[S5E6] The Man Who Solved the Market

Renaissance Technologies has become one of the largest hedge funds in the world through its pioneering investment approach that cares less about business fundamentals and more about mathematical pricing models. Join us today as we discuss Renaissance Quantitative Trading and the people behind this financial juggernaut. Welcome to Business Books and Company. Every month we read great business books and explore how they can help us navigate our careers. Read along with us so you can become a stronger leader within your company or a more adept entrepreneur. But before we get to the book, let's introduce ourselves.

I'm David Short. I'm a Product manager.

I'm Kevin Hudak, a Chief Research Officer at a Washington, D.C. based commercial real estate research and advisory firm.

And I'm David Kopec. I'm an associate professor of computer Science at a teaching college. This month we read the man who Solved the How Jim Simons Launched the Quant Revolution by Gregory Zuckerman. The development of quantitative trading has led to an enormous accumulation of wealth amongst a few large hedge funds. A pioneer in this space was Renaissance Technologies and its founder, Jim Simons. Instead of relying on traditional financial metrics and corporate news, quantitative models like those utilized at Renaissance look at much larger swaths of data fed into sophisticated machine learning algorithms. In this episode, we'll discuss what we learned from the book and how quantitative investing has changed the financial sector. David, kick us off. Tell us about the protagonist of the story, Jim Simons, the founder of Renaissance Technologies.

Yeah, Jim may have the most fascinating career of anyone we've read about on this podcast. He was born in 1938 and grew up in Brookline, Massachusetts. He got his bachelor's at MIT in math and then a PhD from Berkeley by 23, also in math. He famously traveled from Boston to Bogota by scooter with some classmates and founded a factory there with a classmate who was from the area. He began his career as an academic and had very significant accomplishments in math. The Chern Simons form contributed to the development of string theory. It provided a theoretical framework to combine geometry and topology with quantum field theory. He ultimately received the 1976American Mathematical Society Veblen Prize in Geometry for this work. In the mid-60s he worked with the NSA on code breaking, but between 64 and 68 he was on the research staff at the Institute for Defense Analysis while also teaching math at MIT and Harvard and actually took his first forays into trading at this time with a small firm called istar. That was unsuccessful and I think kind of shut down by management as they found out he was recruiting away his colleagues. He was Forced out of the idea for expressing views against the Vietnam War, and then started the Stony Brook trade chapter of his life. So he built a really Top Notch Math Department from scratch from 1968 to 1978 at Stony Brook, where he was the chair of the department. From there he had a really radical change and shifted his focus towards finance, founding Monometrics in 1982, which would become Renaissance Technologies. And so Renaissance ultimately launches the medallion fund in 1988. And I'm sure we'll get into a lot of this, but you know, just astronomical performance for this mathematical model focused fund. At his death, he was estimated to be worth $31 billion and the 55th richest person in the world.

What an incredible background Jim Simons had. And both in code breaking academia, several interesting entrepreneurial ventures. Let's zero in on that period when he leaves Stony Brook. So how does he come to found Renaissance Technologies?

So Jim has been an academic for an extended period of time and he just really feels like he's not being challenged in a way that he needs to. And he frankly wants to be more successful as well. And so he sets out to use some of the math that he's learned about in order to actually build a financial firm. And he works with some of his colleagues on this. But I don't have all the details if Kevin or Kopeck. You want to, you want to share some more about it?

Yeah, I think at this point it was interesting. He had invested in United Fruit Company, I believe it was. He had made some traditional investments while he was at Stony Brook that, you know, he was basing on intuition, market fundamentals, sort of that traditional investing approach. And he was a little dissatisfied with the returns he saw. And early in the book, Zuckerman is really focused on folks coming up to Simons and saying, well, you know, math doesn't pay the money. And then on the other side, you have a number of his professor, his professor colleagues and academics who are sort of looking down on him for some of this early interest in the financial markets. Those who end up joining him, you know, sort of see it as an experiment. And wouldn't it be great to apply what we have been leaders in and won medallions for in mathematics to a real world problem. So I think it was really a combination of Simon's curiosity. A number of his colleagues later on in the book are always saying that he was sort of in it for the money as well. And I think it was a deep dissatisfaction with what, you know, the financial benefits of being a professor. And A leader in mathematics, were, you know, there was an early anecdote about how his father worked at a factory, essentially in the family business, and he had an opportunity to become a salesman and he never did that. And Simon's probably looked back to the experience of his father not jumping out of a stable but low reward profession to go into something that was more along his passions. Saw that, remembered that, and decided to make the leap into, into finance.

I think you touched on a big theme of the book, Kevin, which is the pursuit of money versus the pursuit of what traditional society may see as a more valuable use of your time. So we'll come back to that throughout the episode. But tell us a bit about how his vision for Renaissance was different from traditional investment firms. The title of the book is the man who Solved the Market. So what was he doing differently to solve the market?

Well, as I mentioned, with his investment in United Fruit Company, at this point, most of the big names in trading were involved in traditional investing, essentially tracking the development of markets, gathering intelligence, using their intuition to guide their trades. This would entail economic and corporate research, following corporate news, sometimes even borderline insider trading, as folks would try to get the inside scoop on internal company business to then guide their trades. Traders were experts in the commodities and the companies that they covered. Simon's vision was very different. He believed that there were what he called macroscopic variables and there's a mathematical system to the market which if deciphered, would uncover market inefficiencies and guide his trades in directions that neither he might understand nor their competitors would ultimately understand either. It was all about data cleansing, back testing. So basically applying their system to the market history to see how their system would've reacted and gathering gigabytes at that time worth of historical information and then creating those quantitative models to drive their investments.

And he wasn't the first to think of this. There had been an area of investing called technical analysis that looks at past market data and looks at pricing trends instead of thinking just about company fundamentals. And he wasn't even the first to take that further and think about using more substantial mathematical models to see how prices evolve. But he certainly was a pioneer in it. So tell us more about this whole area, like what algorithmic and quantitative trading has come to mean and how what Simons was doing was a part of that.

