Speaker 1: Pushkin too quick. No, it's perfect Pushkin stuff.
Speaker 2: You got it, Robert Smith. Yeah, tell me about the
Speaker 2: Cold War.
Speaker 1: The Cold War between the United States and the Soviet
Speaker 1: Union thrust us into this technological world that we live
Speaker 1: in today. We know about how it brought us the Internet, satellites,
Speaker 1: silicon chips, but the Cold War also brought us the
Speaker 1: modern financial market system. Stocks, bonds, all traded with high
Speaker 1: frequency computers, right algorithms. This quant world of Wall Street
Speaker 1: came from the Cold War, and the one man most
Speaker 1: responsible for this financial breakthrough was a genius mathematician who
Speaker 1: grew disillusion with the US government and decided to create
Speaker 1: the most efficient money making machine ever built great setup
Speaker 1: for his own profit. By the way, so it's the
Speaker 1: early nineteen sixties and Jim Simons is studying math. Like
Speaker 1: a lot of people in the United States. There was
Speaker 1: this real push after Sputnik for people to go into
Speaker 1: math and science. Jim Simons graduates from MIT from UC Berkeley,
Speaker 1: and he decides that he doesn't really want to do
Speaker 1: mathematics of the physical world. He wants to do the
Speaker 1: highest form theoretical mathematics. His thesis topic on the transitivity
Speaker 1: of holonomi systems or hulminy.
Speaker 3: Do you know what that word means?
Speaker 1: I do not, eh no, But why do you read
Speaker 1: the first Why do you read the first two sentences
Speaker 1: of the first theorem so we can get a feel
Speaker 1: for it.
Speaker 3: Yeah, put it here, you've sent it to me. It
Speaker 3: says theorem one. Let M be a c to the infinity.
Speaker 3: I guess manifold with an affine connection. Let R denote
Speaker 3: the curvative tensor of the connection.
Speaker 1: I don't know what it means.
Speaker 3: I know you could even have us something to the infinity.
Speaker 1: But I'll tell you who loved this, the US government
Speaker 1: and all the people who worked for the US government.
Speaker 1: And he was recruited to work at something called the
Speaker 1: Institute for Defense Analyzes in Princeton.
Speaker 3: Why don't they just call it the CIA math.
Speaker 1: Shop front at Tecton? Is it that technically independent? It
Speaker 1: is a think tank that helps the US government during
Speaker 1: the Cold War. They do things with you know, weapons
Speaker 1: targeting and evaluation, but they also do code breaking, and
Speaker 1: that's where they put Jim Simons, the mathematician. There's anything
Speaker 1: wrong with that? So if you think about it, right,
Speaker 1: because we know this is a show about finance eventually,
Speaker 1: but think about what he's doing in order to break
Speaker 1: Soviet codes. Jim Simons is looking looking at a sea
Speaker 1: of random signals, seemingly random data, but he knows that
Speaker 1: there's a communication inside. He knows there's a pattern, there's
Speaker 1: a signal in the noise, and he's using sophisticated mathematics,
Speaker 1: algorithms and early computers, big computers to churn through this
Speaker 1: data and try and figure out what the Soviets are saying.
Speaker 3: What is the data telling us, what is going to
Speaker 3: happen in the world exactly?
Speaker 1: Now, By all accounts, Jim Simons is pretty good at
Speaker 1: this hard to tell. It's all secret, right, And in
Speaker 1: another world he might have been one of those mathematicians
Speaker 1: who stayed on at the NSA, had an illustrious career,
Speaker 1: and we never would have heard of him because it
Speaker 1: all would have been secret. But instead he did something amazing.
Speaker 1: So this is during the Vietnam War, the early days
Speaker 1: of the Vietnam War, right, and the head of the
Speaker 1: think tank the IDA was publicly defending US military action
Speaker 1: in Vietnam, saying we are winning the war. And Jim
Speaker 1: Simons is young mathematician. The Times in his late twenties. Right,
Speaker 1: he thinks it's total crap, and he writes a public
Speaker 1: letter to the New York Times magazine published there that says,
Speaker 1: the war in Vietnam is a waste of time and
Speaker 1: money and human lives. Quote, it would make us stronger
Speaker 1: to construct decent transportation on our East coast than it
Speaker 1: would be to destroy all the bridges in Vietnam. Huh,
Speaker 1: I know true today? Right? Needless to say, he writes this,
Speaker 1: criticizing his boss, he is fired from the thing. Sure,
Speaker 1: reasonably so, one might say, But Jim Simon's already had
Speaker 1: this idea that would change finance. What if you could
Speaker 1: use these same systems, computers, algorithms, code breaking, right to
Speaker 1: look for patterns in stocks and bonds. He would go
Speaker 1: on to create a firm called Renaissance Technologies, with one
Speaker 1: fund in particular, called the Medallion Fund. Over the span
Speaker 1: of thirty years or so, the Medallion Fund would return
Speaker 1: just a staggering amount of money. It's estimated that its
Speaker 1: trading profits were more than one hundred billion dollars. Just
Speaker 1: you know, the stock market averages about eleven percent gain
Speaker 1: a year, right, seven percent after inflation? Maybe?
Speaker 3: Right?
Speaker 1: Jim Simons was producing returns of sixty six percent a.
Speaker 3: Year for thirty years.
Speaker 1: That sounds wrong to me.
Speaker 3: That sounds like somebody did a made a math there, right,
Speaker 3: Like if you think of Bernie made off Right, the
Speaker 3: famous Ponzi schemer, he was returning like what ten to
Speaker 3: twelve percent a year and.
Speaker 1: That was his dream. That was fake, and.
Speaker 3: That was he was cheating to do that, right, sixty
Speaker 3: six percent a year is so for thirty years? Like sure,
Speaker 3: if somebody could do it for a couple of years,
Speaker 3: they get lucky. Like, are you sure that is right?
