We Study Billionaires - The Investor’s Podcast Network - TIP273: Billionaire Jim Simons' Quant Revolution w/ Gregory Zuckerman

Starting point is 00:00:00 You're listening to TIP. On today's show, we talk about legendary billionaire quant investor Jim Simons. I know this doesn't sound even believable, but Simons achieved a 66% annual return since 1988 before fees for his investors. To talk about this recluse investing legend, we have the author of the bestselling book, The Man Who Solved the Market by Gregory Zuckerman. This is a fascinating discussion, so let's go ahead and dive in. You are listening to The Investors Podcast, where we study the financial markets and read the books that influence self-made billionaires the most.

Starting point is 00:00:41 We keep you informed and prepared for the unexpected. Hey, everyone, welcome to The Investors Podcast. I'm your host, Preston Pish, and as always I'm accompanied by my co-host, Stig Broderson, and we're pretty excited to talk to Gregory here. Gregory, welcome to the show, first of all. Thank you, thank you. Great to have you here. I loved this book. And the reason I think I was so excited about this is because I didn't know that much about Jim Simons. Talk to us about Jim Simons because I think there's a lot of other people

Starting point is 00:01:21 like me out there that don't really know who he is that well. And just talk to us a little bit about who he is. And then also talk to us about how you decided that you wanted to write an entire book about Jim Simons. Sure. So yeah, Jim Simons is the greatest. modern-day moneymaker world of finance has seen. And I use that word, a phrase moneymaker, specifically, because it's hard to define what he is exactly. He's a trader, you could kind of say. Is he investor maybe? Depends how you define either trader or investor. They're short-term investors, he and his colleagues, but it's not high-frequency trading. So, but yeah, his returns are just outrageous, 66% a year on average since 1988, when he in his fund, it's called the Medallion Fund,

Starting point is 00:02:14 and the firm is called Renaissance Technologies. They sort of decided to go all in on a certain type of trading, which is the kind of approach that everyone is embracing today, which is quantitative using a rule-based system as opposed to intuition and judgment. So it's a fascinating story and it's one that I've always wanted to tell. It's just been almost impossible for people like me to figure out how he did it. So he's not only the best trader investor slash moneymaker, but he's also the most secretive one Wall Street has ever seen. He and his colleagues don't talk to press, et cetera. So I decided to see if I could tell that story. And the result of that effort, years ago, I don't know, maybe five, six years ago, I approached Simons and his people.

Starting point is 00:03:04 and said, I want to write this book, and they said, no, we're not going to talk. He's not going to talk to anyone. If he ever decides to talk, decide, he'll let you know, Greg, but don't hold your breath. And I just decided maybe, I don't know, late 2016, that I'm just going to do it, whether he wants to work with me or not. Mostly because I was fascinated by the story, and I wanted to figure out how he did it. So I threw myself into this project, and then for about, I don't know, six to eight months, he and his people still said, sorry Greg, he's not going to talk to you.

Starting point is 00:03:37 Then finally I kind of broke him down and he spent over 10 hours with me. He didn't sort of open up the kimono and tell me his secrets, but that I had to get it from other people. But he was very generous with his time talking about other parts of his life. And yeah, he has a fascinating life

Starting point is 00:03:53 even before and after he ran his firm. So Jim Simons is not just good at math. It might not even be a stretch to call him the very best in the world, But really not just that, he's so talented in so many fields. Could you please give us some background information for those of us who are not too familiar with him? So even if he had never invested in the market, I think he'd still be worthy of a book because he really goes down as one of the greatest mathematicians, specifically a geometer

Starting point is 00:04:23 over the last 50, 100 years. He grew up in Boston and Brooklyn and then in Newton, got his PhD. He first he went to MIT, then he got a PhD in mathematics at Berkeley, came back and taught at Harvard and at MIT. And yeah, some of the math he did later on in his career still cited regularly, both in the world of mathematics, but also in physics. He didn't realize that some of his work would have impact, a broad impact. But he also spent time as a codebreaker for the government. This was during the Cold War, going up against the Russians at the time. We as a country were on a bit of a losing streak.