Yeah, and feel free to expand on this too. But you know, really on the algorithmic and quantitative trading side, it's just that it's less important to understand the underlying market fundamentals. But Instead to map them out in mathematical models and trust the algorithm to generate, you know, quote, consistent enough profits, was what he mentioned. And, you know, I think in addition to kind of trusting the algorithm, a big part of the quantitative trading methodology for them was eliminating the lag from the human traders having to put orders in. You know, one of the early inefficiencies that they removed from the equation was the idea that you would have to call a number of dozens of traders on the floor to put those orders in. So with the advent of high frequency trading, that really behooves them at Renaissance. You know, at times they would account for about 5% of all the market movement and volume in a given day. And so it was the combination of technology to help them cleanse the data back, test the data aggregate into models, plus the high frequency trading that allowed their idea of algorithmic trading or quantitative trading to really succeed.

And he wasn't doing this alone. So he recruited a large set of academics, mostly mathematicians, and later on more computer scientists to join him in this pursuit. And like we talked about a few minutes ago, it was often looked down upon by their fellow academics. So somebody might be an esteemed mathematician, maybe they've even won awards. Maybe they have famous papers that are being highly cited and they leave academia from prestigious positions. Maybe they were a professor. Some of them were at Stanford, mit, Cornell, Stony Brook. These were folks in high positions in the ivory tower. And they're leaving and joining kind of an upstart hedge fund. For what reason? The problem is certainly interesting. Can we find fundamental algorithms that can be used to predict changes in the market, changes in individual sectors, changes in commodities? That's an interesting problem. But it seems to me, as we meet each character in the book, the number one reason they're joining is because they want financial rewards. And that's probably obvious, right? Why else does somebody go to work for a hedge fund? But it is truly something that, that racks some of their brains as they, as they're dealing with the intense work hours and the, the intense pressure of maintaining this hedge fund. They're kind of balancing their former life that they had as an academic and all the prestige, we may say, that comes with that. And kind of the. They can feel good about solving some of the world's problems to really just pursuing the almighty dollar. That was my sense as he's recruiting these mathematicians and computer scientists in the 70s, 80s and 90s, that they're really doing it not because only that it's an interesting problem, but number one, because they feel underappreciated, underpaid in academia and they want that, that financial reward. Is that what your sense was? The main recruiting tool that assignments could employ was the pursuit of wealth.

Yeah, I'd agree that a lot of it was all about financial gain. You know, Zuckerman repeats again and again this math doesn't pay idea. But I also feel like it was some of this for some of his hires who were sort of circumspect towards the end of their careers with Renaissance Technologies. I think they would look back and say that they there was curiosity and some of them even left because they didn't sense a lot of that curiosity in Simons per se, and they thought that he was really just in it for the money. I also think that when you, when you talk about some of those early hires they had, he brought in a lot of folks who he had recruited for Stony Brook originally, and he was setting up a more entrepreneurial department at Stony Brook, which I think did that department really well. So he's already recruiting people that have that entrepreneurial mindset. And so it's almost like a self selection bias there.

So for the first decade of the firm, it has what we may call middling results. It's not taking the hedge fund world by storm, it's not having outsized returns. It's doing enough to keep going.

But.

But it's not really something exciting yet. And it's not that they're not trying quantitative models. They are. The quantitative models they're trying are just not very successful. And there's kind of this revolving door of investment managers who often are famous mathematicians who are coming in to work with Simons, develop their own new quantitative model. And it's just never really taking off to the point where they could say that they've, quote, solved the market. So why is that? Why does it take so long to find this model? Why is there this revolving door? Why don't some of them stick with it at Renaissance? Why so much turnover?

Yeah. So I'm glad to take this one on because I think the main theme of this book is the power and the pitfalls of partnerships. What makes them work, what doesn't? When we get towards the idea of recommending this book or not, it's not necessarily a specific tome on how algorithmic trading works, how quantitative trading works. I believe its best benefit to the business community is in partnerships.

And I'm going to profile kind of four of those key partnerships early on. And Kopeck, you brought up a good point too. That it was middling returns in the beginning. You have to still remember, in the mid and late 80s, they were still largely focused on commodities currencies and the bond markets. And so not necessarily the highest possible upside or reward that they could be getting, but they were really training the model. And like you said, it was folks without financial backgrounds coming in and building these models. So one of his first managers was Leonard Baum, who was recruited from Simon's time at the ida, which was the Institute for Defense Analyses. So Baum comes in and he creates a lot of these models. He was a good partner to Simons as well. But really, Baum just couldn't be convinced on the mathematical or the quant trading. He was too focused on some of those traditional signals. And that would often lead him to butt heads with Simons. So he ended up leaving the firm. That was the first in this revolving door. He was one of the first hires in. Then comes James Axe, who was a recruit from Cornell. And I was actually wondering if Axe inspired Bobby Axe, Axelrod and Billions, but apparently that was more Steve Cohen. But Axe comes on board. He's super intense. He's very entrepreneurial. Simons had even invested in and set up Axe with Axcom, and they were developing early trading algorithms on the west coast in California. Renaissance was a co owner of Axcom, and that really directly led to the Medallion Fund. At the same time, Axe just kept pushing Simons to believe in the model, despite some of the massive losses that were coming down as the model sort of gained its footing. And Simon's, you know, eventually going against his own idea of trusting in the model, that ethos that he had suspended trading that Axe was doing. AX just wasn't having that and left. So some of it with Axe was culture fit right. Simons was in Long island and Axe and his team were in California. Axe was incurring these big losses. Simons was uncomfortable with it, despite his own ethos being trust the algorithm. And so Axe is out and then comes in Elwyn Berl camp. He was a Berkeley professor, which is where Simons had gone for undergrad. He was a game theory expert, and he really revolutionized some of those early algorithms. He was getting more into. He was going beyond just currencies and commodities and starting to look at what the broader stock market could offer there. Burlkamp was motivated by curiosity. He claimed that Simons was really in it for the money. But an important fact was that Burlkamp brought in Sander Strauss, who ended up being the first and most prominent of the data hunters for Simons. Right. He found some historic data sets that fed the model, and that led to some pretty incredible gains. And it was kind of interesting because Simon steps in after Burl Camp probably hit some of the biggest gains for Renaissance Technologies, but it also came at the sacrifice of Burlkamp's mental health.

