Jim Simons A rare interview with the mathematician who cracked Wall Street

you was something of a mathematical phenom you had already taught it Harvard and MIT at a young age and then the NSA came calling what was that about well the NSA didn't exactly that's the National Security Agency it didn't exactly come calling they had an operation in Princeton where they hired mathematicians to attack secret codes and stuff like that and I knew that existed and it had a very good policy because you could do half your time at your own mathematics and at least half your time working on their stuff and they paid a lot so that was an irresistible pull so I went there so you were a code cracker I will till you got fired well I did get fired yes how come well how come I got fired because well the Vietnam War was on and the the boss of bosses in my organization was a big fan of the war and wrote a New York Times article magazine section cover story about Howard and a win in Vietnam and I didn't like that war thought it was stupid and I wrote a letter to The Times which they published saying not everyone who works for it was Maxwell Taylor if anyone remembers that name works for general Taylor agrees with his views and I gave my own views okay so that I can see that would which would different from general Taylor's but on the other hand nobody said anything but then I was 29 years old at this time and some kid came around to said he was a stringer from Newsweek magazine and he wanted to interview me and asked my what I was doing about my views and I told him that I'm doing mostly mathematics now and when the war is over then I'll do mostly their stuff then I did the only intelligent thing I'd done that day I told my local boss that I gave that interview he said what did you say I told him what I said and then he said I got a call Taylor he called Taylor that took ten minutes I was fired five minutes after that okay so but it wasn't bad it wasn't bad because he went you went on to Stony Brook and I went on stepped-up your yellow mathematical career and you started working with with this man here who is this Oh churn yeah churn was one of the great mathematicians of the century I had known him when I was a graduate student actually and Berkeley and I had some ideas and I brought them to him and he liked them and together we did this work which you can easily see up there there it is and led to you publishing a famous paper together can you explain at all what that work was no no oh really what can you explain how you explain it to somebody but uh what how about explaining this I said I don't know but not many not many who I think you told me it had something to do with spheres so let's let's start let's start yes well it did but but I I'll say about that work it did have something to do with that but before we get to that that work was was good mathematics I was very happy with it so was churn it even started a little subfield that's now flourishing but more interestingly it happened to apply to physics something we knew nothing about at least I knew nothing about physics and I don't think turn to a heck of a lot and about 10 years after the paper came out guy named ed Witten in in Princeton started applying it to string theory and people in Russia started applying it to what's called condensed matter and today that those things in there called chern-simons invariants spread through a lot of physics and was amazing we didn't know any physics it never occurred to me that it would be applied to physics but that's the thing about mathematics you never know where it's going to go but this is so incredible so we've been talking about how evolution shapes human minds that may or may not perceive the truth somehow you come up with a mathematical theory not knowing any physics discover two decades later that it's being applied to profoundly describe the actual physical world yeah how can that happen God knows it better but yeah but there's a there was a famous physicist named Wigner and he wrote an essay on the unreasonable effectiveness of mathematics so somehow this mathematics which is rooted in the real world some sense you know we learned to count and measure that's kind of everyone would do that and then it flourishes on its own but so often it comes back to save the day general relativity is another example Minkowski had this geometry and Einstein realized hey it's the very thing in which I can cast general relativity so you never know and so it is a mystery it is a mystery so here's here's a mathematical piece of ingenuity here don't tell us about this yeah yeah well this well that's a ball it's a it's a sphere and it has a lattice around it you know those squares sort of things and what I'm going to show here was originally observed by Euler the great mathematician in the 1700s and it gradually grew to be a very important field in mathematics algebraic topology geometry and that paper up there was had its roots in this so okay so is this thing it has 8 vertices and 12 edges and six faces and if you look at the difference vertices minus edges plus faces you get - ok well - that's a good number oh here's a different way of doing it these are triangles covering Elvis has 12 vertices and 30 edges and 20 faces 20 tiles and vertices minus edges plus faces still equals 2 and in fact you could do this any which way cover this thing with all kinds of yawns and triangles or mix them up you take vertices minus edges plus faces you'll get two now here's a different shape this is a torus of the surface of a doughnut 16 vertices covered by these rectangles thirty-two age of 60 and faces hate it this comes out zero vertices minus H it'll always come out to zero every time you cover a torus with squares or triangles or anything like that you're going to get a zero when you take that thing so this is called the Euler characteristic and it's what's called a topological invariant it's pretty amazing no matter how you do it you're always going to get the same answer so that was the first sort of thrust from the mid-1700s into a subject which is now called algebraic topology and your own work took an idea like this and moved it into high dimensional theory well dimensional objects and found new invariances yes well they were already higher dimensional invariants pontryagin classes actually there were Chern classes there were a bunch of these types of invariants but I I was struggling to work on one of them and model it sort of combinatorially instead of the way it was typically done and that led to this work and we