Speaker 1: This is as far as we can tell an accurate
Speaker 1: number between nineteen eighty eight and twenty twenty one. Can't
Speaker 1: believe it. Double your money in sixteen months and.
Speaker 3: Then double it again and again and again and again
Speaker 3: and again. Infinite money machine keeps doing it at that
Speaker 3: magnitude is extraordinary.
Speaker 1: This is Business History, a show about the history of business.
Speaker 1: I'm Jacob Goldstein, I'm Robert Smith. Today on the show,
Speaker 1: the final part of our investing series, we featured way
Speaker 1: back more than one hundred years ago, the speculator Jesse Livermore,
Speaker 1: the great investor Warren Buffett, love him and now the
Speaker 1: quant Jim Simons, he was part of a revolution in
Speaker 1: finance to put math and numbers before all this intuition
Speaker 1: and business savvy stuff that Wall Street was all about,
Speaker 1: before this right, he built this system that created enormous wealth.
Speaker 1: And the amazing thing is that no one really knows
Speaker 1: how the system works, how they're getting that return. Even
Speaker 1: Jim Simons, the man who built it, doesn't quite know
Speaker 1: what it's doing. When you see a picture of Jim
Speaker 1: Simon as you'd never be like, oh, that's a fancy
Speaker 1: Wall Street hotshot, like he looks like a mathematician. Continued
Speaker 1: to look like a mathematician until the day he died.
Speaker 1: He had the tweed coat, the thick glasses, the wispy hair,
Speaker 1: right penny loafers. He was always smoking. At some points
Speaker 1: he would smoke like three packs of Merit cigarettes a day.
Speaker 3: A machine for smoking cigarettes.
Speaker 1: It's a secret machine. And after that letter to the
Speaker 1: editor about Vietnam, Jim Simons lived a very secretive life.
Speaker 1: He didn't really talk to the press talk about himself. Eventually,
Speaker 1: when he created this money machine, he didn't tell people
Speaker 1: how he did it. He didn't want people to visit him,
Speaker 1: you know, at his investment firm.
Speaker 3: He just wanted to smoke cigarettes and make money.
Speaker 1: Well, and I'm all out of cigarettes. So a lot
Speaker 1: of the story that we're going to tell today comes
Speaker 1: from a great investigative reporter, Greg Zuckerman of The Wall
Speaker 1: Street Journal. He wrote a biography of Simon's called The
Speaker 1: Man Who Solved the Market. So, after getting fired, Jim
Speaker 1: Simons goes to work in academia the State University of
Speaker 1: New York at Stony Brooks Stonybrook University, and by all accounts,
Speaker 1: he was a very good mathematician, but he was an
Speaker 1: even better manager of mathematicians. As head of the department
Speaker 1: there at Stonybrook University, he would travel the country and
Speaker 1: look for young rising stars and convince him to come
Speaker 1: to this math program on Long Island that turned out
Speaker 1: to be one of the best in the country, right,
Speaker 1: and he would bring them all together. Spoiler, some of
Speaker 1: those mathematicians he would later lure to his fund. This
Speaker 1: became one of his real big strengths. So he does
Speaker 1: this for a while. Nineteen seventy eight, Simons is forty
Speaker 1: years old. He's kind of old for a mathematician, right,
Speaker 1: He decides to leave the university because he already looks
Speaker 1: the part. Everyone thinks maybe he's retired.
Speaker 3: But although cigarettes make him look sixty.
Speaker 1: What exactly right? Although he was not going to retire,
Speaker 1: he was going to finally do this idea that he had.
Speaker 1: He was going to use computers to solve the market.
Speaker 1: And it turns out it is much harder than it looks, right,
Speaker 1: like one idea, you're like. Nowadays we think of how
Speaker 1: powerful computers are all the data we have, but back then,
Speaker 1: just having the idea didn't mean that you could pull
Speaker 1: it off.
Speaker 3: That is a classic business truth. Right, Like ideas are cheap, execution.
Speaker 1: Is hard, and in fact, even for Simon's himself, it
Speaker 1: would take probably more than a decade before he could
Speaker 1: really figure out how to do this. Simons teams up
Speaker 1: with a fellow math genius he had met at the
Speaker 1: Institute for Defense Analyses, Lenny Bomb the Bomb. The Bomb
Speaker 1: Bomb's an expert in something called hidden markoff processes, which
Speaker 1: was this way of using probabilistic outcomes of random processes
Speaker 1: to determine hidden that's that's as much as I have
Speaker 1: I was with you. Well, they we're doing what we
Speaker 1: would call today machine learning.
Speaker 3: Machine learning is like, essentially, you build a system in
Speaker 3: a computer. You feeded a bunch of data, and the
Speaker 3: system sort of builds a map of the relationships in
Speaker 3: that data, and then with new data it can kind
Speaker 3: of interpolate or extrapolate and make guesses about what should
Speaker 3: come next. And of course today we have an exciting,
Speaker 3: maybe misleading, confounding term for machine learning.
Speaker 1: We call it AI exactly right, right, And so they
Speaker 1: start this firm called Monometrics. But as you say, the
Speaker 1: key is not the math. They have math in spades.
Speaker 1: What they don't have is they don't have the data.
Speaker 3: Also a classic modern AI problem. Like every AI founder
Speaker 3: I've interviewed, it always turns into a story about collecting
Speaker 3: the data, building the data set.
Speaker 1: And this is the nineteen seventies, right, So data is
Speaker 1: not at the tip of your fingers on a computer keyboard.
Speaker 1: You had to physically go and find data, which is amazing. Right.
Speaker 1: So they have the closing prices every day for you know,
Speaker 1: commodities and bonds and stocks. This was published in the
Speaker 1: Wall Street chart, so everyone had that data.
Speaker 3: It's like one one.
Speaker 1: Number per day per stock, per commodity. Yeah, but what
Speaker 1: you're trying to do is find correlations. You're trying to
Speaker 1: find things in the real world that impact those prices.
