# The mathematician who cracked Wall Street | Jim Simons

Chris Anderson: You were something

of a mathematical phenom. You had already taught at Harvard

and MIT at a young age. And then the NSA came calling. What was that about? Jim Simons: Well the NSA —

that’s the National Security Agency — they didn’t exactly come calling. They had an operation at Princeton,

where they hired mathematicians to attack secret codes

and stuff like that. And I knew that existed. And they 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. CA: You were a code-cracker. JS: I was. CA: Until you got fired. JS: Well, I did get fired. Yes. CA: How come? JS: Well, how come? I got fired because,

well, the Vietnam War was on, and the boss of bosses in my organization

was a big fan of the war and wrote a New York Times article,

a magazine section cover story, about how we would win in Vietnam. And I didn’t like that war,

I thought it was stupid. And I wrote a letter to the Times,

which they published, saying not everyone

who works for Maxwell Taylor, if anyone remembers that name,

agrees with his views. And I gave my own views … CA: Oh, OK. I can see that would — JS: … which were different

from General Taylor’s. But in the end, nobody said anything. But then, I was 29 years old at this time,

and some kid came around and said he was a stringer

from Newsweek magazine and he wanted to interview me

and ask what I was doing about my views. And I told him, “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. And he said, “What’d you say?” And I told him what I said. And then he said,

“I’ve got to call Taylor.” He called Taylor; that took 10 minutes. I was fired five minutes after that. CA: OK. JS: But it wasn’t bad. CA: It wasn’t bad,

because you went on to Stony Brook and stepped up your mathematical career. You started working with this man here. Who is this? JS: Oh, [Shiing-Shen] Chern. Chern was one of the great

mathematicians of the century. I had known him when

I was a graduate student at Berkeley. And I had some ideas, and I brought them to him

and he liked them. Together, we did this work

which you can easily see up there. There it is. CA: It led to you publishing

a famous paper together. Can you explain at all what that work was? JS: No. (Laughter) JS: I mean, I could

explain it to somebody. (Laughter) CA: How about explaining this? JS: But not many. Not many people. CA: I think you told me

it had something to do with spheres, so let’s start here. JS: Well, it did,

but I’ll say about that work — it did have something to do with that,

but before we get to that — that work was good mathematics. I was very happy with it; so was Chern. It even started a little sub-field

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 Chern

knew a heck of a lot. And about 10 years

after the paper came out, a guy named Ed Witten in Princeton

started applying it to string theory and people in Russia started applying it

to what’s called “condensed matter.” Today, those things in there

called Chern-Simons invariants have spread through a lot of physics. And it 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. CA: 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. How can that happen? JS: God knows. (Laughter) But there’s a famous physicist

named [Eugene] Wigner, and he wrote an essay on the unreasonable

effectiveness of mathematics. Somehow, this mathematics,

which is rooted in the real world in some sense — we learn to count,

measure, everyone would do that — and then it flourishes on its own. But so often it comes

back to save the day. General relativity is an example. [Hermann] Minkowski had this geometry,

and Einstein realized, “Hey! It’s the very thing

in which I can cast general relativity.” So, you never know. It is a mystery. It is a mystery. CA: So, here’s a mathematical

piece of ingenuity. Tell us about this. JS: Well, that’s a ball — it’s a sphere,

and it has a lattice around it — you know, those squares. What I’m going to show here was

originally observed by [Leonhard] Euler, the great mathematician, in the 1700s. And it gradually grew to be

a very important field in mathematics: algebraic topology, geometry. That paper up there had its roots in this. So, here’s this thing: it has eight vertices,

12 edges, six faces. And if you look at the difference —

vertices minus edges plus faces — you get two. OK, well, two. That’s a good number. Here’s a different way of doing it —

these are triangles covering — this has 12 vertices and 30 edges and 20 faces, 20 tiles. And vertices minus edges

plus faces still equals two. And in fact, you could do this

any which way — cover this thing with all kinds

of polygons and triangles and mix them up. And you take vertices minus edges

plus faces — you’ll get two. Here’s a different shape. This is a torus, or the surface

of a doughnut: 16 vertices covered by these rectangles,

32 edges, 16 faces. Vertices minus edges comes out to be zero. 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 zero. 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 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. CA: And your own work

took an idea like this and moved it into higher-dimensional theory, higher-dimensional objects,

and found new invariances? JS: Yes. Well, there were already

higher-dimensional invariants: Pontryagin classes —

actually, there were Chern classes. There were a bunch

of these types of invariants. 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 new things. But if it wasn’t for Mr. Euler — who wrote almost 70 volumes of mathematics and had 13 children, who he apparently would dandle on his knee

