success. And those policies, no one of which seems so remarkable, turn out to be a pretty winning combination. Great people, great infrastructure, open environment, and try to get everyone compensated roughly based on the overall performance. And that's worked and it continues to work. So, that made a lot of money. It made an awful lot of money. So, we started a foundation, my wife and me, in 1994. And at first it was in her dressing room, if I'm not mistaken. Is that right? It was this little box with the folders in it and it didn't even have that big a dressing room. So, it was tight quarters. But gradually it expanded and she hired a few people and then a few more. So, we have this foundation which has actually grown quite considerably, not only in size in terms of dollars given out, but I think in terms of sophistication. And it's great. So, you know, my first career was a mathematician and my second career was this business of being a businessman. And now my third career is kind of being a philanthropist. And so, what does our foundation do? Well, it's one of the very few, I think, that's focused almost exclusively on basic science. So, we support basic mathematics, basic physics, a lot of biology, but primarily it's serious research across the board. I think we have an autism project which is interesting, but we do it from the ground up, understanding the genetics, trying to understand the neuroscience, and get insight into this condition, which will provide insight into how our normal brains work. So, we're focused on basic science, and that's very gratifying. Marilyn and I can feel good about that approach. So, it's per but we do other things as well on a small scale, but that's the... and I don't think there is... We were talking to some people the other day. There's probably not a foundation of our size that is as focused just on basic science. Now, at first, we gave to institutions. We gave some money to MIT. We helped the math department. I think there's a few endowed professorships here and so on, but in later years, we've been more focused. We've been focused on making bridges between mathematics and physics and the life sciences at the Institute for Advanced Study and at IHES in France. That's been an important and at Rockefeller, that's been important to us. The autism has been important. Now, we're getting more focused in math and physical science, looking at individual projects. MIT has an application to us for a theoretical computer science institute. And they're not the only applicant, as I know they know. It's a very good application, I have to say, but I also have to confess that there are a few other good ones, too. But some place is going to get a great institute for theoretical computer science. So, those are the kinds of things that we do. And, so I retired from Renaissance at the end of '09 and have never been so busy. People say, "Well, now that you're retired, what are you doing?" I'm busy as hell. So, even we have Math for America, something that we started seven or eight years ago to try to improve the teaching of mathematics. Everyone's concerned about it. American kids in mathematics education. Well, we've taken the view... we have a narrow view. Our view is have teachers of mathematics who know mathematics. Now, you might think, well, of course, but, regrettably, especially when you get to high school, the majority of the teachers of math actually don't know much math. So, that's not a productive environment in order to stimulate the kids in learning math or science or anything else. I mean, you sign up for Italian lessons, you don't want a Chinese speaker, you want an Italian speaker. You would say, I'd like somebody else, please. But the kids don't have a choice in the matter. So, we're trying to do that and we're doing it. Okay, so why don't we have enough teachers of math and science in the high schools who know the subject? And one answer is, well, if they knew the subject well, they'd also know enough to work for Google or Goldman Sachs or God knows where because today the world is so much more quantified and the economy is so much more based on quantitative methods than it was 30 or 40 years ago. So, the difference in the quality of the job, the pay, and the respect is so great for someone who's actually qualified to do a good job of teaching high school mathematics that they get pulled away and don't often find themselves in the classroom anymore. So, you have to make the job better. That means pay these folks more, which we do in New York through our program and a few other cities, and provide them more respect and support, which we do. So, if you pay a guy 25% more and make him or her feel special, all of a sudden the career is a lot better. You make the career better, people stay and will stay in the career. If it stays the way it is, people won't stay in the career. And if we don't do something, it isn't going to be a good situation. So, this is something you probably all have given some thought to. So, I'll conclude this. I was talking to my wife about this talk and what I might say and she said, "Well, you know, you should end with values." I said, "I'm not sure I have any values." She assured me that I had some values if only I could think hard about them. So, I guess I do have some values and I think they have guided me. So, I'm calling them guiding principles. Value sounds like a very serious business. But, I'll tell you some guiding principles which I think have worked pretty well. So, one thing I've always done is do something new. I love to do something new. And I don't like to run with the pack. For one thing, I'm not such a fast runner. So, if you're one of N people all working on the same problem at different places, well, me, I know I would be last. I'm not going to win that race. But, if you can think of a new problem or a new way of doing something that other people aren't all working on at the same time, maybe that'll give you a chance. So, do something new. Second, collaborate with the best people you possibly can. When you see a person, get to know a person who seems like a great guy or a great gal to work with in something, try to find a way to do it. Because that gives you some reach and some scope. And it's also fun to work with terrific people. I wrote down here, be guided by beauty. I really mean that. I think pretty much everything I've done has had an aesthetic component, at least to me. Now, you might think, well, building a company that's trading bonds, what's so aesthetic about that? But it is. What's aesthetic about it is doing it right. Doing it right, getting the right kind of people, and approaching the problem, and doing it right. And if you feel that you're the first one to do it right, and I think we were, that's a terrific feeling, and it's a beautiful thing to do something right. It's also a beautiful thing to solve a math problem or create some mathematics that people hadn't thought of before. So, be guided by beauty is not such a bad principle. And then I wrote down, don't give up. At least try not to give up. And sometimes it's appropriate to be trying to do something for a hell of a long time. And finally, hope for some good luck. So, that's it.