Speaker 1: What's the data set for that? They had to go
Speaker 1: look for it. So for instance, they would buy old
Speaker 1: magnetic tapes from commodities trading exchanges, like big giant computer
Speaker 1: magnetic tapes, and they had to figure out how to
Speaker 1: get the data off of it. They would buy stacks
Speaker 1: of books from the World Bank.
Speaker 3: Huh, like reports that the World Bank, like what is
Speaker 3: what are the reports about what countries were doing and
Speaker 3: exchange rates and things like that.
Speaker 1: They had a stafford go to the Federal Reserve offices
Speaker 1: to like write down interest rate changes over the years.
Speaker 1: Because once you have that, then the computer can start
Speaker 1: looking for pattern right. And to be clear, they're not
Speaker 1: like studying these reports and these day they're just in
Speaker 1: shoveling it in, right, And this is still very basic
Speaker 1: machine learning, and the computer at the time keeps making
Speaker 1: mistakes that they didn't really understand. So, for instance, once
Speaker 1: the computer developed a taste for potatoes, main potatoes, so
Speaker 1: Zuckerman tells his story in his book, the system kept
Speaker 1: buying main potato futures in the state of Maine.
Speaker 3: Potato potatoes big harvest year next year or whatever.
Speaker 1: Yes, okay, until two thirds of the company's money was
Speaker 1: in potatoes. They were all in potatoes. And they got
Speaker 1: a call from the regulators, the cft A.
Speaker 3: CFPP commod Futures Trading Commission.
Speaker 1: Right yeah, saying whoa, who are you guys, Like, what
Speaker 1: are you doing over there? You have almost cornered the
Speaker 1: market on potatoes. You have to sell. And they ended
Speaker 1: up losing money on the trade because because blown out
Speaker 1: on potatoes, they had stopped the computer or whatever the
Speaker 1: computer's plan was. But you know, this was just one
Speaker 1: small weird thing. Simon and Baum were really kind of
Speaker 1: nervous about this whole thing. They had taken investors money,
Speaker 1: they didn't really know if their system worked, and as
Speaker 1: the story gets told, they start to like second guess
Speaker 1: the computer and themselves, and they start to think, well,
Speaker 1: I have this intuition that gold's gonna go up because
Speaker 1: of the geopolitical situation, and they'd make some money on that,
Speaker 1: and then they'd lose some money on that, and so
Speaker 1: by doubting their own system, it just wasn't really working.
Speaker 1: And as they say, like it was super stressful because
Speaker 1: like at that point They're just Wall Street investors, right
Speaker 1: with the big computer trying to buy more potatoes, and
Speaker 1: the and the man won't let them buy potatoes. It
Speaker 1: wasn't really the mathematical based system that Jim Simons had
Speaker 1: dreamed of.
Speaker 3: I mean, in a way, it makes sense, right even
Speaker 3: for them, because what they're trying to do is so
Speaker 3: contrary to human nature. Right, Like human nature, especially if
Speaker 3: you are smart, like Jim Simons clearly is is. Well,
Speaker 3: I'm smart. I can look at the market and see
Speaker 3: what's gonna happen. I can understand what drives the price
Speaker 3: of gold and where it's going, and I'm just gonna
Speaker 3: make a bet. I'm going to bet on you know
Speaker 3: my understanding. Right, that is human nature, And what they
Speaker 3: fundamentally want to do is, in a way take themselves
Speaker 3: out of the equation. Right, say, I'm going to build
Speaker 3: a computer and then I'm not gonna use my gut.
Speaker 3: I'm just gonna do what the computer says. And so
Speaker 3: in a way, it's not surprising that even Jim Simons
Speaker 3: can't quite fully commit to taking himself out of the equation.
Speaker 1: You turn on the TV news there's an oil embargo,
Speaker 1: and you think, oh, does my computer know about this
Speaker 1: now is able to do it like I should sell
Speaker 1: or I should buy? Right, So monometrics doesn't really work out,
Speaker 1: and in nineteen eighty two, Jim Simons starts a new company,
Speaker 1: Renaissance Technologies and Renaissance Technologies. He thought, well, it's going
Speaker 1: to be more of a tech investing firm, you know,
Speaker 1: VC kind of thing, which would be great in the
Speaker 1: nineteen eighties, right, But he kept the computer trading idea going,
Speaker 1: eventually folding it into something that would become this amazing fund.
Speaker 1: We talked about the Medallion Fund. And the first thing
Speaker 1: that Jim Simon's was just really good at was recruiting
Speaker 1: the right people to come into Wall Street, which was
Speaker 1: unexpected at the time. Now we're like, oh, you're a
Speaker 1: mathematics major at Princeton University, good luck at Golden Sacks, right,
Speaker 1: But at the time this was somewhat unusual. And he
Speaker 1: starts hiring his fellow mathematicians, quantum physicists, linguists, number theorist, astronomers, sure,
Speaker 1: which if you think about like astronomers looking at all
Speaker 1: this data in the sky and has to find out,
Speaker 1: you know, where a black hole is. These are all
Speaker 1: people who can see the signal in the noise, and
Speaker 1: he's bringing them out to Long Island, right, And he
Speaker 1: had this insight that I think only a mathematician could have,
Speaker 1: which is math geeks are really competitive. You know, we
Speaker 1: may not think of that, but they really want to
Speaker 1: win and they want to prove these like impossible theorems.
Speaker 1: And mathematicians, you know, frankly, if you haven't made major
Speaker 1: awards by the age of thirty or thirty five, they
Speaker 1: kind of feel like their career is a little bit over,
Speaker 1: so they are open to moving to an investment firm.