He said Simons was calling him four times a day, all hours of the night, pushing him to change the model for even more gains. And so ultimately, Burl Camp even leaves. And again, that was all about culture, mental health. Simon sometimes had changing priorities. The last one I'll cover is Henry Laufer, who was described as Simon's best partner yet at that time.

Laufer was a bit less caustic and abrasive than Axe. He drove a friendlier culture than some of those past managers, and he revolutionized the firm by getting it on a single trading system as opposed to the disparate models they had been using. This likely made Simons much more comfortable with embracing the model and avoiding some of that intrusive intuition filtering in from traditional trading. Even then, though, during the dot com crash, Simons doubted the model.

Laufer kept saying, trust the model. Simons was doubting the model. And that contributed to Laufer being passed over for Mercer and Brown, who we'll discuss soon. Laufer ended up being named the chief scientist, which was a bit of an insult to him as Brown and Mercer took over. But I would say Laufer, it seems, left on his own accord.

He retired from Renaissance roughly at the same time as Simons. So from those four vignettes, you see a lot about culture, risk tolerance, and Simon's sort of coming to his own as someone who can trust the model and trust the algorithm, as opposed to having this intrusive intuition. Get in and you'll see that's another theme of the story.

Thanks for that, Kevin. You really took us through that cast of characters that was revolving through the door at renaissance in the 80s and the 90s, which leads us to Bob Mercer and Peter Brown. Really, they're coming from a different mold than a lot of these prior partners of Simons. Simons has been mostly recruiting mathematicians. And as, of course, the fun goes on and the models get more sophisticated, more and more computer code needs to be written. We have to remember, of course, that computing power is really the enabling technology of algorithmic trading. You couldn't do algorithmic trading in the 19th century or the early 20th century, before there were computers that allowed the ability to process huge data sets. And make predictions with machine learning, like algorithms on those data sets. So it started to become apparent as renaissance continued that they needed more from the world of computer science perhaps than they even needed from the world of mathematics. And of course, computer science was originally a branch of mathematics. But what happens is you start to see more and more computing people being recruited at the firm. And there's kind of a cultural change, it feels like in the book, as that happens. And there's even some vignettes about interviews where folks who come in who have a computing background are kind of surprised how the code that runs these systems is kind of strung together on, you know, bubblegum and duct tape. Finally, we have some really sophisticated computer scientists coming into the firm in the 1990s, and that's really led by Bob Mercer and Peter Brown. They're coming from working at IBM on speech recognition technology. Of course, at the time that was cutting edge stuff. And they were quite successful computer scientists before that in academia as well. So they're coming with this thought of and this perspective of let's get the code right more than let's just get the mathematical formulas right. And that eventually, I think, is what leads to the firm's success under their leadership is they're recruiting different kinds of folks, thinking about bigger systems, thinking about code, not just thinking about math. And I would attribute a lot of that change to the success of the medallion fund in the 1990s. Before we get further though, into the medallion fund, let's take a brief break to thank our friends@Audible.com for sponsoring today's episode. You can check out the man who Solved the Market and other great books over@AudibleTrial.com Biz. That's AudibleTrial.com Biz. If you go to that URL, which we link to in the show notes, you'll get a 30 day free trial of Audible as well as credits towards your first purchase. Like, for example, the man who Solved the Market. You can go listen to this book right now@AudibleTrial.com biz and you can actually go listen to it almost for free. Check out the link in the show notes to get started with your free trial of audible.com and thank you so much to our friends at Audible for sponsoring today's episode. So let's continue talking about the fund, the medallion fund, as it starts to actually be successful. The returns start to be immense. We're talking about average returns over the life of the fund of something of the order of 40% per year, which is unheard of in Investing to have that kind of consistent performance over decades. They have more than one fund at Renaissance, but the Medallion Fund is really the premier fund. Eventually, as this fund starts to take off under the leadership of Brown and Mercer, they actually start kicking out some of the clients who are invested in the fund. Why does Renaissance kick out outside investors from the Medallion Fund?

Yeah, I'm happy to take this one. So you both probably know better than me, but when it comes to algorithmic trading, quantitative trading, it seems like Simons and the team had decided that there was a preset ceiling to the size of the Medallion Fund, at which point some of their model would start to break down almost if they got a little too greedy, made it a bit too large, that some of these models would start failing to predict the different fluctuations and bring those big gains in. So they had that preset cap. And it sounds like the book doesn't get really specific around this, but it sounds like they decided they'd rather invest their own money in Medallion. So employees, retirees of Medallion invest their own money in Medallion for those outsized returns. And so they kind of pushed out the other investors. This is why it's so important that Magerman, who was one of the employees who had a dust up with Bob Mercer later in, in his settlement, part of the settlement, it wasn't about punitive damages or, you know, civil rights violations and getting fired. Really what was driving his settlement was the fact that he was granted the right to still invest in Medallion. That's how important it became to be a member of this, this hedge fund and be a subscriber into this as well. So once Simons also had started trusting the model fully, when he had the confidence that Mercer and Brown had brought in those incredible insights from IBM, that they had automated some of those back office functions, that the company was really set, it seems like he also didn't want to deal with some of the doubts from investors in Medallion. He didn't want to deal with those questions anymore. And so as a result, they actually set up the Renaissance Institutional Equities Fund, or reef, which was set up to channel some of the investor enthusiasm in Medallion, but also the fact that they didn't want to grow Medallion too big and they wanted to keep it a big to themselves. They had never really promised the same returns for the Reef Fund as Medallion, but they charged lower fees. Like Medallion was known in the markets as a very expensive investment.

