Renaissance Technologies

I always used to misspell Renaissance as I was typing it out at R-E-N, and then I would sort of like not really know what came from there, but I learned a mnemonic to make sure I get it right.

Oh, I thought you were going to say you've typed it so many times now over the past month.

Well, there's that too, but are you ready for this? You can't spell Renaissance without A-I.

Oh.

All right, let's do it. Who got the truth? Is it you, is it you, is it you who got the truth now? Is it you, is it you, is it you? Sit me down, say it straight, another story on the way, who got the truth?

I'm David Rosenthal.

And we are your hosts. They say, David, that as an investor, you can't beat the market or time the market, that you're better off indexing and dollar cost averaging rather than trying to be an active stock picker. They say there's no persistence of returns for hedge funds, that this year's big winner can be next year's big loser, and that nobody gets huge outperformance without taking huge risk.

When I was in college, I actually took an economics class with Burton Malkiel, who, of course, you know, was involved in starting Vanguard and is a big proponent of all that. And that is what I learned, Ben.

Well, David, it turns out they were wrong. Today, listeners, we tell the story of the best performing investment firm in history, Renaissance Technologies or Rentech. Their 30-year track record managing billions of dollars has better returns than anyone you have ever heard of, including Berkshire, Hathaway, Bridgewater, George Soros, Peter Lynch or anyone else. So why haven't you heard of them? Or, if you have, why don't you know much about them?

Oh, we are going to talk a lot about that towards the end of the episode, because I think it's kind of the key to the whole thing.

Ooh, cliffhanger, David. I'm excited. So what exactly does Renaissance do? Why does it work, and how did it evolve to be the way it is today? And while the resources that are out there are scarce, because, for one, employees sign a lifetime non-disclosure agreement, David and I are going to take you through everything we've learned about the firm from our research, dating all the way back before Jim Simons started as a math professor, to understand it all.

Yeah, just like how we say, every company has a story, every company's story is powered by payments. And JP Morgan Payments is a part of so many companies that we talk about on Acquired. It's not just the Fortune 500 too, they're also helping companies grow from seed to IPO and beyond.

Yep. So with that, the show is not investment advice. David and I may have investments in the companies we discuss, or perhaps wish we did. And this show is for informational and entertainment purposes only. David, where do we start our story today?

Ah, well, we start in 1938 in Newton, Massachusetts, which is a fairly wealthy suburb just outside of Boston, where one, James Simons, is born. Both of Jim's parents were very, very smart, especially his mother, Marsha. His dad was a salesman for 20th Century Fox, the movie company. His job was. he went around to theaters in the Northeast and sold packages of movies to them.

Super cool.

By the way, we know all this because we have to thank Greg Zuckerman, author of The Man Who Solved the Market, which is the only book out there that is solely dedicated to Rentech and Jim Simons. And we actually got to talk to Greg in our research. He helped us out a bunch. Thank you, Greg.

And helped fact check a few of our assumptions of what happened after the book came out.

So that was Jim's parents. But really a major influence on him growing up was his grandfather, Marsha's dad. There's already kind of echoes of the Bezos story here, with the grandfather, the mother's father, and spending a bunch of time with him and rubbing off on young Jeff or young Jim in this case. And Bezos, of course, would get his start in his career at D.E. Shaw, a quant fund coming.

up at the same time as Rentech.

But back to Jim here. in the 1940s. His grandfather, Peter, owned a shoe factory that made women's dress shoes. Jim spends a ton of time there, growing up at the factory. So Jim's grandfather, Peter, was quite the character.

Which I think he probably would say that thousands of times the rest of his life.

I think so. If you watch interviews with Jim, his hands are always twitching because he has chain smoked his entire life, probably going back to like age 10 in the factory.

Three packs of merits a day.

Unbelievable.

Although I think he quit later in life, but he definitely chain smoked the better part of the first. call it 75 years or something.

I mean, there's these famous stories of the conference rooms at RENTEC and the war rooms when the market is going through like a crazy gyration and it's just filled with cigarette smoke and it's all Jim.

Different time.

Different time. So back to Jim's childhood, though, here in the Boston suburbs. He grows up certainly not uber wealthy or uber rich, but very, very solidly upper middle class. and especially he's an only child. He has all the resources of his parents, his family, his grandfather's, this sort of well to do entrepreneur.

Yes. Important to tease out the difference between those two things. Yes.

Very, very important. And what he means when he says he has no interest in business, it's because from a pretty young age, he gets really into math. So the legend has it. when Jim is four years old, he stumbles into one of Zeno's famous paradoxes from ancient Greek times.

Yep. This is great. The basic gist of Zeno's paradox is, if you are always taking a quantity and dividing it by two, you will never hit zero. You will asymptotically approach zero, but you will never actually touch zero. You need to do addition or subtraction to do that.

So Jim's dream is to go to MIT down the street in Cambridge and study math. He graduates high school in three years. And during the second semester of Jim's freshman year there, he enrolls in a graduate math seminar on abstract algebra. So pretty, you know, heady stuff.

Yeah. And Jim would go on to finish his undergrad at MIT in three years and get a master's in one year. Yeah.

Pretty, pretty smart. But it turns out that that freshman year grad seminar he took actually has a big impact on him, because he doesn't do well in the class. He can't keep up. And Jim's pretty self-aware here. There are other people at MIT who never run into problems.

Which is, you know, what do you do with that information? You realize you have to add a few of your skills together to become the best at something. You have to be smart and something else.

Yes. So Jim's own words on this are, I was a good mathematician. I wasn't the greatest in the world, but I was pretty good. But he recognizes, like you said, Ben, that he has a different advantage that most of the super geniuses lacked. And that's that, as he put it, he had good taste.

By the way, this is exactly the same thing as Jeff Bezos in college, realizing he wanted to be a theoretical physicist. He met some of the extreme brainpower people that would go on to become the best theoretical physicists in the world. And he said, I'm smart, but I'm not that smart. And so switch to computer science.

I think the analogy here is like sports. There are all-star players, there are Hall of Famers, and then there's LeBron and MJ. And Jim ends up being a Hall of Famer mathematician. But he's not Tom Brady.

I mean, he's got a pretty important theorem named after him.

That goes on to become a foundation of string theory and physics, which isn't even Jim's field.

Crazy.

So this realization that Jim has about himself, though, both that he's not the smartest person in the room at a place like MIT, but he can hang with them, and that he has this taste concept, I think, becomes one of the most important keys to the secret sauce that ends up getting built at Rentech, which is that he can relate to everybody. He understands what's going on. Any person off the street probably couldn't even really have a conversation with these folks, but he can. And yet he also has the perspective, maybe some of this is from his grandfather, of what is important out there in the real world. And, as a result, all of his friends at MIT and these super smart people, they look up to him, because you aren't like the kid in the corner at the high school dance.

He's the extroverted theoretical mathematician.

Yes. So he was elected class president in high school. You know, he smokes cigarettes. He's popular with the ladies. He kind of looks like Humphrey Bogart.

Yep.

So, after graduation, Jim leads his buddies on a road trip with motor scooters. You can't make this stuff up. From Boston down to Bogota, where one of his classmates is from. The idea is that they're going to do something so epic that the newspapers are going to have to write about it. So they all load up on scooters and drive down to Bogota.

It's honestly crazy that this group of people took this type of risk.

Totally crazy. So after he's done at MIT and after the road trip, Jim heads out to Berkeley in California so that he could do his PhD with the professor Xing-Hsien Cheung. And much later in life, Jim would collaborate with Cheung for the Cheung-Simons theory that we talked about earlier, that becomes one of the foundational parts of string theory and physics. But before Jim leaves for the West Coast, he meets a girl in Boston and they decide to get engaged in four days.