Okay.

Well, that's considerably more technical a question than I expected. I might as well give a lecture on differential cohomology. But yes, I suppose we consider all those things. We certainly look at risk modeling; risk is important to do. The fat tail risk refers to the fact that risk in financial markets is typically not a normal distribution. The tails of the distribution are heavier and the inside is not as heavy. So, understanding that is real important. We look at everything we possibly can and analyze everything we can. And so far we've got it reasonably right. Another question.

Okay. The question was do I consider high frequency trading to be socially useful? And if so, how much? How much can you make? I don't know how you... how useful. I consider high frequency trading to be natural and definitely socially useful. What's happened is that as the markets have become electronic and computers have been applied to generating prices and accepting trades and all the rest, the markets have grown tremendously more liquid. Spreads have come down. The bid-ask spreads have come down. The fellows who used to be the specialists on the floor, specialists of old, they were supposed to be the market makers. Well, they were full of baloney and they would create very wide spreads and at the first sign of trouble they would disappear. With electronic trading has come the ability to trade fast and that has brought spreads down and it has brought market impact down. So, there's two things that happen when you buy a stock. One thing, you pay a little typically, you will pay a little bit above the mid price. Just as if you sell it, you'll sell it for a little bit below the mid price. But the other thing that happens is infinitesimally perhaps, you move the market. If you buy 100 shares, you're probably not going to move the market. But if you buy 100,000 shares, you probably are going to move the market. Well, how much are you going to move it? If you're the only buyer out there, you're going to move it a heck of a lot. But if there's a lot of buyers and sellers, if there's a lot of people active in that market, 100,000 shares might go very easily. So, the more volume, the better. And it's volume that's been created by these high-frequency traders. So, I think the research shows that the costs of trading, spreads and market impact have come down a great deal, and it's all due to high-frequency traders. So, is it socially useful? Well, if you think liquid, highly liquid markets are socially useful, then I think so. Will there ever be glitches? There could be. There was a glitch that I think they called it the flash crash of a few months ago, and for 8 minutes or so, the market took a dive and then it came right back. In 1987, when the stock market crashed, it went down 25% in half a day, and it didn't recover for 6 months. Because there was nobody there on the other side. This flash crash recovered because someone had made a mistake, I presume. Some kind of order went in that was much too big. There was nervousness in the market. It was already down 3%, and it took a dive. And everyone stood back and said, "Oh my god, what's going on?" But then the algorithms kicked in and all this trading just came back and this thing disappeared in 10 minutes. So, it was a little destabilizing, but it was a whole lot different from the crash of 1987. So, a long answer to a question. Yes, I think high-speed trading is socially useful and I think that the people who argue against it are wrong.

Oh, how did they influence the model? Okay, how did my experience in fundamental trading affect the models that gradually got built? I think that they taught us something. The early models that we built were pretty crude and I think were inspired by just sort of looking at what we'd seen. Being in the markets is a good way to observe them. You really look at them when you have something riding on them. So, I would say it was influential. I think it's a good thing for anybody to do even if he's a quant to get some experience in just trading something. Did that answer your question? Okay, one more.