Starting point is 00:05:00 we weren't able to decipher how the Russians were communicating with each other. And he gets a lot of credit for that as well. So then he started a department at Stony Brook, the mathematics department, and recruited found talent from all over the country. So yeah, again, he had this really glorious storied career in academia and mathematics and code breaking, even before he decided to focus on investing. And frankly, people in his world were little disappointed. when he went and tried to conquer the market because he was so well respected in the world of mathematics and academia. So you allude to the idea that Simons was doing a lot of technical analysis early on when he was

Starting point is 00:05:46 starting his fund. And what I find kind of fascinating is that when you talk about pattern recognition, call it a head and shoulders pattern, or you name it whatever technical pattern that these people that implement that approach are looking for. And I thought about the fact that he came with this geometry kind of mathematical background where he was best in the world, right? I'm kind of curious if the approach he started out using early on was based on him basically coming up with geometrical models. Yes and no. No in that he didn't use the mathematics that he had worked on to trade. Yes, in that he's a scientist. He's a mathematician, and like all scientists, he's sort of

Starting point is 00:06:35 trained and instinctively, he looks for patterns, he looks for structure when on the surface there may not appear to be. That's what scientists do. They look for structure in chaotic situations, and many at the time thought of the market as a random walk. And they kind of viewed it. This is people in the world of academia, they said that you can't really estimate where the market is, you can't tell. And instinctive level, I think Simon's being a mathematician, said, you know what? I think that's wrong. I think there's some structure here. And that's what he told his colleagues, people he hired early on. I don't know what it is. I don't know how we're going to find it, frankly, but my instincts are the market isn't random. And also the market

Starting point is 00:07:19 isn't necessarily something that you and I can predict by just looking at the news and judging where prices are going to go, they saw themselves as maybe much more sophisticated technical analysts, technical-type traders. And, you know, people, a lot of people on Wall Street dismiss technical trading as fruitless, as sort of hocus-pocus, alchemy. And that's not how Jim Simons and his early colleagues saw it. They thought they could do a much better job of technical analysis. Sometimes it's done in a poor way and a not very sophisticated way. But they decided to take it on in a much more sophisticated way. But yeah, it's a much more elaborate way of doing technical analysis.

Starting point is 00:08:04 So in your book, you tell this story about how he looked at the correlations and magnitude through asset classes and looked at the correlations throughout the trading day and broke it up in incrementals. Could you please talk to us how much he was ahead of his time and what the extra computer power did to his investment process? Well, he went back and forth a lot. So early on, he said, we're going to build models. We're going to build mathematical models to make predictions of where the market is going to go. And we're also going to acquire and digest and clean data. And it wasn't something that was done at the time. You can really see Jim Simons and his colleagues as early data scientists before predictive ways of, before obviously Amazon, Netflix and 10 cent, all that kind of stuff. Early on, that's what they tried to do, but it didn't work. And as you suggest, partly it was because the computer power wasn't there, partly because they could only build crude models, and it didn't work. And at one point early on, they cornered the market on contracts for main potatoes,

Starting point is 00:09:10 inadvertently. And basically, it was just another sign of how they weren't doing a great job early on. So he shifted to more traditional type investing, and he made some money, lost some money, and he couldn't deal with it. His stomach literally was feeling pain. So then he came back to building models. And yes, computers were a little more powerful. We're still talking to the late 80s,

Starting point is 00:09:31 but he went back and forth for years. And finally, in the late 80s and early 90s, settled on this approach of short-term trading, not high frequency. We're talking average holding period of about a day or two. And as you say, he broke the day up into bands, bands of five minutes. You started 20 minutes and went down much shorter, and they looked for correlations.

Starting point is 00:09:55 They looked for in all kinds of markets, various commodity markets, currencies, bond futures. They could not figure out equities until later, until about 1996. But they did see repeating patterns, and that's what they do. They look for patterns that you and I can't really, with our eyeballs, pick up on, but they noticed patterns and they bet on. That assumption was that they would repeat. Let's take a quick break and hear from today's sponsors. All right.