Right.

Because their broker fees were so high. And I would say that REEF traditionally underperformed Medallion almost intentionally.

Right.

They hadn't gone so far as make REEF fundamental investings but and still use the algorithmic and quant philosophy. But REEF was looking for longer term investments, longer term market inefficiencies that they could seize upon and it tended to own smaller stocks. So when it comes down to it, there was the idea that Medallion should be kept for company, its employees, its retirees. Make that something special. Apply the full weight of the algorithm there lots of high traffic, high frequency trading. REEF was established to be almost the consumer product which slightly underperformed the Medallion fund but was could be bigger and could allow in more of that general public, more friends and family.

And I'll just add to that that one of the issues they were running into with the fund getting so large is that the fund's purchases or sales of some instrument were itself making causing price changes in that instrument. So the fund was having an outsized effect on the market. And that was one of the other reasons why they wanted to have a cap on how large it could get.

You mean they didn't want to corner the market and main potatoes, Right?

Yeah. So let's talk a little bit about these outsized rewards that the fund was achieving. These returns are just incredible. And if you look at there's an appendix at the back of the book that can Renaissance and its medallion fund to other hedge funds over the course of its decades that it goes through in that appendix and it's the best performing of any hedge fund ever basically. That seems very unlikely to have that kind of consistent performance over decades. Why can't other hedge funds reproduce those results? If there's some algorithm that they're employing, how come no other hedge fund has been able to discover that algorithm? If there's some model that they've developed, why hasn't anyone else been able to develop a model that's close to their model? Through reading this book, did you get any explanation for why their model is so much better than any of the other firms that are involved in quantitative trading?

I, I want to take a step back first and actually reflect on how crazy the performance of medallion is. Because 40% returns, that's after the insane fees. So they were charging for a significant amount of time. 5 and 44. So 2 and 20 is the traditional hedge fund structure. 2% of assets under management regardless of performance and then 20% of the profits. Or maybe it's 20% of the profits above a benchmark like the S and P. Something like that. Depends on exactly how they structure it. Renaissance 5% off the top and then 44% for an extended period of time on the fee structure and they were still returning 40% beyond all that. So it really is just a insane outperformance that the table on comparison to the other funds doesn't even really do it justice because it was actually performing so much better because they have those fees on top of that actual returns. I would say the book does not give a satisfactory answer. And obviously if anyone else knew how to do this, they certainly would try to. There are a number of anecdotes that do reveal some attempts to some degree. So there's a lawsuit that actually takes place where two traitors leave Medallion and they are working for Englander, which is a competitor. And it's. It's Wolfbein and Bella Polsky have left. Simons is furious. He claims that they have stolen, you know, information. They're incredibly successful. They make a hundred million bucks for, you know, Englander. And there does actually seem to be some evidence that these guys did steal some of the pieces of the model that Renaissance was using. There's a reference to, I believe it's Henry's factor or something like that.

It was Henry's signal. It was literally in the code. Henry's signal, yeah.

And that's. That's Laufer that, you know, Kevin was just talking about. So it seems, you know, beyond coincidental for them to have Henry's signal in their model at this.

This new fund.

So does seem like they did take some of the information, does seem like they were able to be quite successful outside of it. But we haven't really heard about Wolfbahn and Belpusky afterwards. It certainly doesn't seem like they were able to continuously outperform. The Englander does actually settle the lawsuit and fires them and pays Simons $20 million, but had made over a hundred million dollars off of the trading that these men had done for him over the time period. So there certainly were attempts. There are certainly a lot of other hedge funds that do, you know, trading that's similar in a lot of ways to what. What it is that Renaissance is doing. I'd say that the biggest difference that I'm aware of, and they go into it a little bit, but it is just very opaque, is that Renaissance claims that the Medallion Fund is a single model, that there is one model, all code goes into it, it's a single monolith. And all of their decisions are made by this one model that. That is quite different from what most other funds do. Most funds have a variety of different models that are Trying to trade off of particular different signals. So putting all of the signals all together and having one model is one thing that's, that's unique about them. But this performance is, yeah, unmatched in history. Like, it, it does to some degree raise, you know, my question of, of credibility. Like, I certainly have no, no evidence to doubt that this is true. But, like, this kind of outperformance is unheard of. And like, is there something weird going on? Who, who knows, I guess, is the real answer. Uh, we have no evidence to support it. The fact that it became like, close to outside funds does make it relatively clear that it's not like a Ponzi scheme.

Right.

I think so. Like, how is it that they've able to, to have this. Did they truly solve the market? Like, who knows? Maybe this just is this amazing model that, that can consistently have these returns that even in downturns, it still does. Okay. Um, it really is just incredible.

And that's really one of the big problems with the book. The book is called the man who Solved the Market, but it does not explain how they solve the market. Of course it doesn't, because whoever they interviewed, and it sounds like they did quite good background research, was not going to reveal what the secrets of the model are. So a lot of people, if you read reviews of the book, come out of it a little disappointed because they thought they were going to find out, what are these secrets? How did Renaissance have these outsized returns? You don't get those answers by reading the book. Now I also want to take this and shift it to some kind of Metacriticism, which is the book is written by a non mathematician. So it's a Wall Street Journal reporter who wrote the book. When I read tech business books, which we've done many times on this podcast, as a computer science person, I often read them and I realize this person who's writing about it doesn't really understand exactly what they're writing about. They have the general outline of it, but they don't know enough to do investigation and dig deep and understand if some of the PR that comes out of the firm that the book is about is really bs. I got the sense that this Wall Street Journal reporter doesn't have enough of a mathematical background to really do the deep digging and maybe call BS on some of what he was hearing from these interviews with folks who used to work at Renaissance. So I think that's always the problem with when a journalist writes a book about a company that's involved in a technical area. You don't really get the kind of deep dive skeptical reporting that you might get if the book was actually written by somebody who has a strong background in that area. And so you are not going to read the man who Solved the Market and find out how the market was solved?