Which is so interesting to think about because at that point in time, there was such an advantage to just being there. This wasn't even the trading floor, but information is all so manual and also relationship driven in the markets that there was basically no way to be in on the action unless you were physically there in on the action.

Exactly. Yeah. You couldn't just log into Yahoo Finance or something or open the stocks app on your iPhone, which even the information they were getting was God knows how long delayed from New York or from Chicago for the futures and commodities that are being traded that Jim gets into. He's as close to the action as he can possibly be, but he's a long, long way from the action. Nonetheless, when he starts out doing this, Jim hits a hot streak and he goes up 50% in a few days.

Trading is easy.

Trading is easy. He says, I was hooked. It was kind of a rush.

I bet.

Except he ends up losing all of his profits just as quickly. Yeah.

Important to learn that lesson early.

Yes. And also right around this time, Barbara, his wife, gets pregnant with their first child and is like, you can't be driving into San Francisco every morning at gambling our future like this.

Right. Effectively playing the ponies.

Yeah, exactly. So, Jim's like, okay, okay, I'll stop. I'll focus on academia for now. So he finishes his PhD in two years. They come back to Boston and he joins MIT as a junior professor at age 23..

When I read this, I couldn't believe that this was Jim Simon's first business venture. Like it's so random, but it really is emblematic of just how much he was thrill seeking and just looking for anything that was unexpected, different, exciting. He just gets bored fast.

Totally. Not just is this the start of his entrepreneurial career? The seeds of this, financially, are what go on to start Rentech.

It's wild.

Totally wild. So Jim takes a year off and goes down to Bogota.

This is a guy with an MIT undergrad and master's and a Berkeley PhD in theoretical math.

Who's now a professor at MIT.

Who is taking a year off to go work on a flooring company in Bogota.

Yes, accurate. So he does that for a year. They get it set up. He gets bored again. He's like, all right, I don't want to just run this company.

He's really racking them up.

But he spends a year there and he's like, ah, got the itch again. And you know, the junior professor salary isn't that much. And like we said about him back from his childhood days, he sees the appeal in being rich. He's like, this is not a path to be rich.

It was basically formed with this idea that, one, across various branches of our government, we need better collaboration and cross funding of the same initiatives. And two, there are going to be a lot of people who don't work for the government that we're going to want to hire to do some pretty secret work.

Yep. So the IDA there in Princeton kind of functioned like the Institute for Advanced Study, which is also in Princeton. That's where Einstein went when he came to America, kind of an independent think tank research group, except it's solely focused on code breaking and signal intelligence with the Russians during the Cold War.

Yeah. It's a pretty wild charter, and especially how special of an organization it was, like. the way these people would spend their time is part code breaking, but part kind of goofing around, because the creativity of mathematicians working together on passion projects is important to discovering clever new algorithms.

Yes. This is so, so key. And this culture ends up getting translated, whole cloth, right into Rentech. So the way IDA worked, and I assume still works to this day, is they recruited top mathematicians and academics to come be code breakers there. They would double their salaries.

And, importantly, it couldn't have been a government division if they were going to be doing that, because there's very specific, congressionally approved budgets for payroll.

Exactly. They figured out that they needed to attract the smartest people in the world, who weren't going to come just go work for the Department of Defense. This was the way to do it. So, like you said, Ben, the charter of the group was that employees had to spend 50% of their time doing code breaking, but the other 50% of the time they were free to do whatever they wanted, like research, pursue whatever they were doing in academia, publish papers, kind of the appeal of going there was, hey, it's the same thing as being a professor at MIT or Princeton or Harvard or whatever, except you're doing code breaking instead of teaching. And there's no bureaucracy to worry about.

It's crazy. 1964, this was published?

Yes. Now, at this point in time, fundamental analysis was then, as in most of the world, today still is, the primary way of investing in things of, hey, I know this company, I'm going to analyze their revenues, their price multiple, or I'm going to think about what's happening in the currency markets or in the commodity markets and why copper is moving here or the British pound is moving there, and I'm going to invest on those insights.

You're effectively looking at the intrinsic value of an asset, trying to assign it a value and make investments based on that.

Yes, fundamental investing. There also existed, in the 60s, technical investing, which kind of is voodoo.

Yeah, using the phrase technical might be a little generous, but what they're looking for, basically, trying to mine trading behavior for signal about the way that it will trade in the future, rather than mining the intrinsic information about an asset for what you think it will do in the future.

And what Jim and his colleagues here are suggesting is that, but just not really done by humans, it's that with a lot more data and a lot more sophisticated signal processing.

And importantly, you might say, why is it this group of people that came to that conclusion of applying computational signal analysis to investing? Well, it's effectively the same thing as code breaking. You are looking for signal in the noise and trying to use computers and algorithms to mine signal from something that otherwise kind of looks random.

Totally. When Jim started working on code breaking, I think he just looked right back to his experience trading in the markets and was like, whoa, this is the same thing.

Which is not an insight other people had. That was the amazing thing about his background, priming him to realize that.

Yes, there's all this noise in this data, and it is impossible for a human to sit here and look at this data and say, oh, I know what the Soviets are saying. No, no, you have to use mathematical models and statistical analysis to extract the patterns.

So mathematical models, statistical analysis. We actually hear a lot of that in the world today, because machine learning is a thing.

Yes, what they are really doing here at IDA and then soon in Rentech, is early machine learning. And Jim just had this incredibly brilliant insight that you can use these techniques and this technology for making investments. Which makes this the perfect time to talk about our presenting sponsor for this season, JP Morgan Payments.

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Yep. So also unsurprisingly, JP Morgan was ranked number one in a recent global banking index of AI capabilities, with Fortune saying they were, quote, head and shoulders above the others. Their customers get AI-powered payment solutions for fraud prevention, customer insights, and treasury insights, all of which grows the bottom line. They can even analyze transaction data to predict and mitigate fraud patterns in real time, with their validation services, helping stop millions of dollars for customers in attempted fraud.

Yep. We were doing some research to prep for this segment, and we came across something pretty wild. The United States Treasury Department has started using AI to detect suspected check fraud and recovered over $375 million in 2023. utilizing the new tools. The U.S.

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Yep. So then the natural question is, okay, what is the model here? How are they going to do this? And it turns out that one of the employees of IDA at this time, and one of the members of this sort of rebel group, shall we say, within the organization, is a guy named Lenny Baum. And Lenny just happens to be the world expert in a mathematical concept called a Markov model.

So the great example that Greg Zuckerman gives in the book is.

Yes, a baseball game.

There's three balls and two strikes. That state has a narrow set of states. after it. It's going to be a strikeout. They're going to get on base.

So we brought up machine learning and AI a minute ago. This is a foundational concept to modern day AI. If you think about large language models and predicting what comes next, it's not like these large language models necessarily understand English. They're just really, really good at predicting states and the next state, i.e. characters and the next character or pixels and the next set of pixels or frame, etc.

And obviously, they're much fancier than that. But that is kind of the underpinning of it all. I mean, I remember in my sophomore year of college computer science class, I had a Markov chain assignment. And it was basically write a Java program to ingest this public domain book. And then I would give it a seed word, you know, the first word of each sentence and press return, return, return, return, return.

Yes, totally. And that is what they were using at IDA to do code breaking. And that's what they propose in this paper that they could use in the stock market, too.