Speaker 3: It reminds me a little bit of you know, Paul Graham,
Speaker 3: he was the founder or a founder of y Combinator,
Speaker 3: the startup incubator. He wrote this whatever essay blog post
Speaker 3: called Fierce Nerds a few years ago. Fierce Nerds this phrase,
Speaker 3: and his point was like, traditionally in sort of culture,
Speaker 3: the nerd was portrayed as kind of deferential. You know,
Speaker 3: the jack is maybe the alpha, and the nerd is
Speaker 3: kind of subservient or whatever. But there is this type
Speaker 3: that he identified, the fierce nerd that's like the alpha nerd,
Speaker 3: the nerd that really wants to win. To show the
Speaker 3: world that they are smarter, better and like it's a
Speaker 3: classic founder type, right, like the tech founder. But now
Speaker 3: also yeah, now it has become but it's also this
Speaker 3: type of kind of competitive mathematician who who Simons had
Speaker 3: spotted and recruited.
Speaker 1: The second major thing I think Jim Simons was doing
Speaker 1: is this relentless focus on data, on hoovering up more
Speaker 1: and more data. They got really good at finding, you know,
Speaker 1: intra day prices, little tick data they call it stocks
Speaker 1: going up going down all throughout the day, not just
Speaker 1: the closing price. You know. They were looking at newspapers
Speaker 1: to get the news items in there, all nacs, old
Speaker 1: punch cards.
Speaker 3: And when you say looking at you mean in putting
Speaker 3: into the system, right, that's just more data, more data.
Speaker 1: Yes, and more importantly, they were good at cleaning up
Speaker 1: the data. And this is something you don't really think about,
Speaker 1: but there's mistakes all over the place, there's mission gaps
Speaker 1: in all forms of data. Figuring that out and figuring
Speaker 1: out what to put in its place, called cleaning the
Speaker 1: data is a mathematical problem, and they were very good
Speaker 1: at it.
Speaker 3: But so simple, such a simple idea, and yet obviously
Speaker 3: so powerful.
Speaker 1: You know, I've downloaded data sets that were like two
Speaker 1: hundred thousand items when I was in business school. And
Speaker 1: when you have two hundred thousand items, you can't find
Speaker 1: a missing cell in there or someone who's like put
Speaker 1: a wrong number in there. Now imagine millions and millions
Speaker 1: and millions of pieces of data. It's very hard to
Speaker 1: not make something that will stop a computer dead in
Speaker 1: its tracks. Right. And so even with all of this,
Speaker 1: it did take them years and years and years to
Speaker 1: really start making money with this system. You know, they
Speaker 1: would place these big bats on a move in the
Speaker 1: stock market. Sometimes it would work, sometimes it wouldn't, and
Speaker 1: they couldn't figure out why. And they finally brought in somebody,
Speaker 1: a game theorist that helped make it work. His name
Speaker 1: was Elwin Burlecamp, and he essentially said, you know, the
Speaker 1: problem here is that you're acting too much like an
Speaker 1: investment fund. To make the quant thing work. You need
Speaker 1: to start acting more like a casino.
Speaker 4: Coins falling out of the slot machine after the break.
Speaker 3: That is the end of the break. Now we're going
Speaker 3: to talk about game theory and why it's a good
Speaker 3: tax like a casino. If you are Jim Simons in Renaissance.
Speaker 1: The year is nineteen eighty eight. Jim Simon's quant investing
Speaker 1: fund that started out as Monometrics has changed its name
Speaker 1: to Medallion because of all the mathematical prizes his employees
Speaker 1: have won, including himself. And they brought in this expert
Speaker 1: in game theory. Elwyn Burlecamp and Burlacamp ran in the
Speaker 1: same circles as a famous investor and gambler named Ed Thorpe,
Speaker 1: who you've met.
Speaker 3: I interviewed him. Yeah, he's a super interesting guy. Know
Speaker 3: now as as an investor, he made a ton of
Speaker 3: money as an investor, but he earlier in his career
Speaker 3: went to Vegas, learned how to count cards, wrote a
Speaker 3: book called Beat the Dealer did this amazing thing, and
Speaker 3: I think the sixties with Claude Shannon, another super interesting guy,
Speaker 3: where they built this machine to like understand how the
Speaker 3: roulette ball was going to break and like basically beat roulette.
Speaker 3: Like extremely interesting finance math guy.
Speaker 1: Yeah. So Ellen Burlacamp knows Ed Thorpe and is thinking
Speaker 1: about gambling strategies, right, and he looks at the Medallion
Speaker 1: Fund and he says, you're making all these big bets
Speaker 1: on market. It moves right, and you win sometimes and
Speaker 1: you lose sometimes. But the key is, if you're going
Speaker 1: to rely on probabilities, you need a lot of bets.
Speaker 1: Imagine a casino that rolled the roulette wheel once a day, Right,
Speaker 1: they could win money from the customers. They could lose
Speaker 1: a bunch of money, but you wouldn't know it's random
Speaker 1: at that point. Yes, right, but you do ten thousand
Speaker 1: roulette wheel spins, one hundred thousand, a million roulette wheel spins,
Speaker 1: and the casino has an edge, and they will win
Speaker 1: money after a million turns.
Speaker 3: It's the law of large numbers. If the odds are
Speaker 3: in your favor, you want to be making essentially as
Speaker 3: many bets as possible.
Speaker 1: Yeah, and he says, let's do at the medallion fund
Speaker 1: what casinos do. Get a tiny advantage, figure out how
Speaker 1: to win slightly more than fifty percent of the time,
Speaker 1: and then just make a lot of bets. So instead
Speaker 1: of you know, making one bet and seeing how it's
Speaker 1: going to.
Speaker 3: Turn out, how all it on Maine potatoes and see
Speaker 3: it how the harvest comes out next.