uncovered some some new things but if it wasn't for mr. Euler who wrote almost 70 volumes of mathematics had 13 children whom he had apparently would dandelo on his knee while he was writing if it wasn't for mr. Euler well there wouldn't perhaps be these invariants okay so that's the least giving us a flavor of that amazing mind in there let's talk about Renaissance because you took that amazing mind and you know having been a code cracker dnsa you start to become a kokrak or the financial industry I think you probably didn't buy efficient market theory and somehow you found a way of creating these astonishing returns over two decades I think the Weitzman explained to me what's remarkable about what you did was that it wasn't just the size of the returns it was that you took them with surprisingly low volatility and risk compared with other other edge ones so how on earth did you do this Jim well I did it by assembling a wonderful group of people right what I started doing trading I got a little tired of mathematics I was in my late 30s I had a little money I started trading and it went very well I made quite a lot of money how it with pure luck I mean I think it was pure silly wasn't mathematical modeling but in looking at the data after a while I realized hey this looks like the substructure here and I hired a few mathematicians and we started trying to make some models just the kind of thing we did back at eye-dea you design an algorithm you test it out on a computer does it work doesn't it work and so on so let's can we take a look at this because I mean here's a typical like graph of some commodity or whatever I mean I look at that and I say that's just a random up-and-down walk maybe a slight upward trend over that whole period of time how on earth could you trade look at that and see something well just random it turns out in the old days and this is kind of a graph from the old days commodities or currencies had a tendency to trend not necessarily that very light trend you see here but but trending in in periods and if you decided okay I'm going to predict today by the average move in the past 20 days hey there's plenty days maybe that would be a good prediction and I'd make some money and in fact is years ago such a system would work not beautifully but it would work so you'd make money you'd lose money and make money but this is a year's worth of days and you you know you make a little money during that period but so you would think it's a very best digital system so you would test a bunch of difference or lengths of trends and time and see whether for example a 10-day trend or a 15-day trend was predictive of what happened sure sure you and uh you you know try all those things to see what worked best but the trend following would have been great in the 60s and it was sort of okay in the 70s by the 80s it wasn't such it wasn't it didn't because at everyone could a lot of people say so what happens though how did you stay ahead of the pack then we stayed ahead of the pack by finding by finding other other approaches and shorter term approaches to some extent but the the real thing was to gather a tremendous amount of data and and we had to get it by hand in the early days and we went down to the Federal Reserve and copied interest rate histories and stuff like that because it didn't exist on computers we got a lot of data and very smart people and that was the that was the key I didn't really know how to hire people to do fundamental trading I had hired a few some made money some didn't make money I couldn't make a business out of that but I didn't know how to hire scientists because I have some taste in that department and so that's what we did and gradually these models got better and better and better and better I mean I think your credit should be doing second remarkable at Renaissance which is building this culture on this group of people who weren't just hired guns who could be lured away by money their motivation was was doing exciting mathematics and science well I hope that might be true but some of it was money I made a lot and I can't say that that no one came because of the money I think a lot of them came because of the money but they also came because it would be fun what role did machine learning play and all this well in a certain sense what we did was machine learning you you you look at a lot of data and you try to simulate different predictive schemes until you get better and better at it it doesn't doesn't necessarily feed back on itself the way we did things but it worked and so these different predictive skins can be really quite quite wild quite unexpected I mean you look you look at everything I look at weather length of dresses yes yes cool opinion waist up dresses we didn't try but uh what sort of things well everything I mean everything is grist for the mill except hem winks Anthony I have to say whether annual reports monthly quarterly report the historic data itself volumes you name it whatever there is we take in terabytes of data a day and store it away and massage it and get it ready for analysis and you're looking for anomalies you're looking for like you said you know the efficient market hypothesis only one anomaly might be just a random thing so is the secret here to just look at multiple strange anomalies and see when they're lying well any one anomaly might be a random thing however if you have enough data you can tell that it's not so you can see an anomaly that's persisted for sufficiently long time so that the probability of it being random is is not high but these things fade after a while anomalies can get washed out so you have to keep on top of the business a lot of people look at the hedge fund industry now and as sort of shocked by it by how much wealth is created there and how much talent is going into it do you have any worries about that industry and perhaps the financial industry in general kind of being on a runaway train that's that I don't know helping increase inequality or or how would you how would you champion what's what's well actually I think in the last three or four years hedge funds have not done especially well we've done tandy but the hedge fund industry as a whole has not done so on we the stock market has been on a roll going up as everybody knows and price earnings ratios have grown so an awful lot of the wealth that's been created in the last let's say five or six years has not been created by hedge fund so it's just another you know people would ask me what's a hedge fund and I say one in twenty I