Exactly. And the way that this applies to investing is just like you might not know the rules of baseball. But if you've watched enough baseball, you can kind of guess at what the probabilities of the next thing to happen are based on the state. Investing is kind of the same thing, or at least the stock market movements are where you don't know the future. You don't know what's going to happen.

Yes, exactly. So Jim and Lenny and this whole little crew, they're pretty fired up. They're like, oh great, let's go raise a fund and invest in the markets using this strategy.

Certainly we're going to be successful at raising that fund and certainly we're going to be very profitable because we've got this great idea.

Totally. What could go wrong? Well, in the mid-60s, the idea that some wonky academics at some random, secretive agency in Princeton, New Jersey, could go raise money was non-viable. I mean, it was hard enough for Warren Buffett to raise money at this point in time for his fund, and he was Benjamin Graham's anointed, appointed disciple. And here are these academics who are working at some random, unknown non-profit, saying, give us money.

This was decades before it became high pedigree to come, from a technical computer science background in the world of investing.

Yes. So a bunch of kind of Keystone Cop style fundraising happens here. They're going around in secret. They're trying to keep the IDA bosses from knowing what they're doing. One of the group ends up leaving a copy of the investment prospectus on the copy machine at work one night and the boss discovers it and calls them all into his office and is like, guys, what are you doing here?

It's a little bit of a clown show on the operational side, even if the idea is good.

Yes. So they end up abandoning the effort, both because they can't raise money and because IDA has found out about this, and they're not too pleased. Shortly after all this, though, Jim ends up moving on anyway, because the Vietnam War starts and he, as you can imagine from his background, is not a supporter of the Vietnam War. at this point in time. Jim writes an op-ed in the New York Times denouncing the Vietnam War and saying like, he's sort of part of the Defense Department, but not everybody in the Defense Department is for the war.

Which is so naive, thinking you can write an op-ed in the New York freaking Times and that's not going to create issues for you in your job.

Even more than that, amazingly, nobody really paid attention to it, except a reporter at Newsweek, who then comes to interview Jim and ask him some more questions, and he just doubles down on this. And when the Newsweek piece comes out, that's when the Department of Defense is like, all right, you got to fire this guy.

Yeah.

So he takes pretty much the only halfway decent paying job that he could get, which is to be the chair of the newly established, or maybe re-established, math department at the State University of New York, Stony Brook, which is the Long Island campus of the State University of New York. This is not Harvard. This is not MIT.

No, it is not.

But it did have one very important thing going for it, which is why Jim ended up there. And that is that Nelson Rockefeller, who was then the governor of New York, had launched a campaign, a hundred million dollar campaign, to try and turn this Long Island campus of the State University of New York into a mathematical powerhouse to become the Berkeley of the East. I sort of thought MIT was the Berkeley of the East already, but Rockefeller is waging a campaign that he wants Stony Brook to become a math and sciences powerhouse. And Jim is the key. He wouldn't be able to recruit somebody like Jim otherwise, but because he's now kind of tarnished his career, here's a very talented mathematician that they can convince to come be chair of the department.

Yep.

And amazingly, it works. Jim starts getting a bunch of great talent, including James Axe, who is a superstar in algebra, and number theory from Cornell. And he ends up at Stony Brook recruiting and building one of the best math departments in the world.

Amazing.

Totally amazing. But in true Jim fashion, after a couple of years of this, and also his marriage with Barbara falling apart, he starts getting restless again. He decides that he wants to go on a sabbatical and go back to Berkeley and reunite with his old advisor there and go spend some time out on the coast in California. And this is where Chern and Simons end up collaborating and developing the Chern-Simons theory. that ends up winning the highest award in geometry from the American Mathematical Society, and really kind of is Jim's personal mark on mathematics.

Yep.

Now, also, right around the same time, remember the Columbian Flooring Company? It gets acquired and Jim and his buddies, who are partners in it, come into a good amount of money. And Jim is newly divorced. He's restless in academia. He has these ideas back from when he was an IDA about what you could do in the markets if you had capital.

Yeah. I mean, it was really viewed in the math community as anyone who's going to do investing is throwing away their talent. And it wasn't even that it was common, the way that it sort of is today.

Right. Jim was the first one. But the idea that you would leave to do anything commercial, you're doing a disservice to humanity.

Yes, exactly. And leaving to do anything, sure. But leaving to do investing was almost just seen as dirty, like it's this rich person's game that provides no value to society.

Yeah, I don't think it was that the rest of the math world was skeptical that it could work. They probably were like, oh, yeah, this could work. But they were like, ew.

Academics tend to be much more motivated by prestige than money. So I could totally see this other people being like, oh, I could do that if I wanted. But I have this higher calling and everyone respects me for this higher calling. And my currency is the papers I publish and the awards that I win. And that's what I want.

Now, Stony Brook, we should say, too, like it's a very nice place. Yes. But it's in the middle of Long Island, on the North Shore. This is not the Hamptons. It's like the Long Island suburbs.

Yep. The wooded Long Island suburbs.

Yes, the wooded Long Island suburbs. Here's Jim, in a strip mall, next to a pizza joint, setting up his trading operation that he decides very cleverly to call monometrics, a combination of money and metrics, or econometrics. And he recruits his old IDA buddy, original partnering crime on the trading idea, Lenny Baum, to come and join him. And this time, though, they have some capital from the sale of the flooring company.

And how much did he make on that flooring sale?

I think, together with Jim, his partners and whatever money Lenny put in, they had a little less than four million dollars in this initial capital.

In 1978.

Yep. Now, Jim also has another advantage at this point in time, which is he's right down the street from Stony Brook and he's just recruited all of these superstar mathematicians.

The table has been set.

Yes. And those folks are more loyal to Jim than they are to Stony Brook.

But they're more loyal right now to academia than they are to finance. This is not a paved pathway until Jim paves this pathway.

Yes, in general, but some of them, and in particular, the superstar James Axe, Jim convinces to come join him in his trading operations.

So, having Baum and Axe and Simons, it's like suddenly this extremely credible team in the math world.

Yes, beyond credible.

Right. All the theorems that a lot of mathematicians are using every day are all named after these three guys who are now at the same firm trading.

Yes. And it's led by Jim, who's somebody that they respect as an academic, but, even more important, is somebody they want to work for and they look up to and they think is cool. He's out there being like, hey, I think we can make money.

And it seemed to me like a lot of the trading of currencies they were doing was basically based on feelings that they had around how a central bank was acting, like if the head of state of a certain country was going to do something or not. It's basically like betting on how one single actor who was in control of currencies at governments would act. So, to your point about very few signals impacting price, it's knowing what one person is going to do.

Yes. And this is super important. At the end of the day, they build some models there. They're getting the early versions and infrastructure and scaffolding of this quantitative approach set up. But in terms of the actual trades they're putting on, they're still doing all of it by hand, and they're still all really going on a fundamental type analysis.

The computers are by no means running loose at this point.

By no means at all. Yeah. They're just suggesting patterns and ideas, and Jim and Lenny and James, they have to then decide, hey, are we going to do this or not? Or are we going to do something just totally different? that we think is what's going to happen?

Fascinating, which is very different than we'll cram a huge amount of data in and then, whatever the data suggests, we know it's true because the data suggests it, which is sort of where they would end up many years later, once they had both the hardware you're referring to, sophisticated computers, the clean data that would be required to make all of those incredibly numerous and fast calculations, and also the real computer engineering architecture to build these scale systems, to actually act on large amounts of signals and understand them all to come up with results. They just didn't have any of that at the time. So it was hunches and chalkboards.