Speaker 1: Year, you know you are going to make bets on
Speaker 1: the market the last maybe a couple of days one
Speaker 1: day intra day, this sort of high speed trading that
Speaker 1: we would eventually see in the market. And here's an
Speaker 1: example of like what they started to see. When they
Speaker 1: did this, they needed just tiny little advantages they could
Speaker 1: exploit in a small way. So the computer spotted something
Speaker 1: that was futures traders out in the market. If they
Speaker 1: had a good week, you know, made money on their
Speaker 1: contracts over the week, they would tend to sell at
Speaker 1: the end of the day on Friday and then rebuy
Speaker 1: the contracts on Monday morning. So if they're selling on Friday,
Speaker 1: the price goes down a little bit. If they're buying
Speaker 1: on Monday, the price goes up a little bit. And
Speaker 1: they didn't really know why. Maybe you know, the future
Speaker 1: traders wanted to chill on the weekend, or you know,
Speaker 1: maybe they were worried about world events. Whatever it was.
Speaker 1: The Medallion Fund was like, well, we're just gonna buy
Speaker 1: a bunch of viewers of contracts on Friday, We're going
Speaker 1: to sell them on Monday morning. We're going to make
Speaker 1: a little bit of money. And that is consistent, at
Speaker 1: least until someone else discovers it.
Speaker 3: Yeah, I mean that one is interesting, right, because you
Speaker 3: can tell it as a story and it makes some sense.
Speaker 3: And there's like human beings taking actions for reasons. Presumably, yes,
Speaker 3: the best things, most of the things they do, there
Speaker 3: is no story. It's just the computer is like, do this,
Speaker 3: and they do it right. That to me is the
Speaker 3: ideal version, because if you can tell a story about it,
Speaker 3: then somebody else is going to figure out that story.
Speaker 1: Why do you think I picked it out? I am
Speaker 1: a story I am a storyteller. I want to explain
Speaker 1: this to you. That's why I picked that example. No,
Speaker 1: presumably thousands thousands of bets where things you could not
Speaker 1: tell a story about because they were correlations of an
Speaker 1: interest rate in Japan affecting the commodity prices in Mexico
Speaker 1: and you can't see the connection.
Speaker 3: Matt Levine, the finance writer, you and I both like
Speaker 3: a lot. He had this thing once about how ren
Speaker 3: as On Simon's firm. Actually at some point like didn't
Speaker 3: want finance people, right, because finance people are always looking
Speaker 3: for the story. Why what is the story of this?
Speaker 3: But if you can tell a story, there is no edge,
Speaker 3: right because somebody else can tell the story. So you
Speaker 3: just want to trust the machine.
Speaker 1: In Greg Suckerman's book. One of the traders for the
Speaker 1: firm said, it would probably be better if stock prices
Speaker 1: didn't have names on them.
Speaker 3: It's like if the company didn't have a name, if
Speaker 3: we were saying, like, oh, what should we do about
Speaker 3: Oracle stock?
Speaker 1: Yeah, so if you're trading Oracle, you probably have some
Speaker 1: deep emotional feeling.
Speaker 3: Like Larry Ellison, you don't like Larry Elison.
Speaker 1: But if it were stock number eighty seven, you would
Speaker 1: be able to just be like, well, this is the
Speaker 1: movement in the numbers, and I don't care what the
Speaker 1: company is.
Speaker 3: Basically the machine says by stock eighty seven, Okay, by
Speaker 3: stock eighty seven.
Speaker 1: Yeah, it would be fewer employees stopping the machine.
Speaker 3: Right.
Speaker 1: So this casino tweak that Briller Camp brought it actually worked,
Speaker 1: and by nineteen ninety they're having very good trading days.
Speaker 1: They're like going up one percent a day sometimes, and
Speaker 1: they were celebrating. They had to start a new rule,
Speaker 1: which is, you cannot break out the champagne unless you
Speaker 1: go up three percent a day in one day, in
Speaker 1: one return in one day. Yeah, for the whole year
Speaker 1: of nineteen ninety Medallion Fund, it's finally churning. Right, it
Speaker 1: returned fifty five percent and that's after fees, and they
Speaker 1: were charging a ton.
Speaker 3: Of Hey, they have a lot of fees to return
Speaker 3: by fifty five percent after fees. I guess I say
Speaker 3: them any amount of fees.
Speaker 1: Yeah, exactly right. So obviously Jim Simon's Renaissance technologies in the
Speaker 1: Medallion Fund, they are not the only people doing this.
Speaker 1: This is the nineteen nineties, right, Uh, there's David Shaw
Speaker 1: starts d E. Shaw, big quant trading firm. Ken Bezos
Speaker 1: worked there in his career, that's correct, Ken Griffins had
Speaker 1: just started Citadel, Kepler, Morgan Stanley, Everyone's playing around with
Speaker 1: these techniques. But Jim Simon's and the Medallion Fund they
Speaker 1: managed to stay ahead of them all. In a field
Speaker 1: where everybody's looking for an edge in the data, they
Speaker 1: had an edge on the edge. So how did they
Speaker 1: do it?
Speaker 3: How did they do it?
Speaker 1: So there's a couple of things. It's just my opinion, right.
Speaker 1: So one thing they did was to say small and focused.
Speaker 1: They closed the Medallion Fund to outside investors and said
Speaker 1: we're going to trade solely for the employees of Renaissance itself,
Speaker 1: for the people who work here because if you have
Speaker 1: tens of billions of dollars one hundred billion dollars, it's
Speaker 1: hard to take advantage of these tiny little moves.
Speaker 3: Well, right, because once you start buying, yes, you drive
Speaker 3: the price up, or you start selling, you drive the
Speaker 3: price down. You get big enough, you're actually moving the
Speaker 3: market and losing your edge, right, classic problem for big funds.
Speaker 1: Actually, Medallion also focused their efforts on where their data
Speaker 1: was the best, which was commodities, currencies, and bonds. Mostly
Speaker 1: they would eventually do stocks, but we'll get to that
Speaker 1: in a moment. And Jim Simmons had this other edge
Speaker 1: that I feel like is super rare on Wall Street.
Speaker 1: His employees loved him and they were loyal, and almost
Speaker 1: no one ever left. I mean, they liked the intellectual
Speaker 1: atmosphere being run by mathematicians, they liked the money. And
Speaker 1: remember they were sort of sequestered out on Long Island
Speaker 1: in a small town, right. They weren't having beers with
Speaker 1: the guys from Goldman Sacks. They weren't getting job offers
Speaker 1: on the street because no one knew their name.
