Preston Pysh 1:07
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 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.

Gregory Zuckerman 1:35
Sure. So yeah, Jim Simons is the greatest modern day money-maker world of finance has seen. And I use that word, a phrase, “money-maker,” 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 and his fund is called the Medallion Fund, and their 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, and judgment. So it’s a fascinating story, and it’s one that I’ve always wanted to tell. It 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 money-maker, but he’s also the most secretive one Wall Street has ever seen. He and his colleagues don’t talk to Press, etc. 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 and said, I want to write this book. And they said, “No, we’re not going to talk. He’s not gonna talk to anyone. If he ever decides to talk; decide, they’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.” 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. So he but he was very generous with his time talking about other parts of his life. And yeah, he has a fascinating life even before and after he ran his firm.

Stig Brodersen 3:56
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?

Gregory Zuckerman 4:12
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. Over the last fifty, a hundred years. He grew up in Boston, and in Brooklyn, and then, then in Newton. Got his PhD first; went to MIT. Then, he got a PhD in mathematics at Berkeley; came back and taught at Harvard and at MIT. And yes, some of the math he did later on in his career; still decided regularly both in the world of mathematics, but also in physics, he had, he didn’t realize it, but some of his work would have impact; a broad impact. So but he also spent time as a code breaker for the government. This was during the Cold War going up against the Russians at the time. We as a country, we’re on a bit of a losing streak. 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, in mathematics, and code breaking even before he decided to focus on investing. And, and frankly, people in his world were a little disappointed, when he went in and tried to conquer the market because he was so well-respected in the world of mathematics and academia.

Preston Pysh 5:38
So you allude to the idea that Simons was doing a lot of technical analysis early on, when he was 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.

Gregory Zuckerman 6:20
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 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, “You can’t really estimate where the market is. You can’t tell.” And in instinctive level, I think Simons 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 isn’t necessarily something that, 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 dismissed 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 in a not very sophisticated way, but they decided to say, 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.

Stig Brodersen 8:04
So in your book, you tell the 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?

Gregory Zuckerman 8:25
Well, he went back and forth a lot. So early on, he said that 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 Tencent, and all that kind of stuff. Early on, that’s what they tried to do. But it didn’t work, and as you suggest, partly is because the computer power was in 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 inadvertently, and basically just another sign of how they weren’t doing a great job early on. So he shifted to more traditional type of 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 the late 80s. 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 in two bands; bands of five minutes. He started 20 minutes, and went down much shorter, and they would look for correlations. They looked for in all kinds of markets; various commodity markets; currencies; bond features. 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 notice patterns, and they bet on. The assumption was that they would repeat.

Preston Pysh 10:20
So one of the things that I distinctly remember in the book is percentage of getting the trade correct was around 50%. And I can’t remember what the exact percent was. It was around 50%. When I read that, I immediately thought, “Okay, well, he must have a really, really firm grasp on the Kelly criterion.”

Gregory Zuckerman 10:39
Okay, 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 it right all the time. But they basically get it 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 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 Ellen Burleighcamp, who literally studied with Kelly as an academic. But when I ran it past some people internally, they kind of say, “Yeah, we tried using Kelly’s 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 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, not all of them in my book. There are so many secrets out there that I’m still trying to figure out and understand.

Stig Brodersen 11:53
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 how Simons was one of the very first to apply machine learning. But most of the machine learning that we understand today has really just progressed in a major way over the past, call it five to ten years. How was Simon able to get the returns that he had, basing before the technology existed?

Gregory Zuckerman 12:32
Well, what they did was, and this was pretty 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, they have this ridiculously good track record. And they weren’t many periods, when they had losses, but there were some and I write about them. There are all kinds of setbacks and real drama behind the scenes. And when they did suffer 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. This is one thing to, I don’t know, belong, you know, oil and oil is down, and okay, you you screwed up. You made–you lost money, but at least you know why. It’s more confusing, when you’re an, an executive; you’re a trader; you’re an employee of Renaissance Technologies. You worked for Jim Simons or you are Jim Simons, and you’re suffering losses and you don’t know why because your model has learned what trades to make often because they had been profitable. So they had to go back and work overtime and, you know, work into the night and into the morning, figuring out why they were suffering. And they did figure out and basically, this one example, the model had taught itself to buy more NASDAQ stocks largely because it was a momentum strategy. It was relatively simple. More simple than they had imagined. In some ways, their model, it 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 two 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 and cleaning it all, etc. But it wasn’t enough. Basically, we’re 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.