Yeah, I understand. Well, the question is, the economy not only in the United States, but in the world looks kind of unbalanced, unsettled. There's big debts being built up by countries and we have our obviously a recession in the United States and we're busy bailing out banks and where's it all going? Going to hell in a hand basket or what? And what should we be looking at? Well, I think all the things you mentioned are worth looking at. The national debt as a fraction of GNP was considerably higher even than it is today right after the Second World War. And we never paid off that debt. What we did was grow the GDP. So, the denominator got bigger and bigger and after a while we were back in the box. So, there's nothing better under most circumstances in my opinion than some good growth. And I don't think we're taking steps in the United States to promote that good growth because I think we should be maybe even printing more money and building infrastructure and doing some things that the country is lagging in which would get some more people back to work and would put us in a better position to compete even if we do have some inflation. It's not the worst thing in the world, inflation. So, I don't think there's any... You know, what happened in this collapse is the United States got a lot poorer. And it got a lot poorer because the value of everybody's house went down. And people's net worth was associated to their home. And they had been borrowing against the rising prices in their home, home equity loans. That had fueled a lot of growth. And that came to an end. So, we had two bad things happen. People's balance sheets were decimated. And they not only couldn't borrow anymore, but they had to be paying back debt. And a balance sheet disaster is not something that's going to get fixed very fast. It's going to take a number of years, I think, to rebuild from this base. And is the government doing the right thing or the wrong thing? I'm not a big fan of this government. I'm not a big fan of the tax bill that just passed. Except that it was... I have a... Is there a clapper up there? No, there's one clapper. I mean, it made obviously no sense to continue the tax cut for rich guys like me. That made no sense whatsoever. And for some of you out here probably it didn't make much sense. But if that was the price to pay as it apparently was because we have a rather timid president, that was the price to pay for extending the unemployment and making some other things, and keeping the tax cuts for less affluent people, it wasn't the worst thing in the world. So, it looks like maybe it'll produce a little extra growth. But that's what we need. We do need growth. And my own view is I'd rather have it at the risk of inflation than have no growth and say we held the dollar value of our dollar. Who cares if everyone's out of work? You got 20% unemployment. A wonderful dollar is not going to be so helpful. Okay, any... Way over there. Look at that guy. He's hiding.

You're talking about the long-term economic modeling that they... Oh, that was wrong. The quant models had nothing whatever to do with the financial meltdown as far as I can see. What had to do with the financial meltdown was based on mortgages being created that were no damn good. And they were created because there was a market for them. So, these subprime mortgages, you lend money to people that you would never dream of lending money under these circumstances. On the other hand, if your uncle said, "Don't worry. You lend it and I'll buy the paper." You might say, "Well, okay, uncle. If you say you'll buy the paper, I'll lend the money." You lend the money, buy the paper, sell the paper to the uncle. This paper got securitized and at the end of the day stamped AAA. Now, who the hell did that? The rating agencies stamped this paper triple A and they stamped it triple A either because they were idiots, which I think is largely the case, but also because their fees were paid by the issuers of this rotten paper who wouldn't have issued it in the first place if they didn't expect to get triple A and then there's whole chain. I mean, in the old days you would go to the bank, you want to buy a house, the bank actually was the one who lent you the money. The bank was going to hold the mortgage, the bank was going to service the mortgage, and the bank wanted you to pay. So, the banks were, but now a bank loans you the money for a microsecond and they sell it to the next guy. So, it had very little to do with quant models. It's true that quants were designing these packages of mortgages. But the underlying statistics, what's the probability that this mortgage is going to get paid or what's the probability that a guy has had seven jobs in 8 years in four different cities and who has simply stated that he earns whatever, what's the probability that he's really going to pay off his mortgage? Anyone with common sense would think it's pretty darn close to zero. But that fact was not taken into account in the underlying statistics. So, that's a long answer. We'll do one more, I think.

I mentioned Warren Ambrose in my beginning of my talk, this mathematician who inspired me. I'm actually going to answer your question. I went to him one day early in my career and I said, "Professor Ambrose, is it best to learn a lot about one area of mathematics or learn a little bit about a lot of areas in mathematics?" And he looked at me and he said, "One can make the cliché either way." And that was the end of the discussion. So, there isn't any right answer to the question you asked. All those things are good. It's good to use economic models. It's good to use price histories. It's good to make models using all these things and there isn't any right or wrong to that answer. So, let's thank Jim for that.