Speaker 3: It's almost like like the machine the machine learning is
Speaker 3: a black box. But then the firm itself. Renaissance is
Speaker 3: like a black box around the black box.
Speaker 1: Black boxes all the way down, but eve.
Speaker 3: Until the pile of gold at the center.
Speaker 1: But you know, his employees were happy and content and
Speaker 1: obviously becoming very rich until Jim Simons made one highre
Speaker 1: that maybe wasn't the best idea.
Speaker 5: In a minute, finally, Jim Simons is going to make
Speaker 5: a mistake.
Speaker 3: As I recall from before the break, Robert Smith, tell
Speaker 3: me what happens next.
Speaker 1: Such a small mistake, he still makes a ton of money.
Speaker 1: Spoiler right, there is one place in New York State
Speaker 1: with as many math geniuses as Renaissance Capital IBM, specifically
Speaker 1: the Watson Research Center upstate, and it was there that
Speaker 1: Jim Simons found a pair of geniuses working on speech
Speaker 1: recognition algorithms, Peter Brown and Robert Mercer. And they arrived
Speaker 1: at the Medallion Fund in nineteen ninety three and they
Speaker 1: solved this last big challenge the fund had trading stocks. Now,
Speaker 1: of course, you may notice that like linguistic speech recognition
Speaker 1: software is very similar to what the astronomers were doing
Speaker 1: to what the codebreakers are.
Speaker 3: Doing classic machine learning, classic machine learning problem.
Speaker 1: And they come in and they look at the problem
Speaker 1: of trading stocks. So if all the things on Wall Street,
Speaker 1: apparently like stocks are the most clugey and human, you know,
Speaker 1: you've got to have someone make the trades for you, right,
Speaker 1: okay at that time, at that time, and the computer
Speaker 1: would have these fancy, optimal trades that you had to do.
Speaker 1: But then sometimes in the real world it would just
Speaker 1: be like, no, you can't short that, or eh, you know,
Speaker 1: we can't get the leverage necessary the margin on the
Speaker 1: stock to make that trade happen, and the computer would
Speaker 1: just sort of stop, you know, and couldn't do the
Speaker 1: trades they needed to do. Remember, there's a lot of
Speaker 1: things that have to happen for all of these to work.
Speaker 3: It's like the second order effects in some like imagine
Speaker 3: a frictionless plane unif verse, the model could win in
Speaker 3: the stock market, but the frictions of the actual stock
Speaker 3: market were stumping it.
Speaker 1: Yeah. So Brown and Mercer come in and they programmed
Speaker 1: their computers so that it was all working together. I
Speaker 1: guess it was in pieces before. But they're like, we
Speaker 1: have one model for everything. And what that meant is
Speaker 1: that the model itself could change its algorithm as it went.
Speaker 1: And so if certain trades were not working, it could
Speaker 1: find the second best, the third best, and then adapt
Speaker 1: all the strategies depending on what they were actually able
Speaker 1: to do in the real world.
Speaker 3: So it's sort of like real time or almost real
Speaker 3: time updating and feedback there.
Speaker 1: And if certain trades were working, the computer could allocate
Speaker 1: more money to those trades without a human being, like yes, no,
Speaker 1: spend my ten million dollars, don't spend my ten million dollars.
Speaker 1: And the two of them were legendary because they worked
Speaker 1: as this sort of pair in the Greg Zuckermann book
Speaker 1: The Man Who Solved the Market them like Penn and Teller.
Speaker 1: So Peter Brown was the I forget who's who in that.
Speaker 3: One of them talks and one of them doesn't. They're
Speaker 3: both magicians. So which Peter Brown was the one who.
Speaker 1: Talked to the one the one to talk. So Peter
Speaker 1: Brown did all the talking right. He never stopped moving,
Speaker 1: He slept at the office, he yelled at people, he
Speaker 1: inspired people. He was just a big character. And Robert Mercer,
Speaker 1: Bob Mercer was the silent one. He was quiet in
Speaker 1: the telling, Peter Roy says, well, Bob comes up with
Speaker 1: all the ideas silently, and I make them work silent.
Speaker 1: Bob Penn and Teller would eventually double the profits of
Speaker 1: the stock trading arm and they would run the firm. Eventually,
Speaker 1: when Jim Simons stepped back, they moved the Medallion Fund
Speaker 1: into foreign markets. It was this constant move of collecting
Speaker 1: more data, more sources to have their giant computer system
Speaker 1: look at, and then trading in more and more and
Speaker 1: more markets. Right. And you know, I would love at
Speaker 1: this point to have, you know, some sort of crisis
Speaker 1: in the computer, some sort of moment where they stop
Speaker 1: making money.
Speaker 3: You would love it for narrative purposes, to be clear, please, Yeah,
Speaker 3: But you know it didn't really happen.
Speaker 1: In the worst possible markets. The Medallion Fund was doing well.
Speaker 1: I mean there would be some dicey days, like literally
Speaker 1: days like during the tech crash in two thousand, where
Speaker 1: they're like, oh, we're losing a bunch of money. What
Speaker 1: are we gonna do? And you know a few weeks later, ah, yeah,
Speaker 1: we are fine. Right. It kept going up, as we said,
Speaker 1: you know, sort of more than fifty percent a year,
Speaker 1: and with low volatility, like there's no risk to this.
Speaker 1: It's just insane when you think about it.
Speaker 3: You shouldn't be able, like I don't mean morally, I
Speaker 3: mean just yeah, kind of the laws of investing physics
Speaker 3: suggests that you shouldn't have that kind of return year
Speaker 3: after year after year. It's just somebody should catch you.
Speaker 3: But it doesn't happen.