while he was writing — if it wasn’t for Mr. Euler, there wouldn’t

perhaps be these invariants. CA: OK, so that’s at least given us

a flavor of that amazing mind in there. Let’s talk about Renaissance. Because you took that amazing mind

and having been a code-cracker at the NSA, you started to become a code-cracker

in the financial industry. I think you probably didn’t buy

efficient market theory. Somehow you found a way of creating

astonishing returns over two decades. The way it’s been explained to me, what’s remarkable about what you did

wasn’t just the size of the returns, it’s that you took them

with surprisingly low volatility and risk, compared with other hedge funds. So how on earth did you do this, Jim? JS: I did it by assembling

a wonderful group of people. When I started doing trading, I had

gotten a little tired of mathematics. I was in my late 30s,

I had a little money. I started trading and it went very well. I made quite a lot of money

with pure luck. I mean, I think it was pure luck. It certainly wasn’t mathematical modeling. But in looking at the data,

after a while I realized: it looks like there’s some structure here. And I hired a few mathematicians,

and we started making some models — just the kind of thing we did back

at IDA [Institute for Defense Analyses]. You design an algorithm,

you test it out on a computer. Does it work? Doesn’t it work? And so on. CA: Can we take a look at this? Because here’s a typical graph

of some commodity. 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

looking at that, and see something that wasn’t just random? JS: In the old days — this is

kind of a graph from the old days, commodities or currencies

had a tendency to trend. Not necessarily the very light trend

you see here, but trending in periods. And if you decided, OK,

I’m going to predict today, by the average move in the past 20 days — maybe that would be a good prediction,

and I’d make some money. And in fact, years ago,

such a system would work — not beautifully, but it would work. You’d make money, you’d lose

money, you’d make money. But this is a year’s worth of days, and you’d make a little money

during that period. It’s a very vestigial system. CA: So you would test

a bunch of lengths of trends in time and see whether, for example, a 10-day trend or a 15-day trend

was predictive of what happened next. JS: Sure, you would try all those things

and see what worked best. Trend-following would

have been great in the ’60s, and it was sort of OK in the ’70s. By the ’80s, it wasn’t. CA: Because everyone could see that. So, how did you stay ahead of the pack? JS: We stayed ahead of the pack

by finding other approaches — shorter-term approaches to some extent. The real thing was to gather

a tremendous amount of data — and we had to get it by hand

in the early days. We went down to the Federal Reserve

and copied interest rate histories and stuff like that,

because it didn’t exist on computers. We got a lot of data. And very smart people — that was the key. I didn’t really know how to hire

people to do fundamental trading. I had hired a few — some made money,

some didn’t make money. I couldn’t make a business out of that. But I did know how to hire scientists, because I have some taste

in that department. So, that’s what we did. And gradually these models

got better and better, and better and better. CA: You’re credited with doing

something remarkable at Renaissance, which is building this culture,

this group of people, who weren’t just hired guns

who could be lured away by money. Their motivation was doing

exciting mathematics and science. JS: Well, I’d hoped that might be true. But some of it was money. CA: They made a lot of money. JS: I can’t say 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. CA: What role did machine learning

play in all this? JS: In a certain sense,

what we did was machine learning. 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 necessarily feed back on itself

the way we did things. But it worked. CA: So these different predictive schemes

can be really quite wild and unexpected. I mean, you looked at everything, right? You looked at the weather,

length of dresses, political opinion. JS: Yes, length of dresses we didn’t try. CA: What sort of things? JS: Well, everything. Everything is grist for the mill —

except hem lengths. Weather, annual reports, quarterly reports, historic data itself,

volumes, you name it. Whatever there is. We take in terabytes of data a day. And store it away and massage it

and get it ready for analysis. You’re looking for anomalies. You’re looking for — like you said, the efficient market

hypothesis is not correct. CA: But any one anomaly

might be just a random thing. So, is the secret here to just look

at multiple strange anomalies, and see when they align? JS: Any one anomaly

might be a random thing; however, if you have enough data

you can tell that it’s not. You can see an anomaly that’s persistent

for a sufficiently long time — the probability of it being

random is not high. But these things fade after a while;

anomalies can get washed out. So you have to keep on top

of the business. CA: A lot of people look

at the hedge fund industry now and are 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 — I don’t know —

helping increase inequality? How would you champion what’s happening

in the hedge fund industry? JS: I think in the last

three or four years, hedge funds have not done especially well. We’ve done dandy, but the hedge fund industry as a whole

has not done so wonderfully. 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 funds. People would ask me,