Yes, and so much so that even Jim is ringleader here. He's far from convinced that he should put all of his wealth into this thing. He's like, oh, yeah, this is interesting. We're building. We're experimenting like great, but I also want to put my money somewhere else too, for some diversification.

Which was essentially spun out of Renaissance. Like it was kind of the venture capital work that they were doing at Renaissance that didn't fit with the rest of Renaissance.

Yes. So here's how it all went down. And this is so poorly understood out there.

Yes.

Howard was a computer science and business school professor at the University of Pennsylvania. So he taught CS at Penn and business at Wharton. And he had been involved in bringing Arpanet to Penn and was kind of like early, early internet pioneer. And so as a result, he was super plugged into tech and early startups and really early, early proto internet stuff. And Jim gets excited about investing together with Howard.

And again, trading on currencies and commodities at this point.

And Howard's going to bring in private company technology investing. And they pick a name for a firm that is going to reflect this, Renaissance Technologies. It's crazy. And that is why Rentech is called Rentech.

I could not, when we figured this out in the research, I could not believe that this is not a more widely understood story. That this is the origins of what is today a fantastic venture capital firm, first round capital. But you could not name two more different strategies. in investing. I mean, a long term illiquid thing like venture capital, highly speculative versus, you know, we're going to trade.

Totally. But when you know the whole background in history, it kind of makes sense, because this is their personal money. This is Jim and his buddies, and Lenny and James and Howard. There's not institutional capital here. They're not out pitching LPs of like, oh, you should invest in my diversified strategy of currency trading and private technology startups.

When they say multi-strategy, this is really multi-strategy.

Yeah, we'll get into what multi-strategy today means later. But in these early days of Rentech, 50% of the portfolio was venture capital and 50% was currency trading. And in fact, a couple of years after they get started, the currency trading side of the firm almost blows up. when Lenny goes super long on government bonds and the market goes against him and the whole portfolio drops 40%, which is wild. That ends up triggering a clause in Lenny's agreement with Jim and they sell off Lenny's entire portfolio and he leaves the firm.

And because we quickly got to this point in the story, it would be easy to say, well, that's a clause that has a lot of teeth. There were many sort of rumbles of something like this potentially happening. Simon's going to Lenny and saying, hey, maybe we should cut some of our losses and it's okay to trade out of these positions. And Lenny was just very dug in on, I'm a true believer. And that's how you can get into a situation where you trigger a covenant like this.

Totally. And again, also shows they weren't doing model-based quantitative trading really at this point in time.

Now, so much gut.

So, as a result of that, for a while, Rentech is truly almost entirely a venture capital firm. At one point on the venture side, just one investment, Franklin Dictionaries. Do you remember Ben, the Franklin Electronic Dictionaries? Yeah, that was one of their biggest investments. That one investment is half of Jim's net worth.

What?

At this low point for the trading side. Yes.

I had no idea. That's crazy.

Yeah. So in the book, Greg talks about, oh, Jim was focused on venture capital, and that's kind of the story out there. It's like, well, he was focused on venture capital because that was the only thing worth it and making money.

Well, I mean, it's the only thing where they actually had an edge from Howard's access to deal flow, because they certainly didn't have an edge in the global currency markets.

So I think, perhaps in part because of the trading losses, James Axe starts to get a little disillusioned too. And he tells Jim that he wants to move out to California with Sandor Strauss, who started working with them at this point. Sandor was another Stony Brook alum that joined them. And the two of them want to move out to California and do trading out there. Jim says, sure, fine.

So it's this interesting arm's length thing where Jim strikes a deal where he's going to own a part of Axe.

It's another company that is now doing the quantitative trading.

Yep. And I think Jim owned a quarter of it. Is that right?

Yes, that's right.

And importantly, I don't think anyone had any idea what Axe.

No, nobody would have done what they did had they known what was coming.

Yes. Wouldn't have spun it out.

No. So once Axe and Strauss get out to California, Strauss, he's kind of on the computing data infrastructure side. That's what he was doing at Stony Brook, and that's what he came into Renaissance to build. He starts getting really into data, and he starts collecting intraday pricing movements on securities. At this point in time, I think really the best data you could get from providers out there was maybe open and close data on securities pricing.

Not only that, he's getting historical data that predates what your traditional data providers would give you, and then ingesting it into computers and cleaning the data to get it into the same format as the tick data. So he's getting early 1900s, even 1800s stuff, to try to just say, at some point, hopefully we'll be able to make use of this, and I want to have this just really, really clean data set about the way that these markets interact.

I mean, he's doing ETL on the data. Yes. I think before anybody knew what ETL was.

Again, no one told him to do that. That was just a self-motivated, almost like obsession of like, well, if we're going to have data, it should be well formatted and well understood and labeled, and all that.

So that's one thing that happens. The other thing is Jim says, oh, you're going out to California. Let me hook you up with my buddy, who's a Berkeley professor out there, Elwin Berlekamp. And Berlekamp had studied with folks like John Nash and Claude Shannon at MIT.

I love that Claude Shannon is coming in again. I know. We talked about a lot on the Qualcomm episode, father of information theory, really the center of gravity for attracting tons of talent to MIT and kind of paving the way for what would become phone technology and telecommunications broadly in the future. But the fact that Berlekamp is crossing paths at MIT with Claude Shannon, so cool.

So cool. And most importantly for this specific use case, Berlekamp had worked with John Kelly, who developed the Kelly criterion on bet sizing, which poker players will likely be well familiar with.

Yep.

So, with this combination now of much, much, much better and deeper data from Strauss and Berlekamp coming in and working with Axe on the models and saying, hey, we should be smart about the bet sizing that we're doing in the trades that are coming out of these models versus I don't know what they were doing before. Maybe it was naive of like every trade was the same, or just like we should actually be systematic about this. The models start really working.

Yep. This is the turning point.

Yeah. In these kind of mid 80s years, Axecom is generating IRRs of like 20 plus percent on the trading side. You know, not necessarily going to beat venture capital IRRs, but liquid. Yes. Reliable.

Well, that's the thing. They don't know how reliable yet. They know they've done it kind of a few years in a row here. But the question is how uncorrelated to the stock market over a long period of time and how predictable are these returns, or is it just super high variance?

Yes. But the early results are really good and Jim and Berlekamp especially are very encouraged by this. So in 1988, Jim and Howard Morgan decide to spin out the venture investments and Howard goes to manage those with basically their own money. Fun coda on this. When Howard starts first round, a number of years later with Josh Koppelman, Jim, of course, is a large LP.

That's wild.

Isn't that amazing?

Wow. That is a untold story about Jim Simons. I think I read basically every primary source thing on Jim or Renaissance on the whole internet, but I assume you got that from Howard.

Yeah, it was super fun talking to Howard about this and just the history of how first round started and early super angel investing and everything that became.

I also didn't realize that first round's fund one was a 50x on $125 million fund.

First institutional fund, which I believe they called fund two.

I mean, wild, wild stuff.

Totally wild. So when Howard spins out the venture activities, Jim then decides to set up a new fund as a joint venture between Rentech and Axecom, and they decide to name it. after all of the collective mathematical awards that Jim and James and Berlekamp and all these prestigious mathematicians have won in their careers. They name it the Medallion Fund.

And listeners, we've arrived. This is the part of the story that matters. The Medallion Fund is the crown jewel, or you might even say actually the only interesting thing about Renaissance, and it is born out of this observation that, oh my God, what they're doing over there at Axecom is really interesting. Maybe they shouldn't be doing it all the way over there. Maybe that should be a deeper part of the fold here at Rentech, and we shouldn't have let that get away or frankly given up on the quantitative trading strategies too early.