Speaker 1: And it's interesting, right. I think anyone else who did
Speaker 1: this would say, I am a genius. I have insight,
Speaker 1: I know what I'm doing. But you'll notice in this story,
Speaker 1: like Jim Simons himself has sort of back a little bit.
Speaker 1: He's running the machine, he's hiring the people, he's letting
Speaker 1: them run the actual machine, right, And even the people
Speaker 1: who are making these decisions at this point, like Robert Mercer,
Speaker 1: he has a line that I love from the Zuckerman book.
Speaker 1: I'm gonna read this here. We're right fifty point seven
Speaker 1: percent of the time, but we are one hundred percent
Speaker 1: right fifty point seven percent of the time. You can
Speaker 1: make billions that way.
Speaker 3: Isn't that the basically the Anchorman line. There's that line
Speaker 3: I think gets sixty percent of the time.
Speaker 1: It works every time exactly right. So it's funny, right,
Speaker 1: because that is the philosophy that gets you this money.
Speaker 3: That's the casino like, that's how casinos just.
Speaker 1: Need the little bit of edge and then you need
Speaker 1: to ruthlessly exploit that and you have to keep the edge.
Speaker 1: So at this point, there's really no drama from the algorithm.
Speaker 1: It is working, it is six seating, it is evolving. Really,
Speaker 1: the only drama really comes from the personalities. As Jim
Speaker 1: Simons gets older, he starts to step back from the business,
Speaker 1: and I will say some minorly dodgy things start to happen.
Speaker 1: At one point, they get caught booking short term gains
Speaker 1: in the market, which means immediate trading profits as long
Speaker 1: term gains, essentially paying less tax than they should. And
Speaker 1: the way they do this is is they're trading like
Speaker 1: with the stocks in a basket stored at the bank,
Speaker 1: and they don't really take the profits out till after
Speaker 1: a year well whatever it was. The RS is like, no, no,
Speaker 1: you can't do that, and so they had to pay
Speaker 1: a reported seven billion dollars to the IRS.
Speaker 3: A lot sounds like a lot. I don't know how
Speaker 3: much they have, but seven billion is a big number.
Speaker 3: They have more, they have more than seven billions.
Speaker 1: Yeah, and then Bob Mercer silent Bob starts to get
Speaker 1: a little bit weird at the office, right, it starts
Speaker 1: to get into politics, always a bad idea. So apparently
Speaker 1: he's very conservative. He's a right wing guy, and he
Speaker 1: starts to talk about conspiracies at work. Now this is
Speaker 1: puzzling to everyone, right, because we have a firm of
Speaker 1: brilliant mathematicians, Bob Mercer, brilliant logical guy. Right, but he's
Speaker 1: getting to these arguments at the firm about how, oh,
Speaker 1: you know, global warming is overblown, or that like radiation
Speaker 1: exposure is actually good for you. This is all stuff
Speaker 1: that's like circulating the internet and infringe, you know, science stuff.
Speaker 1: He actually funded a scientist who collected human urine to
Speaker 1: extend human longevity.
Speaker 3: Okay, I guess didn't work as far as.
Speaker 1: I know, I don't know, but it started to rub
Speaker 1: people the wrong way in this like really smoothly functioning
Speaker 1: you know, deep black box out on Long Island. Because
Speaker 1: he was now super rich, Robert Mercer, he started using
Speaker 1: the money to actually influence politics, to support right wing causes.
Speaker 1: He and his daughter's especially create a political action committee.
Speaker 1: They started to work with Steve Bannon and Kelly and
Speaker 1: Conway before Donald Trump was running for election in twenty eleven,
Speaker 1: and apparently he was the one that urged Donald Trump
Speaker 1: to hire them. Right that sort of sent the Donald
Speaker 1: Trump campaign in sort of a right word direction. So
Speaker 1: he becomes a big donor to Trump. This comes out
Speaker 1: and all of a sudden, Bob Mercer and Rebecca Mercer's
Speaker 1: daughter become this sort of symbol of the shadowy right
Speaker 1: wing money making forces propelling this president that certain people
Speaker 1: don't like into power. People start to pick it outside
Speaker 1: the firm and appearances by Robert Mercer. There's a huge
Speaker 1: piece of The New Yorker about him. And eventually Jim Simons,
Speaker 1: who at this point you know, is leaving the working
Speaker 1: of the firms to Bob and Peter, he has to
Speaker 1: step in and say, Bob Mercer, you need to resign,
Speaker 1: and Bob Mercer steps back. And in case you were
Speaker 1: one wondering, they still made money money to the whole,
Speaker 1: the whole whole thing. At this point, you can have
Speaker 1: all the drama in the world because the computer has
Speaker 1: no drama anymore. It is just getting better and better.
Speaker 3: How about this for a fuego take, Maybe the human
Speaker 3: drama is actually helpful for the firm, Yes, because it
Speaker 3: distracts people from wanting to question the results of the computer.
Speaker 1: The more they fight, the better their return, the less
Speaker 1: they want to fiddle with the computer. Yeah, that's exactly right.
Speaker 3: So what did you say, Robert, the return has been
Speaker 3: for whatever thirty years? Do you say sixty six percent
Speaker 3: a year?
Speaker 1: Sixty six percent were the trading returns, okay, and then
Speaker 1: there were fees, which got bigger and bigger every year
Speaker 1: because they're building this giant computer system, right, and so
Speaker 1: after fees, yeah, thirty nine percent still unbelievable.
Speaker 3: So okay, So that since nineteen eighty eight is twenty
Speaker 3: twenty one, twenty one, okay, So let's just play around
Speaker 3: with it for a secon because I think it's hard
Speaker 3: to really grasp.
Speaker 1: Right.
Speaker 3: So I was a teenager in nineteen eighty eight, but
Speaker 3: I I bought CDs, right, this was the CD era.
Speaker 3: What did CDs cost in nineteen eighty eight, fourteen ninety nine?
Speaker 3: At Tower Records? Okay, Sam Goody, I wasn't cool enough
Speaker 3: to shof at Tower Records. I shopped at Sam Goodye
Speaker 3: nineteen eighty eight albums Green Rim Green, which I loved.