mean if they which means now it's two or twenty whatever you know it's two at fixed fee and twenty percent of profits hedge funds are all different kind of creature rumor has it you charge slightly higher fees and we hire trudge we had to charge the highest fees in the world at one time five and forty four that's a five and forty four five percent fixed fee forty five percent flat forty four percent of upside is still made your investors spectacular many good returns yes people got very mad at my investors for having a charge such high fees okay you can withdraw but how can I get more was watching how can I get more but at a certain point as I think I told you we bought out all the investors because there's a capacity as a capacity to this but should we worry about the hedge fund industry attracting too much of the world's great mathematical and other talent to work on that as opposed to do any other problem so it's not just mathematical we hired astronomers and physicists and things like that i I don't think we should worry about it too much it's still it's still a pretty small industry and in fact bringing in science into the investing world has has improved that world it's it's reduced volatility it's increased liquidity spreads are narrower because people are trading that kind of stuff so I don't I'm not too worried about Einstein going off and starting a hedge fund not you're the face in your life now where you're actually investing though at the other end of the supply chain yeah in actually boosting mathematics across America his your wife Marilyn I know you're you're working on philanthropic issues the other yeah tell me about that well yeah we are Marilyn started there she is up there beautiful wife she started the foundation about twenty years ago I think 94 I claim it was 93 she says was 94 but it was a one of those two years we started the foundation and just as a convenient way to give charity and she kept the books and so on and we did not have a vision at that time but gradually a vision emerged which was to focus on math and science to focus on basic research and that's that's what we've done and six years ago or so I left Renaissance and went to work at the foundation so that's what we do it's a mouth for America here is basically investing in math teachers around the country giving them some extra income and giving them support and coaching and and really trying to make yeah make that more effective and make that a calling to which teachers can aspire yeah yeah it's a instead of beating up the bad teachers which has created morale problems all through the educational community in particular math and science we focus on celebrating the good ones and and giving them status yeah we give them extra money $15,000 a year we have 800 math and science teachers in New York City in the public schools today as part of a core they're there there's a great morale among them they're staying in the field next year it'll be a thousand and that'll be 10% of the math and science teachers in New York Public Schools and that's Jim here here's another project you've supported philanthropically research into origins of life I guess what are we looking at here well I'll save that for a second and then I'll tell you what you're looking at but so origins of life is a fascinating question how do how do we get here what's what's a well there's two questions so one is what is the route from geology to biology how did how did we get here any other questions what did we start with what material if any that we have to work with on this route those are two very very interesting questions the first question is tortuous path from from geology up to RNA or something like that how do i all work in the other what do we have to work with well more than we think so what's pictured there is a star in formation now every year in our Milky Way which has a hundred billion stars about two new stars are created don't ask me how but they're created and it takes them about a million years to settle out so in steady state there's about two million stars in formation at any time that wood is somewhere along this settling down period and as always crap sort of circling around it dust and stuff and it will form probably a solar system or whatever it forms but here's the thing in this dust that surrounds a forming star have been found now significant organic molecules molecules not just like methane but formaldehyde and cyanide things that are the building blocks the seeds if you will of life so that may be typical and it may be typical that planets around the universe start off with some of these basic building blocks now does that mean all others going to be life all around maybe but it's a question of how tortuous this path is from those frail beginnings those seeds all the way to life and most of those seeds will fall on fallow planets but for you personally finding an answer to this question of where we came from how did this thing happen that is something you would love to see I love thinking well after seeing life like to know you know if that path is tortuous enough and loop and so uhm probable that no matter what you start with we're we could be a singularity but on the other hand given all this organic dust that's floating around we could have what's a one's friends out there be great to know Jim a couple couple years ago I got a chance to speak with Elon Musk and asked him the secret of his successfully of and he said taking physics seriously what was it listening to you what what I hear you saying is you know taking math seriously that that that has infused your whole life it's it's made you an absolute fortune and now it's allowing you to invest in the futures of thousands and thousands of kids across America and elsewhere could it be that science actually works and math actually works Vincent well Matt certainly works Matt certainly works and but this has been fun working with Marilyn and giving it away has been very enjoyable I just find it it's an inspirational thought to me that by taking knowledge seriously so much more can come from it so thank you for your amazing life and for coming here to Ted Earley thank you Jim Simon this drive is really

Channel: Maikel Akkermans

Views: 69,900

Keywords: Mathematician (Profession), James Harris Simons (Organization Leader), Mathematics (Field Of Study), Interview, Wall Street, cracked, who cracked Wall Street, mathematician who cracked Wall Street, Simons, Jim Simons, tedtalk

Id: siGBO4Nqqws

Length: 23min 11sec (1391 seconds)

Published: Thu Sep 03 2015