Preston Pysh 15:04
The percent return; annual return of 66%, I mean, literally, since 1988 is just mind-boggling. It’s almost, if you told somebody that on the street, they’re not even gonna believe it. So I’m kind of curious if some of those returns were captured early on like performance now is less.

Gregory Zuckerman 15:24
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 it. It’s not just that they do really well, and they outperform. So they’ve been up like 30-40% over the last, you know, four or five years. But it’s not just that. It’s the Sharpe ratio. They are not correlated to the market, and they hardly have any down months. Now, I do have to make the point that–and, 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 Simons, the fund we’re talking about–the Medallion Fund; Jim Simons’ Medallion Fund, it’s big. But it’s not as big as Buffett. And for consciously, so they’re capped at $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 him to in my book: Peter Lynch, Stevie 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 a $100 billion there. So it’s not a small fund. 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 they do is a group, 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 Sharpe ratio, and they don’t really suffer too much in the way of losses. So if not the traditional type of investing that you and I think, “Oh, I’m going to bet where gold and silver are headed.” It’s groups of investments in relationship to each other. And maybe there’s a lesson there that we are 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 relate–these relationships among groups of stocks.

Stig Brodersen 17:57
Before reading your book, I thought Simons was similar to Ray Dalio’s approach because he’s so rule-based in solving the market. But what I found remarkable is how different the approaches are. Redalio’s much more into risk parities and holding positions much longer. Now, with that being said, were there any areas where you saw similarities between Simons and Ray Dalio?

Gregory Zuckerman 18:21
I thought they’d be more similar. And I thought Simons and his colleagues will be more similar to people like AQR, and Cliff Asness, and others. They’re not as you say, they’ve got a very unique approach. They’re about computer models coming to kid 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. 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, that it’s almost sad that some of the biggest decisions governing our lives by the Federal Reserve; certainly by–in the White House; in government, they’re made by individuals using their gut instinct, and intuition, and judgment. And that’s–just leads you to mistake after mistake; emotional mistakes; greed; fear when it comes to investing. And what Simons does and what Dalio does is they embrace a rules-based, and they don’t overrule and override their system. It’s, it’s about systems and not stories. And if you think about the mistakes that we all make as an investor; the biggest ones over the past few years, it’s about falling in love with a story: be it Sarah Knows; be it WeWork; 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.

Preston Pysh 20:00
So my next question was kinda–kind of hit at what your biggest lesson learned was. I’m kind of curious, what was the second thing you learned through writing this book that just astounded you the most?

Gregory Zuckerman 20:12
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, no clue about. It’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. 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 new mistakes of smaller investors. The behavioral errors that we all make, 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: 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, “Should we be buying some protection here?” And this is the last guy in the world, you 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 careers. He’s 81 years old. And he’s made his billions on turning in decisions over to computers. The point being, it’s hard to do that. 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, and they’re 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.

Stig Brodersen 22:13
Interesting. So let’s talk more about how Simons trade. You said earlier that the average holding period might be a day. Which holding period range would you give 95% of the trades? Would it be between say, eight hours to two days, perhaps?

Gregory Zuckerman 22:31
Eight hours and three days or so, I would say is 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 bank 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 them off, and they break up their trades. So what looks like they’re high frequency, it’s not. But yeah, they’re mostly trading for these short periods. But yeah, they, 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.

Preston Pysh 23:26
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?

Gregory Zuckerman 23:41
Sure, and while my book is about Jim Simons, at least a lot of is about Jim Simons, it’s just as much about the people around him, who are responsible for the real breakthroughs. Simons is a brilliant mathematician and scientist. He understands all the quants; 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, he’s a computer programmer; a scientist, who is quite how fascinating and odd in some ways. He hums all day long. He whistles 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 breakthroughs. So until around 1996, Simons and his team were making a lot of money in every market, but 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, it’s to put on leverage. You really had to figure out equities, and they couldn’t. And it took Mercer, and it took Brown, to do it. They and along with some younger individuals, a guy named David Magerman. 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 Simons 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.

Stig Brodersen 26:02
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. 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.