Those ideas had all existed before. This is the first time that it's all brought together and actually working and operationalized.

And, frankly, that computers got good enough to actually do it too. That's another big piece of this.

I don't know that Strauss could have done his data engineering too much earlier in time. Yeah. But before we get into the just absolutely insane run that this Medallion Fund is about to go on, that continues right through to this day, now is the perfect time for another story about ServiceNow. ServiceNow is one of our big partners here in Season 14 and is just an incredible company.

Yep. ServiceNow digitally transforms your enterprise, helping automate processes, improve service delivery, and increase operational efficiency, all in one intelligent platform. Over 85% of the Fortune 500 runs on them, and they have quickly joined the Microsofts and the NVIDIAS as one of the most important enterprise software companies in the world today.

So we talked on our Novo Nordisk episode about how ServiceNow founder Fred Luddy discovered this core insight that software can transform and eliminate manual tasks. And on Hermes, we told the story of how current CEO Bill McDermott came in and turbocharged that into an absolute monster $150 billion market cap global behemoth. The key thread that connects those two eras is that from day one, Fred knew the ServiceNow platform could be used across the whole enterprise. But at the same time, he also knew from his decades of prior software experience that launching a broad horizontal offering right out of the gate as a startup was a recipe for failure. You need to start with a specific vertical use case.

And that's been true for us here on Acquired too. David, if we didn't name it Acquired and cover technology acquisitions that actually went well, we never could have broadened and become the podcast that tells the stories of great companies. You can't just start as that.

Totally.

Well, this is what's so cool and where I think the playbook lesson really is for listeners. Because you can't just pick any use case, you have to be strategic about it. And IT was the perfect vertical because every other department has to interface with them, from the CEO on down. So they're going to notice when IT service management rapidly improves. All of those support tickets that used to take forever are now just magically resolved.

Once those other departments do pull the trigger on joining the ServiceNow platform, who is in charge of rolling it out for them? Of course, it's IT, who are already true ServiceNow believers. I'm honestly not sure that there's a better enterprise software playbook in history than ServiceNows. So once they established the beachhead in IT, they then took the same platform to HR with employee experience. They took it to CSM with customer service requests.

So, if you want to learn more about the ServiceNow platform and playbook and hear how it can transform your business, head on over to ServiceNow.

So they've got this grand new plan and vision with the Medallion Fund.

It's that and it's two other things. One is the further into the future you look, the less certain you can be about it. If you know something is worth $10 right now, what you know five minutes from now is it's probably going to be worth about $10.. The most likely situation is it's within 5% of that. If you ask me three years from now, I have almost no intuition about that.

There is a great Bob Mercer quote about this. later. He says, we're right 50.75% of the time.

And I do think he's making up that number. I think it's illustrative.

Right. But we're 100% right. 50.75% of the time. You can make billions that way.

It's so true. When you have that little edge, it's about making sure that you're not betting so much that a few bets that don't break your way can take you down to zero, and to make sure you can just play the game a lot.

A lot. Yes. And then back to the Kelly criterion, adjust your bet sizes over time as you're making those bets.

Yep. Now, of course, this is all great in the abstract. if it's that you're literally sitting at a casino and you're somehow perfectly making these bets and you're just sitting right there at the table and then you can walk over to the cashier. It gets a little bit different. in the market.

This is slippage.

There's all sorts of practical consideration. You could get front run by other people. It's not just a computer program that gets executed. You actually have to meet the constraints of the real world when you're deciding. instead of a few big bets, we're going to have 100,000 tiny bets.

Yes. And as time goes on and the whole quant industry emerges and becomes much more sophisticated, I think it's particularly the slippage there that becomes the governor on how high velocity you can actually be on this. And the slippage is that once you are at a certain scale, you are going to move the market with your trades.

So the deeper you get into the order book, like, let's say, you want to buy $5 million of something, maybe your first $100,000, you're pretty sure you can get the quoted price. But by your last $100,000 of that $5 million buy, the price might have gotten pretty different already.

Yeah. We're going to come back to this in just a minute. But this certainly for early Rentech and then even now still for all of quantitative finance, is a really, really, really important thing.

And David, in a very crude way, calls back to last episode on Hermes, the idea that the price would be highest for the family member that is willing to sell now and sort of goes down over time. If the family was going to sell to Bernard Arnault, it would behoove you to be first in the order book, not last in the order book. Yes.

I feel like there's this metal lesson that I've been learning through Acquired and my own personal investing over the past couple of years. Every market is dependent on supply and demand. You can see quoted valuations and quoted price streams, but oftentimes that's like the mistake of just looking at averages.

Exactly. Yes. Looking at the quoted price of an asset is wrong. You actually should be looking at what is the volume that is willing to buy and what is the volume that is willing to sell. And for all of those buyers and all of those sellers, what are the price at which they are willing to transact?

Yes. Now, at this point in time, when the Medallion Fund is first starting to work in, say, late 1989, early 1990, it's small enough that this isn't a big consideration yet.

Right.

Medallion was about $27 million under management. when Burley Camp bought out Axe. In 1990, the first full year after that, the fund gains 77.8% gross, which, after fees and carry, was 55% net. Now, what were the fees and carry?

I mean, either. one of those numbers is shooting the freaking lights out. Assuming that this is not a crazy high risk strategy that they executed and will completely fall apart under different market conditions, like if this is an actual repeatable strategy that produces the numbers you just said, unbelievable, world changing.

Hell yeah, let's go. And indeed, it was a hell yeah, let's go situation.

So the numbers. you quoted me, the gross and the net, sounded quite different. Talk to me about the fees and carry.

So carry, I've seen different sources of whether it was 20% or 25% in the early days, but the management fee on the fund was 5%, which is crazy. The top venture capital firms in the world charge a 3% management fee, and even that is like, everybody holds their nose and is like, this is ridiculous. How on earth were these nobodies charging a 5% management fee out the gate to their investors? Well, a couple things. One, their investors were not sophisticated.

So they set that precedent.

They set that precedent. But two, though, they actually needed the money, because Strauss's infrastructure costs were about $800,000 a year. So they just backed into the management fee based on like, hey, we need $800,000 a year to run the infrastructure. Plus, we need some money to pay folks, and whatnot. Like, great, 5% management fee.

And so the pitch they're making to the investor base is like, if you believe that we should be able to massively outperform the market doing quantitative trading, well, we're going to need a lot of fees to do that. And so the investors basically took the deal, if they thought about it enough. Okay, so that's the fees. On the performance, that 20% or 25%, it's just not actually that far above market, if it's above market at all. What you're seeing is a high fee, normal-ish performance fee fund at this point in time.

High management fee, normal-ish carrier performance element. Yep. So at the end of 1990, Simons is so jazzed about what's going on that he tells Berlikamp, hey, you should move here to Long Island. Let's recentralize everything here. I want to go all in on this.

On the one hand, making a 6x in one year sounds great.

On the other hand, this is the equivalent of when Don Valentine sold Sequoia's Apple stake before the IPO to lock in a great game, but miss out on all the upside to come.

David, I think we should throw this out so people understand the volume of this. They've generated on the order of $60 billion of performance fees for the owners of the fund over their entire lifetime. So, on the one hand, 6x in a year ain't bad. On the other hand, you owned a giant part of something that has dividended $60 billion in cash out to its owners. Oof.