“What’s a hedge fund?” And I’d say, “One and 20.” Which means — now it’s two and 20 — it’s two percent fixed fee

and 20 percent of profits. Hedge funds are all

different kinds of creatures. CA: Rumor has it you charge

slightly higher fees than that. JS: We charged the highest fees

in the world at one time. Five and 44, that’s what we charge. CA: Five and 44. So five percent flat,

44 percent of upside. You still made your investors

spectacular amounts of money. JS: We made good returns, yes. People got very mad:

“How can you charge such high fees?” I said, “OK, you can withdraw.” But “How can I get more?”

was what people were — (Laughter) But at a certain point,

as I think I told you, we bought out all the investors

because there’s a capacity to the fund. CA: 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 the many other problems in the world? JS: Well, it’s not just mathematical. We hire astronomers and physicists

and things like that. I don’t think we should worry

about it too much. It’s still a pretty small industry. And in fact, bringing science

into the investing world has improved that world. It’s reduced volatility.

It’s increased liquidity. Spreads are narrower because

people are trading that kind of stuff. So I’m not too worried about Einstein

going off and starting a hedge fund. CA: You’re at a phase in your life now

where you’re actually investing, though, at the other end of the supply chain — you’re actually boosting

mathematics across America. This is your wife, Marilyn. You’re working on

philanthropic issues together. Tell me about that. JS: Well, Marilyn started — there she is up there,

my beautiful wife — she started the foundation

about 20 years ago. I think ’94. I claim it was ’93, she says it was ’94, but it was one of those two years. (Laughter) We started the foundation,

just as a convenient way to give charity. She kept the books, and so on. 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 what we’ve done. Six years ago or so, I left Renaissance

and went to work at the foundation. So that’s what we do. CA: And so Math for America

is basically investing in math teachers around the country, giving them some extra income,

giving them support and coaching. And really trying

to make that more effective and make that a calling

to which teachers can aspire. JS: Yeah — instead of beating up

the bad teachers, which has created morale problems

all through the educational community, in particular in math and science, we focus on celebrating the good ones

and giving them status. Yeah, we give them extra money,

15,000 dollars a year. We have 800 math and science teachers

in New York City in public schools today, as part of a core. There’s a great morale among them. They’re staying in the field. Next year, it’ll be 1,000

and that’ll be 10 percent of the math and science teachers

in New York [City] public schools. (Applause) CA: Jim, here’s another project

that you’ve supported philanthropically: Research into origins of life, I guess. What are we looking at here? JS: Well, I’ll save that for a second. And then I’ll tell you

what you’re looking at. Origins of life is a fascinating question. How did we get here? Well, there are two questions: One is, what is the route

from geology to biology — how did we get here? And the other question is,

what did we start with? What material, if any,

did we have to work with on this route? Those are two very,

very interesting questions. The first question is a tortuous path

from geology up to RNA or something like that —

how did that all work? And 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 100 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 are about two million stars

in formation at any time. That one is somewhere

along this settling-down period. And there’s all this crap

sort of circling around it, dust and stuff. And it’ll 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

there’s 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. CA: So for you, personally, finding an answer to this question

of where we came from, of how did this thing happen,

that is something you would love to see. JS: Would love to see. And like to know — if that path is tortuous enough,

and so improbable, that no matter what you start with,

we could be a singularity. But on the other hand, given all this organic dust

that’s floating around, we could have lots of friends out there. It’d be great to know. CA: Jim, a couple of years ago,

I got the chance to speak with Elon Musk, and I asked him the secret of his success, and he said taking

physics seriously was it. Listening to you, what I hear you saying

is taking math seriously, that has infused your whole life. 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? That math actually works? JS: Well, math certainly works.

Math certainly works. But this has been fun. Working with Marilyn and giving it away

has been very enjoyable. CA: 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. Thank you. Jim Simons! (Applause)

Folks, at 4:26 interviewer credits his religion of how Evolution amazingly shapes the mind …… And seconds later the true credit is unwittingly given when the reply is "God knows".

The interviewer looks like Elon musks brother

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Mathematicians can make numbers do whatever they want them to. I am not one of those people that thinks numbers never lie. Math only works if you buy into said laws of mathematics.

Thank God we do not see this interviewer much within TED talk videos. He is the worst

Original data scientist.

the interviewer is constipated

Wonder what Jim Simons thinks of bitcoin and blockchain ?

that's funny, cos wall st. games aren't based in laws of physics or mathematical realities. they're made up as much as santa claus.