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Starting point is 00:14:39 remember what the exact percent was. It was around 50 percent. When I read that, I immediately thought, okay, well, he must have a really, really firm grasp on the Kelly criterion. It's a really good question. So you would think so. And that was, and that is their approach where they try to get it right. They want to try to get right all the time, but they basically get right 51% of the time, 52% of the time, not unlike a casino. And they trade very frequently. So if you trade a lot and you're getting a right just barely over 50% of the time, you can make a lot of money. And that's what they do. And I know he studied the Kelly criteria, as did others I write about in my book. There's a guy named Elwyn Burla Camp, who literally studied with Kelly as an academic. But when I ran it

Starting point is 00:15:27 past some people internally, they kind of say, yeah, we tried using Kelly specifically and maybe some of the concepts we can use, but the Kelly criteria itself didn't really work for them. So I'm a little confused myself, frankly, you would think that they use it, but they didn't use as much as one might think, but that could be a head fake someone who was trying to trick me on. And frankly, some of the time I had to be careful because they don't want, the people internally, especially they don't want the secrets coming out. I do think I reveal some of the secrets, all of them in my book, there are still many secrets out there that I'm still trying to figure out and understand.

Starting point is 00:16:03 Very interesting. And I just wanted to mention to the listeners who are not too familiar with the Kelly Criterion that it's a formula to determine how much of your portfolio you could put behind any bet that you made, given that you know the probability that you're right and what the risk and reward of the outcomes would be. But anyways, you talk about Hymn Simons was one of the very first to apply machine learning, but much of the machine learning that we understand today has really just progressed in a major way over the past, called five to ten years. How was Simons able to get the returns

Starting point is 00:16:38 that he had basically before the technology existed? Well, what they did was, and this was really early, as you suggest, they built a model that taught itself, and Simons and his colleagues often weren't even sure what the model was doing and why. And there were times that led to all kinds of confusion, even panic. I'm thinking specifically, for example, in 2000, when the NASDAQ market collapsed. And there were several days when they were suffering bad losses, and they didn't know why. And so, listen, they have this ridiculously good track record, and there weren't many periods when they had losses, but there were some, and I write about them, there were all kinds of setbacks and real drama behind the scenes. And when they did suffer

Starting point is 00:17:31 losses, in some ways, it was more painful and confusing and chaotic than if you and I were to suffer losses because they didn't really know why they were suffering losses. It's one thing to, I don't know, be long, you know, oil and oil is down. And okay, you screwed up, you lost money, but at least you know why. It's more confusing when you're an executive, you're a trader, you're an employee of Renaissance technologies and you work for Jim Simons or you are Jim Simons and you're suffering losses and you don't know why because your model has learnt what trades to make often because they had been profitable. So they had to go back and work overtime and work into the night and into the morning figuring out why they were

Starting point is 00:18:17 suffering and they did figure it out. And basically, this one example, the model had taught itself to buy more NASDAQ stocks, largely because it was a momentum strategy, was relatively simple, more simple than they had imagined. In some ways, their model had got ahead of itself and was buying more NASDAQ stocks and continuing to buy more because historically into that period, it just had worked. So it was a simple momentum strategy. And the model was allocating more cash to it because it had been working. So that's a really simple example. But there were others, too, years earlier, they did interesting quasi-machine learning type strategies where they were building data for themselves. So they had collected all this data, and they were early at collecting it all

Starting point is 00:18:58 and cleaning it all, et cetera. But it wasn't enough. Basically, they were building data. So there were all these early attempts at machine learning. And I don't want to suggest that they devoted all their capital to these kinds of strategies early on, but over time they did, and they were real pioneers. The percent return, annual return of 66 percent, I mean, literally since 1988, is just mind-boggling. It's almost if you told somebody that on the street, they're not even going to believe it. So I'm kind of curious if some of those returns were captured early on, like performance now is less. They are in 2019, they're beating the market, not by a lot, but a little bit. And in recent years, they've continued to outperform.