Yes, it'll be a story about the man, but that's really great feedback and I agree with you there, Kopeck. Going back to the returns though, you know, I also think the phrase that pays was always trust the algorithm, trust the model. And when it comes to how Medallion was able to achieve this, and you guys can correct me if I'm wrong here, it seemed like Simons knew when to pull back and be a bit more conservative. Sometimes it was maybe a little overreaction. It led in the departure of two of his early team members like we discussed. But it also seems that in two of the biggest shakeups, one being the dot com bust, two being the 08 recession, algorithmic trading had enabled them to succeed and in fact they had incredible returns during both of those events, in part because the quantitative trading model did not buy into the hype of the dot coms and it also predicted all of the market volatility around 08, which allowed them to escape relatively unscathed. So I do think that some of this was Simons injecting. Going against his own philosophy and injecting some risk aversion may have helped the company at times, but then also the very nature of quantitative trading allowed them to avoid a lot of those losses during two of the most impactful economic and market events of our lifetimes.

Okay, so let's talk a little bit about the politics at the end of the book. The book actually gets surprisingly political. Bob Mercer became something of a funder of conservative causes during his time at Renaissance, but especially towards the end of his time at Renaissance, he got very involved in the 2016 presidential election, specifically backing Donald Trump and those associated with Donald Trump and Jim Simons, who's of course the founder and continued to be the chairman even after Bob Mercer and Peter Brown were appointed co CEOs, was very involved in Democratic causes and was backing Hillary in the 2016 election. It becomes a core focus of the book's narrative for the last couple of chapters. What did you think about the book's exploration of this area, both the substance of it and also from a meta level? Was this a good use of space in the book?

So I honestly felt like this was a reflection of the time of when this book was written and that had it not been written right as the 2020 election was impending, that I just don't think Zuckerman would have gone into all the detail here. I thought it was interesting. I thought, like, I sort of knew some of this history. I didn't know all of it. We can certainly go into it a little bit. But to me, it really feels like, like a mashed together, like, other book, really. Like, it kind of comes out of left field and yeah, really drives like a significant amount of the end of the book. And it just, like, has nothing to do with solving the markets. Right. And felt like it was an important thing in terms of, like, Renaissance Technologies becoming a more publicly facing thing. People being aware of, of rentech. It's probably when a lot of people heard about Bob Mercer for the first time. But in terms of it actually being important to, like, what happened with Renaissance, I guess it is like, ultimately Mercer is sort of forced out shortly after this political controversy happens. And I guess let me say a little bit about what that is because we're kind of speaking over it without addressing it directly. So Mercer was one of the largest funders of Trump during the 2016 election, and his daughter Rebecca was also very heavily involved. I think she had positions on the campaign and then ultimately in the White House. He and she are credited with introducing Steve Bannon to Trump. And Steve Bannon is one widely credited with having turned around the Trump campaign and ultimately delivering Trump the White House in 2016. And so many people believe that Bob Mercer was a very important figure in getting Trump elected in 2016. Many of the people that worked at Renaissance Technologies were quite liberal. Many of the investors were pension funds and union funds and things like that that had issues with having this very significant Trump donor getting that money from the fund of their union. So there were some unions that I think withdrew their funds, and it certainly was something that had an impact on the company, but it really, to me felt like it was very much just the timing that led it to become such a significant part of the book and to me felt kind of unnecessary and not consistent with the tone of the rest of the book donor getting that money from the, you know, the fund of their union. So, you know, there were some unions that I think withdrew their funds and it certainly was something that had an impact on the company, but it really, to me felt like it was very much just the timing that led it to become such a significant part of the book and to me felt kind of unnecessary and not consistent with the tone of the rest of the book.

Yeah.

And short I definitely agree with you on the kind of political intrigue narrative that really takes up the last quarter of the book. And in fact I sort of regretted that Simons became a background character despite being the protagonist of the book. I do think though that what Zuckerman was trying to do here was to show us the law of unintended consequences.

Right.

We start the book with these bright eyed bushy tailed academics dipping their toes in the world of finance. And then by the end of the book he sort of painted this picture of the monster that was created for many of them. I think that David Megerman is essentially, if it weren't fictional, he would be sort of the roll up character for a number of these academics. Right. He sees the work that he contributed to the billions that he contributed to being used for something that he is very much opposed to and then being pushed out of the Renaissance fold by the end of the book after a disastrous charity event that they were at where he had dust ups with both Rebecca and Bob Mercer and Simons in the end. So I do think that this was a, this was a choice by Zuckerman to sort of come around360 and end the book with. With some message.

Yeah. I also found it a little disjointed. I always think about what's the motivation of the author in writing the book in the first place and I wonder if this book comes out in 2019. It's a couple years after Trump takes office, obviously for the first, first term. Mercer and his daughter had been in the media quite a bit. So was that part of his motivation to write the book in the first place? It had to be in the book in some way. It maybe didn't need to be as large a part of the book because ultimately it led to Mercer having to leave as co CEO. So it's an important part of the story. And when Mercer is forced out as co CEO is quite fresh in 2019. I think it was a year or two before the book came out. So it had to be part of the story. Did it have to be that big a part of the story? I don't think so. And was it maybe done partially to help sell books? I think that's possible. Maybe it was also just the author's interest. Right. And the author clearly is kind of against Mercer. You can see that throughout the narrative he kind of sticks in these points, how Mercer's viewpoints seem weird and, you know, and unconventional and some of them are in my opinion. But at the same time, why does he kind of throw those in throughout the narrative to kind of lead up to this whole section later on. I guess it makes it more of a continuous thread throughout the book. But then to have it such a big focus of the last quarter did seem disjointed to me and almost like it was done to sell books or maybe to appeal to some of the author's colleagues who view the political situation the same way and are kind of abhorred that he's covering this subject matter on this hedge fund that had these characters that led to Trump's election. So it seemed very unnecessarily political in some ways, the way it was covered.