Gregory Zuckerman 26:39
Yep, that’s exactly right. That was one of the things that kind of blew me away. And, and their just–approach in general is very different. So, you know, one could read my book, and say, “Well, heck! These guys scored 66% a year on average since 1988. It must suggest that it’s possible, and it must suggest that there are more inefficiencies in the market 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 in Wall Street is oh, I’ve got a PhD working for me. These are the people that ran departments at major universities, and Simons is able to woo them and lure them, and get them working on his system. And so, these are the top brains. Indeed, they would be remarkably well-respected departments in and of themselves if they were just set up as academic departments. 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 a book–one lesson is, don’t think you can do this at home. Don’t think there are so many inefficiencies out there. They’re limited inefficiencies. They do exist! 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 inefficiencies as you might think. And, 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, you know, it’s hard. Don’t-try-this-at-home kind of thing.

Preston Pysh 28:20
So what’s the one thing that Jim Simons does better than everyone else, and you have to exclude making money and mathematics?

Stig Brodersen 28:29
I would say, he’s actually–he’s 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 in a draft will draft the best player available; just looking for talent. They’ll draft them, 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, they get people to work together because it’s an open system internally. Outside they don’t talk to anybody. But internally, they cooperate. They’re very collegial. Everybody has access to their code, even the more junior employee because if they can see a way to improve on it, they’re invited to it; 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 will 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. You can 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 Investor’s Podcast. We absolutely loved 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 Solved the Market: How Jim Simons Launched the Quantum Revolution.” Gregory, where can people learn more about you and your book?

Gregory Zuckerman 30:20
Sure, so I’m on Twitter. I’m on my 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’ve made. And I learned from it, so I’m eager to hear from people if they want to reach out.

Preston Pysh 30:37
Well, we can’t thank you enough. This was really a lot of fun to chat with you.

Gregory Zuckerman 30:41
Oh, likewise! Nice to be here.

Stig Brodersen 30:44
All right guys, so at this point in time in the show, we play a question from the audience. And this question comes from Brandon.

Brandon 30:50
Hey, Preston and 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. I’ve had a difficult time finding a side-by-side comparison of the two investment vehicles. I 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.

Stig Brodersen 31:21
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 ETF. So an ETF is an investment vehicle to invest in multiple securities. And the 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 the thing the reason for this misconception is that the most popular ETFs track what we particulary 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. You have an index, and you also have an index fund tracking that index for nearly every financial market in existence. So in the US, the most popular index fund tracks the S&P 500. It’s 500 of the biggest public traded companies, but several other indexes are widely used as well, you know, you can think of the Russell 2000 made up of small cap company stocks, or the Barclays Capital US 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, you can use an ETF, but you could also try it 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 the 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 it rebalances the portfolio. Another important reason is that an ETF is cheaper to operate. So the expense ratio is just lower than on the mutual fund. And since it’s cheaper to operate, the investment return will also be correspondingly higher. Now, the inner mechanics are less important than understanding the generally should favor an ETF or an index fund. And then you ask which index funds should you buy? Okay, so the first thing to note is that you never buy the actual index. But you buy 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 will not just be the same as the index, but how well the manager tracks the index. Now, passive managed index funds are designed to track that index automatically. So the so called tracking areas typically very small, in that case is 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 Carlisle, who we had on the show here multiple times, gets an ETF called CIG, and he uses an active strategy in selecting stocks with the aim of out performing the MSCI US large-cap index. So here you are buying the skill set of Tobias, the fund manager, rather than the benchmark index. You are required to have the benchmark index. But it’s really not so important if you actively manage because by definition, you want to do something that is different. Now before I hand this over to Preston, thank you so much for your question. And on top of the access to TIP Finance and the Intrinsic Value Course that Preston will talk more about later as a thank you for asking a question. I also created a course about investing in ETFs that we’ll make sure to give you access to, and we really hope you find relevant.

Preston Pysh 35:38
Well now, Stig, how am I supposed to top that response? Brandon, great question. I think Stig knock this one out of the ballpark. I really don’t have anything else to add. For asking such a great question, we’re gonna give you free access to our Intrinsic Value Course. For anyone wanting to check out the course go to tipintrinsicvalue.com. That’s tipintrinsicvalue.com. The course also comes with access to our TIP Finance tool, which helps you find and filter undervalued stock picks. 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.

Stig Brodersen 36:19
All right, guys! That was all that Preston and I had for this week’s episode of The Investor’s Podcast. We see each other again next week.

Outro 36:26
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