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