Well.

And I typically like the cinematic nature of the books that we read. And I think that we just didn't get a payoff here.

Right.

Because you have Meagerman at the end and you have Mercer. They actually end up leaving. Well, Meagerman was fired, but around the same time. And I was looking forward to the epilogue covers it a bit. But this idea that now that we have this kind of polarization out of the way, we've talked about politics, here's how they buckled down and they just became a well oiled, over functioning machine.

Right.

And we didn't really get that in the epilogue as much.

Yeah. The other thing we get at the end of the book is discussion of Jim Simon's philanthropic pursuits. And he's funded things like autism research and exploration of physics through his Simons Institute. Now he dies about five years after the book is published. I think he dies in 2024. So we don't see the fruition of these efforts through the rest of his life. But what did you think about the philanthropic coverage and why was that kind of thrown in at the bookend of the book?

Yeah, I think that some of this was almost a reaction to the overreaction of the author towards the Mercers. You know, Simons and Brown and others are portrayed as more progressive leaning. And Simons in particular, the way that he kind of re resumes his role as the protagonist of the book is largely through his philanthropic efforts, which were very, very admirable. And so it's almost kind of painting. It's giving them a fair shake.

Right.

Mercer was behaving this way. Simons always had philanthropy. You know, having amassed over $30 billion. Simons and his wife were less about vanity. Philanthropy is something that I think Zuckerman mentions. At one time, they were more focused on impactful contributions like the Simons foundation. You mentioned a focus on health and education, math, teaching and math education at elementary and middle school levels. You mentioned the autism research as well. With one of the most ambitious autism research programs, gathering Data and following 2,800 families that were impacted by autism. I love the fact that, you know, given his math and cryptology background, he was really big on computing and astronomy and physics as well. He had been crusading to better understand the background radiation, the background noise of the universe, to really get to the origin of the cosmos. And I'd also say while it's not vanity, driven two of the most important philanthropic efforts that he did, he tragically lost his two of his sons in different accidents. And for someone who built his life around identifying patterns and forecasting market volatility and just systematizing everything, the fact that he suffered two tragic, unpredictable deaths is just sad. And, you know, one thing that warmed my heart was seeing his reaction to that at first, depression and trauma and dealing with that trauma. But when he lost his first son in a climbing accident, he established a nature preserve in his memory. When his second son drowned on a trip to Bali, he established an institute in his memory to support Nepali healthcare. And I just thought that, you know, the reaction to trauma being to help others and to drive better lives for others was really good. And again, it all comes down to this contrast between what Mercer was doing with, you know, the funds from Renaissance versus what Simons was doing. I think it humanizes Simons at the end.

Right.

We have to remember this is a biography. It's not necessarily a business book at all times. And so I applauded Zuckerman for including that at the end.

It's interesting you say that, that this is a biography. It clearly has elements that are biographical, but it doesn't fully just focus on Simons. It focuses on Renaissance technology and takes a lot of breaks to go into large vignettes of the other characters. I think it's hard to put this book clearly in one genre, but I think the reason that they included so much about philanthropy at the very end of the book is almost to answer this question about the ethics of all the folks who are involved at Renaissance Technologies. And throughout the book, we see folks who are joining or who are leaving or who've even just been there a long time kind of have these musings about, oh, you know, do I like what I'm doing? Should I have left academia? And as we talked earlier, the kind of snide remarks from former academics who are like, they're just in it for the money, so what's their justification? Of this pursuit that they've done for decades, where these are some of the greatest minds in mathematics or computer science who could have been solving hard problems for all of society and instead are, quote, solving the market really for themselves. They're not even letting other people into their fund at the end, so all they're pursuing is the almighty dollar and they're keeping their research to themselves. Right? They, some of them are publishing papers still, and some of them are doing math research still. But ultimately, how they solve the market is not in this book because obviously they're not publishing it. Right. So I feel like the reason the philanthropy is covered so much at the end is almost the author wants to justify Simon's pursuit of the almighty dollar becoming one of the richest men in the world instead of solving those hard math problems. And look, people are free to do whatever they want, but it doesn't mean we need to applaud them for their philanthropic work. When they earn $30 billion, I think that's the least you can do is some philanthropic work. And maybe a lot of Simon's work is very, was very genuine. I'm sure it was. I mean, it sound. It sounded very genuine. But why place it the way it did in the book at the very end and not kind of incorporated throughout the entire book? Because he was doing the work for decades, right? Unless it's kind of this, oh, here's the justification for all of what he did. And I think we can have a pretty extensive discussion about the ethics of algorithmic trading. But what did you think in general about the ethical nature of this business and the folks who were involved with it?

One quick point I think is worth repeating. I think we may have made it earlier, but it really jumps out to me as we're talking about this again, is that while Simons often talks about, like, a desire to keep New York good quality math teachers in the math program and, you know, have them, you know, pay them extra so they'll, they'll stay in those roles and won't take, you know, industry jobs where they can make more money given math skills or, you know, in other demand. What he actually did was take a lot of the most brilliant mathematicians and pull them out of academia and pursuing additional, you know, math in fields that could be, like, actually fairly important to, you know, fundamental discoveries of, you know, physics and things like that and had them focus on, like, making money instead. So I do think it's something that, like, he probably struggled with even himself. And, you know, it's certainly something that, that they Talk about in the book a little bit of like, oh, like, is this, is this the right thing? But he really did, like, take these, like, the most brilliant minds of his generation and put them towards printing billions, which, like, hey, like, people certainly have a right to, to do whatever they want with their. With their time.

But.

But it is like, an interesting thing where with a lot of his philanthropy, he seeks to kind of keep people focused on these academic pursuits and advance this research when the way he made all the money was pulling people away from that research.