You are all talking about how the interviewer should have let him speak more and not cut him off. But the guy he is interviewing didn’t actually want to directly answer a lot of his questions and gave broad or evasive answers to a lot of questions either bcse he felt the generals public would not understand these complicated mathematical concepts, bcse he didn’t want to give out his trade secrets, or simply bcse he isn’t comfortable talking in front of such a big crowd.

But the truth is for whatever reason he didn’t really cooperate with what the interviewer was asking and the interviewer truly asked very valid questions that were very well organized in sequence. Very honestly both men are great men in their own fields and the interviewer is one of the best in the whole world and he does an amazing job taking to these remarkable individuals.

But you have to understand it is his job to get juice out of him and if he’s not really biting he has to bounce of and keep the flow going to make for a very interesting conversation worth being televised otherwise the whole thing would fall flat. We always tend to critique others but some situations simply are as they are. The interviewer did really good considering all the circumstances and is perhaps one of the best interviewers in the entire world otherwise he wouldn’t be assigned by Ted to interview all these remarkable people.

Objectively speaking I don’t think any average person could come remotely close to his interviewing or public speaking skills.

The truth is pulling off a televised or an important you tube broadcasted interview in this case comes with natural constraints and sometimes things like this happen either bcse the two people talking naturally don’t have the very best organic chemistry in the world or simply bcse the person interviewed is not comfortable or bcse the audience is not responding properly.

All in all, considering all the circumstances and factors both the interviewer did very well, he tried to make the guest comfortable without bothering him and to make for a very good interview worth watching regardless of the situation he was dealt.

We shouldn’t judge and critique others so fast bcse these things are not as easy as they look. And in the entire world very very few people can pull off such interviews on this level.

The interviewer actually showed true mastery and know how in handling the situation at hand. I really doubt anybody in the comment section could have done better than him if they were actually directly engaging with an old mathematician that is not that comfortable being on stage or answering a lot of the questions.

Both men are obviously great people but some things simply are as they are.

Hes gone from cracking codes to cracking the code to cracking THE code

This host is such an idiot

Chris shoyuld get fired this idiot moron

"I'm not too worried about Einstein going off and opening a hedge fund " 😂 😂

It would be interesting to know even some of this guys Algos

The wise Buddha had advised that ANYTHING that is Man-MADE!!! CAN!!! be Man-MANIPULATED!

Amazing. So intelligent but does not even know the place he lives is NOT a planet in a heliocentric solar system, that does not exist in REALITY! SMH.

17:40 ☼ convenient way to launder money & shield it from taxes. Starts a charity w/o an aim. Some of us here do work in finance you know.

☼ $20 Billion! oh. well. That's a lot.

What a desperate bunch here…all looking to get out of the rat race..but let me tell you, with or without money a rat is always a rat.

Sure he did! LOL

Interviewer is unbearable

I remember the first time I encountered the Chern-Simons theory, I was struggling to learn quantum field theory. from what I recall, for string theory, if the Euler characteristic is described by wrapping polygons around the sphere, this describes the modes of vibration for an open string, i.e., V-E+F = 2, so 2 is the number of poles (singularities) connecting the ends of the string. For the torus, 0 poles, so this describes a the modes of vibration of a closed string. I think C-S theory comes in when you're looking for discrete combinations (combinatorial stuff, like Jim mentions) of modes. So instead of looking at all the modes, i.e. a continuum, you get a quantization of the string modes. Speaking strictly form memory and the greatest humility. He is a very inspiring type of guy, it folks like him that encourage the rest of us to learn things are far outside of average.

i don't like this interviewer one bit. he's got this gay sassiness about him like he's waiting for any chance to make sarcastic remarks about you or something…lol

No such thing as mathematically predicting a random walk with trader manipulation.

How do you get topology like that for. 2 sphere. 0 torroid for -1 what. For one what.

JESUS CHRIST LET THE MAN TALK!!! This interviewer is the worst

5:30 just noticed his ankles. Now I can unsee them

Instead, I wish TED interviewed the mathematicians who work for Jim and developed the logic used today. If you listen carefully in this and others interviews, you'll find he is a smart mathematician but the "cracking Wall Street" was done by others. Just talking to the boss is one way to get a watered down version. Also keep in mind, the Medallion may no longer be a true Quant fund — based on comments made, it might well just be using HFT/arbitrage. I would be very surprised if they came up with a secret formula in the 80s or 90s and it has worked continuously since. Everyone, including me, likes to believe in the holy grail even when there is no visible evidence that it exists.