Yeah. That's just on the carry side of it. The owners are the principals. So, just like dollars out of the firm, it's probably twice that. I would estimate, probably $150, $200 billion that have come out of Medallion over the last 35 years.

So it's now the Jim Simons show in New York, with Strauss building the engineering systems and Axe, I think, still had a small stake.

Yes, that's right. And Strauss had a stake as well. So once Jim takes control and moves everything back, he basically decides that he's going to turn Rentec into an even better, even more idealized version of IDA and the math department at Stony Brook. He's going to make this an academics paradise where, if you are one of the absolute smartest mathematicians or systems engineers in the world, this is where you want to be. So, of course, he starts raiding the Stony Brook department itself again.

And listeners, I imagine this is probably the point where you're starting to get confused and saying there are so many people in this story. I think we're on eight or nine. We just keep introducing more people. And that is the story of Renaissance. It is not this singular, clean narrative.

A lot of people and just a lot of time too. This is 25 years. This is a quarter century from the time that Baum and Simons write the paper at IDA. until Medallion really starts to work. It takes a long time.

And we haven't even introduced the two people who would become the co-CEOs of this company for 20 years.

Yes. Well, let's get to that. So Jim moves everything back to Long Island, sets it up as this academic paradise, is recruiting the smartest people in the world. In 1991, the next year, the firm does 54.3% gross returns and 39.4% net returns after fees. So not Jim's bogey of 80%, but still pretty freaking great.

And we should say the years of modest performance are behind them. From every single year forward, they shoot the lights out. From 1990 onward, they never lose money. And on a gross basis, they never even do less than 30%. It's working.

The historic run has begun, let's just say. Yep. So 1992, gross returns are 47%, 93%, they're 54%. At the end of 1993, Simons decides to close the fund and not allow new LPs in. So if you're an existing LP, you can stay in, but they're no longer open for new inflows.

And the performance fees on this are $7 million, $13 million, $52 million. The free cash flow flowing to partners here is certainly becoming real too.

Yes, but as they get into that, call it on the order of magnitude of a billion dollar scale, they start bumping into the moving markets problem and the slippage that we were talking about earlier.

And that's sort of in the mid nineties.

Yep. As they're hitting this $250 million, half a billion dollar scale.

Right. The computer model spits out, we should go buy this huge amount of something at this price. They go to do it. They can only buy 10,, 20, 30 percent of the amount they want at that price. And then suddenly the price is very different.

Yep. Up to this point, the vast majority of what Medallion is doing is trading currencies and commodities, not equities. Because you might be thinking, okay, yeah, I hear you. The nineties was a different era, but half a billion dollar fund doesn't sound that big. How are they moving markets with half a billion dollars?

It's not the equity markets.

It's because they're in these thinner markets. It's not that commodities and futures are small markets. They're large, but they're thin compared to equities. There's just not that much volume and you just can't trade that much without slippage becoming a huge issue. And Medallion is now hitting that limit.

There's so many buyers and sellers every day showing up to trade, so many different companies at such high velocity. It's almost this honeypot for Renaissance's systems. This is sort of their moment. This is what they were built for. And it's kind of funny that they've just been in kid glove land the whole time with these thinly traded markets with minimal data.

And this brings us to Peter Brown and Bob Mercer. And in 1993, one of the mathematicians that Jim had recruited to Rentec, a guy named Nick Patterson, gets especially passionate about going out and recruiting new talent along with Jim. And this is, I think, one of the keys to Rentec and the culture there. People want other smart people to come be there too. Nick's sitting there like, this is a joy.

Yep. And it's interesting that you talk about speech recognition as the perfect fit for what they were doing. And you might say, why is that? Well, the actual work that goes into speech recognition, natural language processing, is kind of the same signal processing that Renaissance is doing to analyze the market.

It's not just kind of, it's exactly the same signal processing.

Right. Speech recognition is a hidden Markov process where the computer that's listening to the sounds to try to turn it into language doesn't actually know English, right? Obviously. But what it does know is, when I hear this set of frequencies and tonalities and sounds, there's a limited set of likely things that could come after it. And in Greg's book, he greatly points out this perfect example.

Yes, at operational scale. And this is what's so important and why the two of them become probably the most critical hires in Rentex history, even including all the great academics that came before them. Because they're good on the math side, but they have this large systems experience. And Jim and Nick know that if they're going to move into equities, because of the volume of data and because of how much more complex that market is, they need more complex systems. And the current talent at Rentex coming from academia has just never experienced that or built anything like it.

And the world that they're entering is just exploding in complexity and dimensionality. And when I say that, here's what I mean. The data that they are mining, that they're looking for, is this intraday tick data between every stock trading. So they're in this sort of trying to map the relationship between one stock and every other stock, not just at that moment in time, but every time before and every time after it. They're also, once they do, identify patterns, which this is key, the algorithms identify the patterns.

And the universe of equities is so much more multidimensional and interrelated. There are only so many currencies in the world. And there are especially only so many currencies that are large enough trading markets that you can operate in. There's not infinite, but thousands and thousands of equities in the world that are deep enough markets that you can operate in. And to some degree, they're all correlated with one another.

And just keep adding layers of complexity here. Keep adding new things to multiply by. Many of these are traded on multiple exchanges. So you might also be looking for pricing disparities on the same equity on different markets at different points in time. So there's just dimensions upon dimensions of things to analyze, correlate, and act upon.

So Patterson and Simons go raid IBM. They're like Steve Jobs raiding Xerox PARC. They bring Peter and Bob and one of their programming colleagues, David Magerman, over from IBM into Rentech. And they get started on building the equities model. But it turns out, A, they're obviously very successful at that.

But if you can nail it, it means that you can do interesting things like, hey, we don't have a lot of data on this particular market, but it looks a lot like something we do have data on. So if it's all part of the same model, we can just apply all the learnings from this other thing onto this brand new thing that we're looking at with little data for the first time. And because we're putting it all in one model and no one else in the world is, we can discover patterns that no one else knows about.

It turns out that this was actually the second most important innovation that Bob and Peter bring to Rentech, the actual product and performance of having one model. The most important thing is that if you have only one model, one infrastructure, everybody in the firm is working on that same model. You can all collaborate all together, which is especially important when you have the smartest people in the entire world all in one building. Before this, there were separate models within Rentech. So insights and innovations and work that one team was doing on one model wouldn't get applied or translate over to work that was happening by another team on another model.

They did have the cultural element where it was encouraged that you share your learnings, but someone would have to take the time during their lunch break and go learn from you about those and then implement it in their version. There's a lag and it may actually not get implemented.

Yeah, this is wholly unique and revolutionary. No other at-scale investment firm, period, and especially quant firm, operates this way today, with just one model. There are portfolio managers and teams and multi-strategy people are culturally competitive with one another. But even if they're not, the work that you're doing on this side of Citadel is not impacting the work that you're doing on that side of Citadel.

Right.

What Bob and Peter do is they unify everything at Rentech. So all the wood is going behind one arrow.

Yes. And before we talk about the impact of that, we want to thank our long-time friend of the show, Vanta, the leading trust management platform. Vanta, of course, automates your security reviews and compliance efforts. So frameworks like SOC 2, ISO 2701, GDPR, and HIPAA compliance and monitoring, which is quite topical if you are in the heavily regulated finance industry and you need a lot of security and compliance. Vanta takes care of these otherwise incredibly time and resource draining efforts for your organization and makes them fast and simple.

Vanta is the perfect example of the quote that we talk about all the time here on Acquired, Jeff Bezos, his idea that a company should only focus on what actually makes your beer taste better, i.