Speaker 1: Are you doing the math?
Speaker 3: Roy instead of buying RM's Green for fourteen ninety nine.
Speaker 3: In nineteen eighty eight, I had put fourteen dollars in
Speaker 3: ninety nine cents into the Renaissance Medallion Fund and left
Speaker 3: it there and let them charge me their fees. How
Speaker 3: much would that fourteen dollars in ninety nine cents from
Speaker 3: nineteen eighty eight have turned into buy what did you say?
Speaker 1: Twenty twenty one, thirty three years at thirty nine percent annually,
Speaker 1: seven hundred and eighty five thousand dollars, you could buy
Speaker 1: a house in stead of Green. Although Green was a
Speaker 1: fabulous it's a.
Speaker 3: Great album, but I'd rather pay for my children to
Speaker 3: go to college than have listened to Green. That is amazing.
Speaker 3: And then, of course the wild thing is if you'll
Speaker 3: leave the seven hundred thousand dollars in there for one
Speaker 3: more year at thirty nine percent, it's like a.
Speaker 1: Million, basically, exactly exactly.
Speaker 3: I also bought Rattlin Hum that you're just looking at
Speaker 3: the list, So that's another seven hundred grand.
Speaker 1: So need to say. Jim Simons made a ton of
Speaker 1: money out of his money making machine and made money
Speaker 1: for a ton of other people. He died in twenty
Speaker 1: twenty four and spent his last few years giving away
Speaker 1: a lot of that money and traveling on his yacht.
Speaker 1: I mean he earned it, right. He was a big
Speaker 1: donor to Stonybrook University, where he had run the department.
Speaker 1: He supported research into autism, and I love this part.
Speaker 1: He had a fund to help math teachers stay in
Speaker 1: public education by basically giving the money to still teach
Speaker 1: in high schools instead of you know, going to Wall
Speaker 1: Street like he did. Fine. At the end of these
Speaker 1: investing episodes, I usually say that the investor that we're
Speaker 1: featuring found an inefficiency in the market, right, that they
Speaker 1: somehow did a bunch of research, found data that no
Speaker 1: one else had, and then they exploited that in the
Speaker 1: market to make a lot of money. So when I
Speaker 1: talked about Jesse Livermore, the speculator back in nineteen twenty nine,
Speaker 1: he was a chalkboard boy who knew the numbers of
Speaker 1: the stock market inside and out and had.
Speaker 3: That infra day trading data in his brain because he
Speaker 3: was writing it down on the chalkboard.
Speaker 1: All exactly right. And when we talked about Warren Buffett,
Speaker 1: he would actually physically go to the CEO of a
Speaker 1: firm and get information that no one else had at
Speaker 1: the point.
Speaker 3: On Saturday, you just go on the weekend and knock
Speaker 3: on the door and say, tell you about your company.
Speaker 1: The thing about those two examples is that eventually any
Speaker 1: sort of edge you have in the market gets competed away.
Speaker 1: Other people see the edge, other people get the data.
Speaker 1: Other people see what you're doing and just do the
Speaker 1: exact same thing. But Renaissance Technologies and the Medallion Fund,
Speaker 1: at least so far, are in the middle of this.
Speaker 1: And I don't know if it's that they continue to
Speaker 1: ingest so much more data that they're finding new inefficiencies
Speaker 1: all the time, or if their computing power is just
Speaker 1: getting more and more powerful so that they're able to
Speaker 1: spot things that no one else has. But Renaissance, the
Speaker 1: Medallion Fund, you know, is still going strong. It's still
Speaker 1: making money for its employees, it's investors. One of the
Speaker 1: things I do love about this story is that even
Speaker 1: the people involved, the mathematicians, are just like stunned by
Speaker 1: the amount of money that they make. You know, in
Speaker 1: Wall Street, there's a kind of feeling like, ah, weirned it.
Speaker 1: But these are logical, rational people who, at various points
Speaker 1: in this story stop to ask themselves what are we doing? Like,
Speaker 1: are we doing good? Are we doing harm? If we're
Speaker 1: making all this money, who is losing the money? Who's
Speaker 1: on the other side of these trades? You know, if
Speaker 1: we have fifty point seven percent, who's at forty nine
Speaker 1: point three And they decide at some point they're like, yeah,
Speaker 1: you know, we're adding liquidity to the market, We're you know,
Speaker 1: closing inefficiencies. But really what we're doing is there are
Speaker 1: people out there making emotional decisions and we're not making
Speaker 1: emotional decisions. And when they get a little too optimistic
Speaker 1: or a little too pessimistic, they sell early, they buy
Speaker 1: too soon. Whatever it is, we spot it, We take
Speaker 1: a little percentage of that trade, and we win. That
Speaker 1: is their theory about what's happening out there. It's them
Speaker 1: versus the emotions.
Speaker 3: That sounds like a suspiciously human centered narrative story, right
Speaker 3: given Like there's another version of the story, which is
Speaker 3: maybe it was that the beginning, but surely at this
Speaker 3: point a lot of the time, it's the renaissance computers
Speaker 3: against somebody else's computers. And it's not that the other
Speaker 3: computers are more emotional. It's just that the Renaissance computers,
Speaker 3: the Renaissance algorithm, the Renaissance AI is just better. Their
Speaker 3: computers are just better than the other computers for now.
Speaker 1: And the key to that is you would have to
Speaker 1: read this story in machine language. Why are the Medallion
Speaker 1: computers better than everyone else's computers? Only the computers know.
Speaker 1: Our producer is Gabriel Hunter Chang, our engineer is Sarah Bruguier,
Speaker 1: and our showrunner is Ryan Dilly. I'm Jacob Goldstein, and
Speaker 1: I'm Robert Smith. We'll be back next week with another
Speaker 1: episode of Business History, a show about the history, wait
Speaker 1: for it, of business