Starting point is 00:19:43 It's not just that they do really well and they outperform. So they've been up like 30, 40% over the last four or five years. But it's not just that. It's the sharp ratio. They are not correlated to the market and they hardly have any down in months. Now, I do have to make the point that, and some people have made it to me, well, it's a little unfair to compare Jim Simons and his team to somebody like Warren Buffett because Warren Buffett manages a huge, remarkably big conglomerate at this point. And Jim

Starting point is 00:20:14 Simons, the fund we're talking about, the Medallion Fund, Jim Simon's Medallion Fund, it's big, but it's not as big as Buffett. And for consciously, so they're a cap to $10 billion. They don't compound their returns. They give back their profits every single year and go back to $10 billion. Earlier, it was smaller. It was $5 billion until about a decade or so. So you could say, well, it's not fair to compare somebody like Simons to the people I do compare them to in my book, Peter Lynch, C.V. Cohen, George Soros, and Buffett, because his key funds, the one we're talking about, is capped at $10 billion. And I would grant you that, it is also the case that they use a lot of leverage, so they get up to over $100 billion there. So it's not a small fund.

Starting point is 00:21:00 But yeah, they do cap that fund and return all their capital, which makes it a little bit easier for them. It's important to keep in mind that they don't make any outright bets ever. They don't look at, you know, Apple or Facebook, and we think that's going higher. Everything I do is about groups. So they are long, about four or five thousand stocks, and they're short about four or five thousand stocks. And everything is relationships. It's groups of stocks versus other groups. It's groups of stocks versus factors, groups of stocks versus an index. So it's very complex. They care a lot about the downside and hedging themselves. And that's why they've got such a crazy high sharp ratio and they don't really suffer too much in the way of losses. So if not the traditional type of

Starting point is 00:21:44 investing that you and I think, oh, I'm going to bet where gold and silver are headed as groups of investments in relationship to each other. And maybe there's a lesson there that we're all, myself included, really focused on the wrong thing. You know, again, where specific index is going or where a specific stock is going. And maybe it's a better idea to look for these relationships among groups of stocks. Before reading your book, I thought Simons was similar to Redallio's approach, because he's so rule-based in solving the market. But what I found remarkable is how different the approaches are.

Starting point is 00:22:20 Redallio is much more into risk parodies and holding positions much longer. Now, with that being said, were there in the areas where you saw similarities between Simons and Redalio? I thought they'd be more similar, and I thought Simons and his colleagues would be more similar to people like AQR and Cliffassness and others. They're not, as you say, they've got a very unique approach. They're about computer models coming to decisions. It's not about individuals. There are many individuals working there. They're some of the brightest scientists and mathematicians in the world, but they have just a unique approach.

Starting point is 00:22:57 They don't hire anybody from Wall Street. Ray Dalio does. The similarity is, as you suggest, they're both rules-based. And I'm a big, huge believer, and that's one of my big takeaways from this process, and it's almost sad to some of the biggest decisions governing our lives by the Federal Reserve, certainly in the White House, in government, they're made by individuals using their gut instinct and intuition and judgment. And that just leads you to mistake after mistake, emotional mistakes,

Starting point is 00:23:29 greed, fear when it comes to investing. And what Simons does, and what Dalyu does is they embrace a rules-based, and they don't overrule and override their system. It's about systems and not stores. And if you think about the mistakes that we've all made as an investor, the biggest ones over the past few years, it's about falling in love with a story, be it Serenos, be it we work, be it Uber. And that just leads us down the wrong trail. And that's what Jim Simons and Renaissance technologies is all about the scientific approach and embracing a system and rules as opposed to instinct and judgment. So my next question was kind of kind of hit at what your biggest lesson learned was.

Starting point is 00:24:15 I'm kind of curious, what was the second thing you learned through writing this book that just astounded you the most? Well, there are a lot of things that astounded me. The other lesson for the individual investor is to steer clear of, Renaissance. You don't want to invest short term because the most sophisticated investors out there are digesting data that you and I have no clue about. There's this whole new world of alternative data. They just digest it faster than anybody else. They trade faster. They can see the patterns that we can't see. The only way to really profit is to do what they're not doing,

Starting point is 00:24:55 and that's to trade longer. And it doesn't mean necessarily years, but you can't compete with these people. You don't want to be on the other side. I mean, they take advantage of our mistakes. Often it's the mistakes of larger investors. It can be the mistakes of smaller investors, the behavioral errors that we all make.