Vanta takes care of all of it for you. No more spreadsheets, no fragmented tools, no manual reviews to cobble together your security and compliance requirements. It is one single software pane of glass, just like one model, that connects to all of your services via APIs and eliminates countless hours of work for your organization. There are now AI capabilities to make this even more powerful, and they even integrate with over 300 external tools. Plus, they let customers build private integrations with their internal systems.

And perhaps most importantly, your security reviews are now real-time instead of static, so you can monitor and share with your customers and partners to give them added confidence.

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Yes, and indeed a machine it is. So Peter and Bob come in in 1993 and 1994, 1995, they're building this. Rentec is getting into equities.

And yeah, just imagine the computers that you were using during 1994 and 1995.

And here's what's amazing. Returns go down, maybe slightly, certainly, a bit from the blowout year that 1994 was, but they're still above 30% every single year. Most years above 40%. This is unbelievable. that they're maintaining this performance as they're going into this hugely more complex market and they're scaling assets under management.

And David, if you just kind of look at this and do the math, okay, so 94,. their AUM was 276 million and they grew 93%. And then their AUM the next year was 462 million and then they grew 52%. And their AUM the next year was 637 million. You kind of quickly get where I'm going here, which is, oh, they're scaling AUM, not by bringing in new investors.

It's closed to new investors.

It's all just compounding. This is the same capital that they had in 1993. that has gone from 122 million at the beginning of that year to 1999, being 1.5 billion.

Yes. And then in the year 2000, they just totally blow the doors off.

They grow the fund from 1.

Yes. While the whole rest of the market is down big time, Medallion is up 128% gross on the year. And this becomes a theme. High volatility is when Medallion really shines.

And here you go, uncorrelated. They have their final stamp of approval right here of not only are we a money printing machine, we are a money printing machine in all environments, regardless of the state of the broad market. And, David, as you said, volatility actually makes their algorithms work even better. because what are they doing? They're looking for scenarios where the market's going to act erratically and they can take advantage of people making decisions that they shouldn't.

So there's a really fun story around this. that really illustrates Jim's genius in managing the firm and the people, and how this year was when they really figured this out. So the first couple days of the tech bubble, bursting, Medallion actually takes a bunch of large losses. And part of it might be that the model wasn't tuned right yet, because nobody at Rentec had seen this type of behavior in the market before. Part of it might also be, too, that it didn't perform well for those couple days.

Yep.

After this goes down, Peter Brown comes to Jim and offers to resign, given the losses that they incurred over these couple days. And Jim says, what are you talking about? Of course, you shouldn't resign. You are way more valuable to the firm now that you've lived through this and you now know not to 100% trust the model in all situations.

Fascinating. It's such a good insight. That illustrates Jim as a leader right there.

It totally does. There's a parallel story, when Jim ultimately does retire in 2009 and Peter and Bob take over as co-CEOs, where a year or so before the quote unquote quantquake had happened, where, similar to the tech bubble bursting, there was all of a sudden very large drawdowns among all quantitative firms in the market and Rentech gets hit. And during that period, Peter argued very strenuously that we should trust the model, stay risk on, this is going to be an incredibly profitable time for us. And Jim pumped the brakes and stepped in, intervened and took risk off. And Peter goes to Jim again around the CEO transition and says, hey, Jim, aren't you worried that with me running the place now, I'm going to be too aggressive and blow it up one of these days?

It is so true though. I even find this about myself that I will naturally take the position of the foil to the person across from me. So if somebody is being pushy in some way, I'll find myself taking a position where, if I pause and reflect, I'm like, I don't think I expected to take this position coming into this conversation. But you naturally want to play the other side to balance out the person sitting across from you.

So back to the year 2000 and this incredible performance. Then to what you were saying earlier about uncorrelated returns, not only did they shoot the lights out that year, they're doing it when the market is down. We got to introduce this concept of a sharp ratio now, which for all of you listeners that are in the finance world, you'll know this, but for everybody else, this is a really important concept.

And I think people grasp it intuitively. We've mentioned this concept a couple of times. this episode where, okay, great. It's amazing to have a fund that 25Xs or a year where you have 100% investment return, or I bought Bitcoin yesterday and it doubled overnight. Does that make you one of the best investors in the world?

Yes. And so the Sharpe ratio is a measurement combining these two concepts.

Exactly. So. it's named after the economist William F. Sharpe. It was pioneered in 1966..

Good.

1995 to 2000, Sharpe ratio of 2.

Good. Where do I sign up to invest?

At some point, they added foreign markets and achieved a Sharpe ratio of 6.

And I believe, if I have my research right, in 2004, they actually achieved a Sharpe ratio of 7.

Astonishing.

You know, again, back to our sports analogy here, these aren't Hall of Fame numbers. These are like, I don't know, make Tom Brady look like a third stringer.

Yes, exactly.

So, on the back of 2000 and this rise, the next year, in 2001,, they raise the carried interest on the fund to 36%, up from either 20% or 25%, whatever it was before. Now, remember, they've already closed the fund to new investors. So they're still outside investors in the fund, but no new investors are coming in. And then the next year, in 2002,, they raise the carry to 44%.

There's two interesting ways to look at this. One, they're just trying to jack it up so high that they just purge their existing investors out, where they're saying, we're not going to kick anyone out yet, but we've been closed to new business for a long time now. You should see yourself out at some point. The other way to look at this, which I think is probably the right way to look at it, is

You should pay 20% carry for a firm that delivers you 15% annual returns. We're delivering you 50% annual returns.

Totally. So. I have to imagine it didn't go over well with the existing investors, but they just have so much leverage that what's going to happen?

Okay. Once again, I'm sorry, audience. I have to say, hold on one more minute for another perspective that I have to offer on the carry element, but I want to finish the story first. Okay. So 2001, they raised the carry to 36%.

And not all alumni get to stay. There's select alumni that get grandfathered in.

Yes. Now, why did we do this? I'm going to talk about one reason in a minute, but one reason is super obvious. The Medallion Fund is now at $5 billion in assets under management that they're trading. Even in the equities market, they are now hitting up against slippage.

Yep.

And so if they want to maintain this crazy, crazy performance, they just can't get that much bigger.

This is the problem that Warren Buffett talks about all the time and why he has to basically just increase his position in Apple rather than going and buying the next great family owned business. The things that move the needle for them are so big that that's really all they can do. And when you are big, you're going to move any market that you enter into.

Yep.

And the strategy that rent tech is employing right now, they're just deeming doesn't work at north of $5 billion.

So in 2003, they start kicking all the outside investors out of Medallion, but clearly there's still lots of institutional demand to invest with Renaissance. So what do they do?

Well, time to start another fund. So they start the Renaissance Institutional Equities Fund. And there's a couple of things to add a little bit of context to really why they decide to do this. Well, the first one is sometimes there's just more profitable strategies than they had the capital to take advantage of in Medallion, but they weren't sure it would be on a durable basis. If they were sure that they could manage $10, $15, $20, $25 billion in Medallion all the time, then they would grow to that.

Yeah. There's a fun story around this that Peter Brown tells of. Jim came into his office one day and said, Peter, I got a thought exercise for you. If you married a Rockefeller, would you advise the family that they should invest a large portion of their wealth in the S&P 500?? And Peter says, no, of course not.

And these guys are very used to sharp ratios that are far better than the S&P.

Right. And so Jim says, yes, exactly. Now get to work on designing the product that they should invest in.