I can't believe that I'm going to be the most free market of the three of us and say, maybe those universities should be paying them better. But Kopeck, you brought in a valuable point of view, I think, coming from academia, but I think it's not necessarily what's more ethical, what's less ethical when you look at the algorithms themselves. You know, it depends on if you believe an uneven playing field is just unfair or if it's truly unethical.

Right.

If you call it statistical arbitrage or algorithmic trading or high frequency trading, all of these terms have different connotations and weights.

Right.

Simons noticed that mathematical models could track and project market inefficiencies and that high frequency trading could seize upon this. He put those two things together. There were moments when some of those drastic market shifts were caused by the algorithms that Simons and his team developed. I mentioned the main potatoes before, but also, you know, the flash crash, you know, triggered by algorithms in 2010. So there's a bit of ethics and a bit of, I would say, not unethical, but there is a sense of responsibility and accountability here.

Right.

And, you know, as. As Zuckerman notes, speculators had been doing a lot of this for centuries, and other firms did exist at the time that they were doing what Simons was doing, but just with less prominence, less legacy, maybe less staying power. I do think that when it comes to their justifications, I think that Zuckerman was communicating to us with the turn towards Mercer that sometimes these things have unintended consequences. These academics got into this and it created a monster. So I can see that commentary from the author. But I also do believe that in the end, I imagine Simons would be proud of his legacy. I don't think he thought what he was doing was unethical. And in fact, when you talk about insider information, insider trading, that algorithmic trading has somewhat replaced, it may even be a good thing.

When you think about what a business does, I always think about Is it something positive for society or something negative for society? As somebody who dabbles in investing in individual stocks myself, I would never invest in a cigarette company. I would never invest, probably even in an alcohol company. It's just not something that I want to be a part of. Even if I think I could make money doing it, I'm not putting myself on a high horse. I think that's a pretty low bar not to invest in things like that. Renaissance technology would invest in anything if they could make money doing it. That's the sense I get from the book. There is a part of the book that talks about the ethics of this and how the employees felt about it. I'm going to read a quote from page 229. So this is talking about how employees felt about their profession and how they justified what they were doing at Renaissance to themselves. Here's what the Zuckerman wrote. Most employees concluded that their heavy trading was adding to the market's liquidity or the ability of investors to get in and out of positions easily helping the financial system. Though that argument was a bit of a stretch since it wasn't clear how much overall impact Renaissance was having. Others committed to giving their money away after they had built a sufficient treasure chest while trying not to focus on how they're expanding profits necessarily meant dentists and other investors were losing from their trades. It's not a zero sum game, but what algorithmic trading does is negative for some other investors. But now this is me, now I'm we're out of the quote. And so they have pretty weak justifications here. The author is saying they basically don't buy the liquidity argument. And by the way, that's the same argument that I've had from some of my friends from college who went into investment banking. You talk to them a few years out like, you know, and say, well, do you really like what you're doing? Kind of pushing paper around, not doing anything productive for society, but making more money for your firm. And they'll say, we are doing something productive. We're creating liquidity for the market. It's as if they've all learned that, that, that sentence to repeat to people who challenge their conscience on the other side of this, the building up wealth so that later on they can give it away. I mean, that's kind of nice, but that depends if you agree on what they're going to give it away to. A lot of people don't like that Bob Mercer generated all that wealth to empower Steve Bannon and a Lot of people feel on the other side, maybe they don't like that Simons gathered all that wealth to give it away to Hillary Clinton. So, you know, that's also not a great justification unless we think that we should just empower individuals to be able to give away to their favorite causes. And that's always a good thing. So there is no good justification in the book for the ethics, in my opinion, of what the firm is doing. That doesn't mean it's not an interesting problem, and it doesn't mean that more power to them, that they solved the problem and were able to generate so much wealth for themselves. But I certainly wouldn't hold any of them on a high horse. And I think that's one of my problems with the book as a whole, is it feels like a hagiography of Simons when really what Simons did is accumulate as much wealth as he could for himself. And yes, he gave a lot of it away later on, but that isn't really somebody who I think of as a hero who spends their life pursuing wealth. I don't know. And maybe, you know what, if my fit. There's some vignettes in the book about somebody who couldn't afford, like a professor. There's a couple who are like, I couldn't afford some of the things I needed for my daughter or my son. I get it. If I was in that position, I would do it, too. I'm not smart enough to probably work at an algorithmic trading firm, but if I was in financial straits and I need to go work at a somewhere like this, I don't think what they did is unethical. I don't. Let me just put it that way. I don't think the work that Renaissance Technologies did was unethical, but I also don't think we should be holding them on a high horse like they're some paragons of virtue and that they deserve a hagiography written about them.

Well, and I also think too, that Zuckerman, it almost seemed out of place in the beginning, but he would go into the personal relationships that some of these folks would have, the failures of their marriages, Simon's family issues as well. You know, I think that the model, as portrayed by Zuckerman, the model is cold and indifferent and returns these incredible gains, but the outcomes, you know, they can't escape the cold and indifference of the model when it comes to some of the outcomes in the market, in their family lives, et cetera. So I. I think he profiles that, and I definitely agree with you on the ethics part, Copak, I think it's not an ethics conversation. It is more of a responsibility, social responsibility and accountabilities discussion.

Okay, that was intense. Anything else we didn't cover that you want to cover about this book?

I just love that you used hagiography. Going back to my religion classes in college. So I thought that. That you are spot on in that analysis, that in some ways this was, you know, putting them on a pedestal. In other ways, Zuckerman was sort of taking them down a bit. And you could come at this from very different angles. I think the biggest thing is that they did not tell us how they solved the market, but that they told us about the man who solved the market.

Yeah, I think that's a fair summary. Okay. Thinking about the book as a whole, do you recommend this book to our listeners and if so, who should read it?