Starting point is 00:25:14 And it's true of even sophisticated people. They panic. They get greedy. I mean, I was blown away that late last year, I write an anecdote in my book. Look, Jim Simons is on vacation with his wife. The market's collapsing, and he starts getting nervous. And he calls up his private wealth guy, the guy who runs his family office and says,

Starting point is 00:25:35 he shouldn't we be buying some protection here? And this is the last guy in the world he would have thought to be panicking because he's worth $23 billion. And it's all by turning the decisions over to computers. And the point being, the lesson here is it's hard to do that. Even for mathematicians, even for scientists, even the people that have built their career. He's 81 years old, and he's made his billions on turning his decisions over to computers. The point being, it's hard to do that.

Starting point is 00:26:02 And yet one needs to, if you're going to be in that world, and the only way to compete with them is to do what they're not doing. And that's to find some edge. And that's something that may be some industry that you and I know better than others. There are very few of them, and we as investors get way too cocky and confident about it. But that's one of the key lessons, I think. Interesting. So let's talk more about how Simon's trade. You said earlier that the average holding period might be a day.

Starting point is 00:26:33 Which holding period range would you give 95% of the trades? Would it be between, say, eight hours to two days, perhaps? Eight hours and three days or so, I would say, as most of their trading, they internally, and they call it moments to months. So they will go sometimes months. They look like a fast trading firm, and people even on Wall Street think they are faster trader than they are, largely because they do have a ridiculously high turnover and they're trading all day long, but people internally tell me that's mostly to put on trades or to take

Starting point is 00:27:10 them off and they break up their trades. So what looks like their high frequency, it's not. But yeah, they're mostly trading for these short periods. but yeah, they don't really go longer than a month or two. When they find something that's really profitable, they don't want others. Their competitors to discover them, so they hide their trades and they're very sneaky and clever and cunning about it. Let's take a quick break and hear from today's sponsors.

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Starting point is 00:30:55 This is a paid advertisement. All right. Back to the show. So in your book, you talk about Bob Mercer and Peter Brown being responsible for Renaissance key breakthroughs. Can you explain to our audience a little bit about them and their contributions to this model that they developed? Sure. And while my book is about Jim Simons, at least a lot of it is about Jim Simons, it's just as much about the people around him who are responsible for the real

Starting point is 00:31:24 breakthrough. Simons is a brilliant mathematician, scientist, he understands all the quant, he managed, he hired a lot of these people, but he himself didn't develop the algorithms. It's the groups of colorful, unusual. So Bob Mercer is among them. He was hired by Simons from IBM. He was doing speech translation and other kind of speech work at IBM. And he's a computer programmer, a scientist who is quite fascinating and odd in some ways. He hums all day long. He whistles to himself, usually classical tunes. He for years ate the same lunch every day, peanut butter jelly, and tuna fish. And he, as you also say, is responsible along with Peter Brown and other scientists from recruited from IBM, they're responsible for the key breakthrough. So until around 1996,

Starting point is 00:32:17 Simons and his team were making a lot of money in every market bought equities and they could not figure out how to profit from stocks. And that was fine for a lot of people at the firm, but it wasn't for Simons. He wanted to get really big and you couldn't get big as a firm. You couldn't manage billions and billions unless you could profit from stocks. There are just limited markets in commodities, let's say if you think about things like soybeans and some other more narrow markets and some currencies are really limited. So to manage billions and to put on leverage, you really had to figure out equities and they couldn't. And I took Mercer and it took Brown to do it. They, and along with some younger individuals, a guy named David Magerman,

Starting point is 00:33:00 I write about how Magerman found a glitch in their system. And it looked like it should be really profitable on paper. They seem to be, have developed something really profitable. And yet they time and time again, they couldn't figure it out. And Jim Simon's almost pulled a plug on their effort. And then Magerman found a real glitch. One of their numbers wasn't updating. It was static. It was an S&P 500 number. And the greatest investment firm in history almost wasn't able to create this remarkable system if it wasn't for this young programmer finding a glitch. Wow. Now, Gregory, please allow me to read this quote from your book that I think that our listeners will find absolutely fascinating. Simons and his colleagues generally avoid predicting pure stock moves. It's not clear that any expert or system can predict how individual stocks move over the long term or even the direction of financial markets.