Right. And so that's basically what they come up with is, can we create something that's like an S&P 500 with a higher sharp ratio? Can we beat the market by a few percentage points or, frankly, even match the market each year with lower volatility than if they were buying an index fund? And you can see who? this would be very attractive to pensions, large institutions, firms that want to compound at market or slightly above market rate, but don't want to risk these massive drawdowns or, frankly, just big volatility in general, should they need to pull the capital earlier.

And the marketing is built in. It's not like there's any lack of demand of outside capital that wants to invest with Rentech.

Right. It's really funny. There's all these stories about how the marketing documents literally say, this is not the medallion fund. We don't promise returns like the medallion fund. In fact, we're not charging for it like the medallion fund.

Clearly, this is a very different product.

But people did not perceive that. People were very excited. It's a Renaissance product. It's the same analysts. They're using all their fancy computers.

Yeah, that has not performed anywhere near how medallion has performed.

Correct. Has it served its purpose?

Yeah.

But is it medallion? No, it's not special in the way the medallion is special.

Yes.

A couple other funny things on the institutional fund. So I spent a bunch of time scrolling through 13 Fs over the last decade from the medallion filings. I think they have two institutional funds.

Yeah, there's institutional equities and diversified alpha.

So the funniest thing is they file these 13 Fs. And David and I are very used to looking at 13 Fs of friends of the show who run hedge funds, who we've had on as guests, or perhaps really just any investor, where you want to see like, or what are they buying and selling this quarter? And usually you see 15,, 25, maybe 50 different names on there. Well, the 13 F for Renaissance has 4,300 stocks in these tiny little chunks. And there's a little bit of persistence quarter to quarter.

Hey, they've been listening to a choir.

That's right.

That's one of the signals in the model. Yeah.

You kind of get the sense, from looking at these filings, that these things were flying all over the place. And this was just the moment in time where they decided to take a snapshot and put it on a piece of paper. And, even though this is the end of quarter filing, of what their ownership was, if you had taken it a day or a week earlier, it could look completely different.

Yes. The way that some folks we talked to described the difference between the institutional funds and Medallion to us is that Medallion's average hold time for their trades and positions is call it like a day, maybe a day and a half. Whereas the average hold time for the institutional funds positions is like a couple months. So, across 4,300 stocks in the portfolio, there's a lot of trading activity that happens on any given day, but it's a lot slower in any given name than Medallion would be. Which makes sense.

Yep. And frankly, it just looks a lot like the S&P 500.. Like when you look at as of November 23,. so 11 of the 12 months of the year had happened, they were up 8.6%. Okay.

Zero percent chance. Speaking of the fund. that is the reason why we are covering this company on this show, we set up during the tech bubble crash. that volatility is when Medallion really shines. Well, there's no more volatile periods than 2007 and 2008..

Yep.

2007, Medallion does 136% gross. 2008, Medallion does 152% gross. Like, get out of here. This is 2008, while the rest of the financial world is melting down.

And so this really does illustrate where do they make their money from, who is on the other side of these trades? It's people acting emotionally. They have effectively these really robust models that are highly unemotional, that are making these super intricate multi-security bets, and they are putting on exactly the right set of trades to achieve the risk and exposure that the system wants them to have. And who is on the other side of those trades? It's panic sellers.

Yes. Well, there's one slight nuance to that, but I don't know how much it holds water. And the apologist nuance would be, well, Warren Buffett could be on the other side of the trade, and Medallion could make money on that trade with Warren over its time horizon of a day and a half, and Warren could make money over his time horizon of 50 years.

Super fair.

So I think the argument against that, though, is that Medallion sold after a day and a half to somebody else who bought at that lower price. And so, somewhere along the chain, that loss is getting offloaded to somebody. The direct counterparty of Medallion and the quant industry, writ large, might not take the loss, but somebody is going to take the loss along the way. It is, as you say, a zero-sum game.

But I think the important thing is, can you and your adversary both benefit? And I think in this case, you and your counterparty, the person you're trading against, yes, you have two different objective outcomes. Like, can I get a penny over on Warren Buffett by managing to take him on this one trade? But his strategy is such that that is irrelevant.

So, after the historic performance during the financial crisis, as I alluded to earlier, Jim retires at the end of 2009, and Peter and Bob become co-CEOs, co-heads of the firm in 2010.

They gained confidence that they had enough profitable trades they could make, that they could raise the capacity without dampening returns.

Yes.

And perhaps they could have done it earlier, and they just didn't have the confidence that it would work at larger size. But I bet they're very good at knowing how large can our strategy work up to before it starts having diminishing returns.

And, importantly, during periods of peak volatility, like, say, 2020, Medallion continues to shoot the lights out. So, from at least the data that we were able to find on Medallion's performance over the past few years, 2020, they were up 149% gross and 76% net. So the magic is still there. And one way to look at it, which may not be the be-all and end-all, but I think is a good way to compare Jim's era at Medallion versus Peter and Bob's era. During Jim's tenure, Medallion's total aggregate IRR from 1988, when the fund was formed, to 2009, when he retired, was 63.5% gross annual returns and 40.1% net annual returns, which of course did include many periods of lower carry, 20% versus the 44%.

It's amazing. And we weren't able to tell, there's some sources that report that they've grown from $10 billion in the last few years to being comfortable at a $15 billion fund size. And if so, that just means that they continue to find more profitable strategies within Medallion to keep those same unbelievable returns at larger sizes.

Yeah. And at the end of the day, this is all just insane. So, as far as we can tell, Ben, you alluded to this a bit at the beginning of the episode, and as far as anybody else can tell, Medallion has by far the best investing track record of any single investment vehicle in history.

So give me those net numbers.

So during the entire lifetime so far of Medallion from 1988 to 2022, that's 34 years, the total net annual return number is 40%, 4-0 over 34 years. after fees. It's 68% before fees, which equates to total lifetime. carry dollars for the whole firm of $60 billion, Justin Carey, by our calculations.

Astonishing.

That is a lot of money.

Also, David Rosenthal, good spreadsheet. work on this. You have not done a spreadsheet for an episode in a while, so I admire your work on this one.

Yeah, I still know how to use Excel.

That's right. Okay, so $60 billion in total, carry.

So, $60 billion in total carry is a lot of money. And, well, speaking of a lot of money, we do need to mention, before we finish the story here, that that Rentech money has bought a lot of influence in society. So Bob Mercer, that name may have sounded familiar to many of you along the way, Bob was the primary funder of Breitbart and Cambridge Analytica and one of the major financial backers of both the 2016 Trump campaign and the Brexit campaign in Great Britain. Now, lest you think that Rentech dollars are solely being funneled into one side of the political spectrum, Jim Simons is a major Democratic donor, as are many other folks at Rentech.

Yeah, Henry Laufer and other folks are also huge donors, approximately to the same tune as what Bob Mercer is on the right.

Yeah, tens of millions of dollars, many tens of millions of dollars on all sides and through many campaign cycles here from Rentech employees and alumni. This did become a flashpoint for the firm. in the wake of the 2016 election. Mercer obviously became a controversial figure, both externally and internally within the firm.

Especially once people realized he was the through line through Breitbart, Cambridge Analytica, the Trump election and Brexit.

Yes. Ultimately, Jim asked Bob to step down as co-CEO in 2017, which he did, but he did remain a scientist at the firm and a contributor to the models, even though he wasn't leading the organization with Peter from a leadership standpoint any longer.

Ultimately, the thing that surprised me the most is how these people all still work together, despite having about the most opposite political beliefs you could possibly have.

Yeah, understatement of the century.