To be honest, I don't really recommend this book. I think we covered a lot of the most interesting anecdotes and what we learned from it, but it just felt a little bit too disjointed to me, like the end piece on the politics, I guess if I were going to recommend it, I would recommend either reading the first part or the second part, depending on what it is that you were looking for. So if you are interested in what, you know, Bob Mercer did in the 2016 election, I think those last few chapters are good, you know, coverage of that. If you're interested in the history of Renaissance technologies and all the different players and really getting pretty interesting insight into them as people, but not a lot about, you know, how they actually solve the market, then, yeah, I would recommend the, you know, first three quarters of the book, but as a whole, it just fell flat for me.

Yeah, I'm torn on recommend or not to recommend. I think that for different audiences, my answer might change. So, for example, if you are interested in learning about finance, culture, talent recruitment and retention, building a good internal culture, I said, I think the theme of this is the power of partnerships and the pitfalls of partnerships. I think this is a good book for you to read. I think if you are a retail trader, consumer trader, amateur market watcher, I would definitely recommend it. Just as a reminder of just how much information is out there and that the elite hedge funds and these firms have so much more of it than we do as retail traders. Even with the incredible revolutions of the Internet age, it seems even before that, quant trading was a thing and thriving. And it serves as a reminder of what you should Expect when you're getting to the market, the chips that are stacked against you. And if you remain optimistic after that, all power to you. And I also think that this is it's interesting because this book would probably be most beneficial or informative to some of the algorithmic traders themselves. And I read one review from one of them. They were super critical, just like Kopec was of the book, not having enough math, not having enough in depth algorithmic trading background, not telling us how he solved it.

Right.

These are the audiences that the book would most appeal to and it's not giving them enough.

Right.

It could also appeal to fans of compelling biographies. I thought that some of the stories, the background, the crisscrossing of different industries, public sector, private sector, were interesting from a biographical standpoint. So again, I'm a bit torn on recommending this. I wouldn't just recommend it to our average listener, but again, if you're interested in learning more about partnerships that work and fail and interested in getting a good perspective on retail trading versus hedge funds, it might be for you and for me.

People might have predicted this if you've been listening. But I can't recommend this book for several reasons. Number one is what both of you spoke to, which is the disjointed nature of the narrative. The few chapters on the politics could have been better weaved into the narrative and didn't need to be so explicitly political. My other problem with the book is that it goes into character arcs and out of them like as if it's buying something from a vending machine. We go into a chapter and we'll get half a chapter talking about someone's background and how they got involved in the firm. And ultimately that's somebody who might have only been in the firm for three or four years and then we never see them again. I don't think that was a good use of the book space. If it wants to be a biography. And this is kind of the it can't decide what it is thing that we talked about earlier. If it's a Jim Simons biography, use that space talking about Jim Simons and introduce enough of those characters that we understand their interactions with Jim Simons. But don't give us like many mini biographies throughout the book, distracting us from the main story, which ultimately is probably not a great use of space. And also, of course the book title is not accurate. We learn. Well, at least its promise is not accurate. You expect you're going to find out how they solve the market. Of course, I guess if you Think about it logically. You knew we weren't going to find out how they solved the market, but we don't even get enough to understand the basics of probably how they solved the market. And I think, again, that might be partially because the author is not coming from a math background themselves, or maybe it's just because literally that's how little the author could get out of the various former employees that he interviewed and from the. Also from the public disclosures that he was able to. To go through. So, yeah, I don't really know who this is for other than somebody who is really interested in hedge funds, really interested in algorithmic trading, if that's you. If you want to know the history of Renaissance technologies and you want to understand kind of the characters that were involved in the founding of algorithmic trading, then this is a great book. But for basically anyone else, I can't personally recommend it.

Kopeck, I love what you just said, too, about some of the vignettes and the character arcs just disappearing. You know, I was thinking as I read it, there was one point when it was probably six pages of people coming in and then just disappearing. And it reminded me of the Sandlot, if you remember that movie from the 90s, which is fantastic.

Sure.

And how at the end they're doing the epilogue and the narrator says, you know, Weeks, he got really into the 60s and no one ever really saw him again. And the character just fades from the shot. And I felt like in the beginning, this is almost like the sandlot coming together with all the professors and academics. And then there was the same sort of goodbyes to some of these characters where he would either wrap it up with a sentence or you would never hear from them again.

Right.

He doesn't mention anything about Lauferb.

And I like something you mentioned, Kevin, that I would add, which is the culture of these firms is well covered in the book. And so if you want to better understand kind of hedge fund culture, you get a significant amount of that in this book. And that's mildly interesting. So that would probably be the thing that I got the most out of it. While I don't didn't find out how they solved the market, I did find out kind of what it's like to work at one of these hedge funds, which is mildly interesting. Okay, so next month we're going to be reading Influence. David, tell us a little bit about what we'll be reading.

So Influence by Robert Cialdini is like, it sounds a book about influence, the psychology of Persuasion. The book was a bestseller. Millions of copies have been sold and I think there's a 20th anniversary edition that came out a few years back. So it's not a brand new book. It's a well worn resource that lots of people have used to try to become better at influence and persuasion with a lot of academic psychological work. So I think it'll be interesting for us all to learn more about how we can influence better.

Thanks for that, David. I'm looking forward to it. Want to remind our listeners you can get a free trial of audible.com by going to audibletrial.com biz. That's audibletrial.com biz. You can check out all the books we read on this show in their Audible forms for free by trying that free trial. And before we get to saying goodbye, is there anything that either of you want to plug and how can our listeners get in touch with you?

You can follow me on xavidgshort.

You can follow me on x @hudak's basement. That's h u d A K S.

Basement and I'm Dave Kopeck on X. That's D A V E K O P E C. Don't forget to subscribe to us on your podcast player of choice, whether that's Spotify or Apple Podcasts. Hit that follow button, hit that subscribe button. Make sure you don't miss an episode and we'll see you next month.