Starting point is 00:33:57 What Renaissance does is trying to anticipate how stocks move relative to other stocks, factor models or to an industry. I just find that so interesting and so counterintuitive to how Wall Street invests. Yeah, this is exactly right. And that was one of the things that kind of blew me away. And they're just approach in general. This is very different. So, you know, one could read my book and say, well, heck, these guys scored 66% a year

Starting point is 00:34:27 on average this 1988. It must suggest that it's possible. and it must suggest that there are more inefficiencies than you would imagine. But I really take the opposite lesson that even they, even a firm with over 90 PhDs, and they're not just PhDs. Everybody on today on All Street says,

Starting point is 00:34:46 oh, I've got a PhD working for me. These are the people that ran departments at a major universities, and Simons is able to woo them and lure them and get them working on his system. So these are the top brains. they would be remarkably well-respected departments in it of themselves, and they were just set up as academic departments.

Starting point is 00:35:06 And yet they, again, only get it right 51% of the time. And even they are kind of a little bit wary about their ability to keep it going. They seem to be able to. But I can't come away with the book. One of the lessons is don't think you can do this at home and don't think there are so many inefficiencies out there. They're limited inefficiencies. They do exist.

Starting point is 00:35:27 And don't get me wrong. And Jim Simons has made that point to me and others that he doesn't believe in the efficient market theory. But be humble about it and be aware that there aren't as many inefficient as you might think. And yes, as you said in your question, they just do it with a different approach. And they see things that you and I are missing. So they're able to do it. But it's hard to don't try this at home kind of thing.

Starting point is 00:35:53 So what's the one thing that Jim Simons does better than everyone? else, and you have to exclude making money and mathematics. I would say he's actually his great quant, but he's also as good as a manager and a builder. So he's got this real great touch for hiring people. They look for talent, sort of like an NFL GM and a draft who will draft the best player available. Just looking for talent. They'll draft him. And that same thing with Simons, they just want really super smart people. Then when they get there, they don't even really have anything for them to do necessarily or specifically laid out. They say, go figure out a way to improve our code. And that brings in how they're managed. More than any other company I'm aware of, more than anybody in Silicon Valley,

Starting point is 00:36:42 they get people to work together because it's an open system internally. Outside, they're not allowed to talk to anybody, but internally, they cooperate, they're very collegial. Everybody is access to their code, even the more junior employee, because if they can see a way to improve on it, they're invited to, they're encouraged to. And as a result, people run into problems, they get frustrated, they can't figure out a way to improve some part of their code. So somebody else will see that and we'll try to help. And so they work really collegially. And that's Jim Simons and he creates incentives internally. He rewards people. If you're cleaning data, for example, it's not the sexiest job in the world, but at Renaissance it is. And you're rewarded. And you can

Starting point is 00:37:23 make millions or tens of millions if you're really good at it. So you can learn as much from his management techniques, I think, as from his investment strategies. Well, Gregory, thank you so much for coming here on The Investors' podcast. We absolutely love the book, and I'm sure that all our listeners would enjoy it too. And for people listening to this, the name of the book is The Man Who Salt the Market, How Jim Simons launched the Quant Revolution. Gregory, where can people learn more about you and your book. Sure. So I'm on Twitter. I'm on LinkedIn and I love compliments, constructive criticism, even criticism. Some of my best sources are people that were snarky and read a piece I did and pointed out mistakes I met and I learned from it. So I'm eager to hear from people if they

Starting point is 00:38:09 want to reach out. Well, we can't thank you enough. This was really a lot of fun to chat with you. Oh, likewise. Nice to be here. All right, guys. So at this point in time in the show, we'll play a question from the audience and this question comes from Brandon. Hey President Stig, this is Brandon from Vancouver, British Columbia, Canada. I've just finished the intelligent investor and the Buffett's books courses and I wanted to say thank you for the wealth of knowledge that I've gained from both courses. My question today is in regards to the differences and similarities between index funds and ETFs.

Starting point is 00:38:42 I've had a difficult time finding a side-by-side comparison of the two investment vehicles and was hoping to get more clarification. also in your mind which is a better investment. Thank you for your time and keep up the great work. Brandon, that's a great question and I can easily understand why you're confused. Very often when people talk about ETFs and index funds, they are using them interchangeably. But they're certainly not the same. So let's first talk about what is an ATF.

Starting point is 00:39:12 So an ETF is an investment vehicle to invest in multiple securities. And a common misperception is that an ETF always tracks the performance of the stock market. And while it can certainly do that, you can also track bonds, small-cap stocks, or anything you can basically think of. And I think the reason for this misconception is that the most popular ETFs track what we typically refer to as the stock market. For instance, the S&P 500 in the US or the market for all stocks in the world. Okay, so let's talk about what is an index fund.

Starting point is 00:39:46 You have an index and you also have an index fund tracking that index for nearly every financial market in existence. So in the U.S., the most popular index fund tracks the S&P 500, so 500 of the biggest public trading companies. But several other indexes are widely used as well. You know, you can think of the Russell 2000 made up a small cap company stocks or the Barclays Capital U.S. aggregate bond index. So an index is really just something that is made up that follows different criteria depending on what is of interest. Now, if you buy an index fund,

Starting point is 00:40:27 you can use an ETF, but you can also track an index through a mutual fund. Now, that takes me to your second question when you ask about what is the better investment. And please don't get me wrong here, but I don't think it's the right question to ask. Rather, I would say that if you decide to invest in an index fund, you would, for tax reasons, prefer to invest in the index fund through an ETF rather than a mutual fund. And the difference is mainly due to legislation, where an ETF is generally a more tax-efficient way to invest, since it doesn't have to pay tax on the capital gains it makes whenever rebalances the portfolio. Another important reason is that NTF is cheaper to operate, so the expense ratio is just lower than on a mutual fund.

Starting point is 00:41:14 And since it's cheaper to operate, the investment return will also be correspondingly higher. Now, the inner mechanics are less important than understanding that generally should favor an ETF or an index fund. And then you ask, which index fund should you buy? Okay, so the first thing to note is that you never buy the actual index, but you're buying into a fund manager building a portfolio whose holdings mirror the security of a particular index. For instance, if you buy an index fund tracking the S&P 500, the fund manager will buy stocks in 500 different companies. And the performance of the fund

Starting point is 00:41:50 will not just be the same as the index, but how well the manager tracks that index. Now, passive managed index funds are designed to track that index automatically. So the so-called tracking area is typically very small. In that case, it's really more a question of picking the right index rather than picking the right fund manager. Now, this is opposed to an active managed ETF that by definition is created to outperform the selected index. For instance, our friend Tobias Kailail, who we had on the show here multiple times, he has an ETF called CIG, and he uses an active strategy on selecting stocks with the aim of outperforming the MSCI US Large Cap Index. So here you are buying the skill set of Tobias, the fund.

Starting point is 00:42:37 manager rather than the benchmark index. You are required to have a benchmark index, but it's really not so important if you're actively managed because by definition you want to do something that is different. Now, beforehand, this over to Preston. Thank you so much for your question. And on top of the access to TEP finance and intrinsic value course that Preston will talk more about later as a thank you for asking the question, I also created a course about investing in ETFs that we'll make sure to give you access to and really hope you find relevant. Well, now, Stig, how am I supposed to top that response? Brandon, great question.

Starting point is 00:43:18 I think Stig knocked this one out of the ballpark. I really don't have anything else to add. For asking such a great question, we're going to give you free access to our intrinsic value course for anyone wanting to check out the course. Go to tip intrinsic value.com. That's tip intrinsic value.com. The course also comes with access to our TIP finance tool, which helps you find and filter undervalued stock picks.

Starting point is 00:43:43 If anyone else wants to get a question played on the show, go to Asktheinvestors.com, and you can record your question there. If it gets played on the show, you get a bunch of free and valuable stuff. All right, guys, that was all that Preston and I had for this week's episode of the Ammasters podcast. We see each other again next week. Thank you for listening to TIP. To access our show notes, courses or forums, go to theinvestorspodcast.com.

Starting point is 00:44:08 This show is for entertainment purposes only. Before making any decisions, consult a professional. This show is copyrighted by the Investors Podcast Network. Written permissions must be granted before syndication or rebroadcasting.

We Study Billionaires - The Investor’s Podcast Network - TIP273: Billionaire Jim Simons' Quant Revolution w/ Gregory Zuckerman

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