If I asked you to name the greatest investor of all time, you would likely immediately think of Warren Buffett. And there is good reason for that. Buffett has put up 20% annual returns since 1965, which is about double what the market returned over the same period. If you didn’t name Buffett, you might think of Peter Lynch, which would be another good choice. Lynch steered the Magellan Fund to a 29% return from 1977 to 1990, which is even better than Buffett’s return, albeit over a much shorter period.
If you are looking for the investor with the best returns ever, though, you might be surprised to find out that neither of them is even close. From 1988 to 2018, Jim Simons guided the Medallion Fund to a 39.1% annual return. And that is not even the most impressive part. Medallion charged a fee of 5% of assets over that period and a performance fee that ranged from 20% to 40%. The fund’s gross return before fees was over 66% per year. There is no investor who has come even close to that.
How was a mathematician with
no formal stock market training able to produce those kinds of returns? That is
the subject of the new book The
Man Who Solved the Market by Gregory Zuckerman. If you haven’t
bought the book already, I highly recommend that you do. It not only offers the
details of how Simons built the Medallion fund into the most successful fund
ever, but also provides a behind the scenes look at the sometimes-rocky road
that he and his team followed to get there.
We are very fortunate that Greg
has agreed to join us this week for our Five Questions interview.
Just a note before we begin. This interview was transcribed from a phone conversation, so please forgive any grammatical errors. I also went over the usual five questions since I couldn’t help asking some follow up questions along the way.
Jack: Thank you for taking the time to talk to us. And congratulations on an amazing book. I have always been intrigued by Simons and have read everything I could find about him over the years, but you have managed to uncover so much new information here that almost everything in the book was new to me.
Since our blog is primarily focused on investment strategy, I wanted to start by asking about the approach employed by Medallion. When a fund produces this kind of track record, it immediately triggers a variety of questions about how they did it. Obviously, most of what they do is not public, but I was wondering if you could offer some high-level detail about what you uncovered about their investment strategy?
Greg: Starting really high, I’d say they are a very short-term oriented technical trading shop. They do
Jack: There was a quote in the book that talked about how being right 51% of the time, but doing it with close to 100% certainty can be worth a lot of money over time. Is that a good depiction of how their investment strategy works?
Greg: It is. And what keeps them humble among other things is the fact that they don’t get it right all the time. They see themselves to some extent; at least early on the founders and people that were there at the beginning really saw themselves as something akin to a casino where you get it right more than 50% of the time, but not that much more than that, and you trade frequently. Now, keep in mind they’re not high frequency. People often confuse them with a high frequency shop. They’re not high frequency, But it’s pretty frequent. They look more high frequency than they are because they’ll layer into trades rapidly and break up the trades and it looks like it is rapid trading, but it’s not high frequency. But yeah, that’s exactly right. They get it right a little bit more than 50% of the time, but they know, not with certainty, but close to certainty. And they also know they’re really good at other parts of the equation, like their impact on the market and slippage. They know when to trade. They know how to trade. They know the impact of their trading better than most everybody and risk management as well. A guy like me, I was focused on the signals, and it’s more than that. People internally have kind of always emphasized to me that it’s way beyond the signals. It’s the impact on the market and how to get size, leverage, that kind of thing.
Jack: Yeah, it seems like they have mastered the total picture. Obviously when you produce 66% per year, you must have all your bases covered. One of the interesting things for me in reading the book is they don’t seem to be very reliant on knowing the reason why a specific approach works. In factor investing, one of the first things you learn is that for a factor to work going forward, it should have some kind of economic basis or reason it should work. But they don’t seem to worry about that as much as other investors. Do you think that is a fair conclusion?
Greg: To some extent. So it’s a little bit overstated. It’s not that they trade without thinking about the why. They will put on some allocations though without knowing. If it’s scientifically provable, if they can demonstrate it, then they will put it on without knowing the why. But then they’ll still work to figure out the why and only when they understand the why will they allocate more capital to it. So yes, they will do trades without understanding them. But they’re not oblivious to the dangers of that. They’re scientists. They understand a phenomenon could be happening that could be coincidental. So yes, if there’s a statistically valid phenomenon they’ll put the trade on, but in small size until they understand it.
Jack: There is probably no more competitive business than asset management. If a fund is able to gain an edge over their competitors, it is usually short lived, as other funds find a way to identify what they are doing and take advantage of it themselves, which typically eliminates the edge. The most interesting part of the Medallion story to me is how they have been able to avoid that. With so much money at stake, competitors have done everything they can think of over the years to try to figure out what Renaissance is doing. And there have also been departures from the firm over the years, some of whom tried to manage money on their own. But no one has been able to come close to the returns of Medallion. How have they been able to maintain these returns and no one has been able to figure it out and replicate it?
Greg: Yeah, it’s a great question. It’s an important one. I think the answer is – and it does make sense when you think about it – first of all, they don’t hire the same types of employees as everybody else. They don’t hire from other firms. Generally speaking, they don’t like Wall Street, and even the academics they hire are different. So everyone on the street today talks about the PhDs that work for them. Everyone, even the fundamental shops guys will point to, “Oh, over in that corner is our PhD.” So everyone’s got a PhD. But, first of all, RenTech is a firm dominated by PhDs. They’ve got about 100 of them out of the 300 employees. But it’s not just PhDs. So other firms will have science, math, and other scientists with PhDs. The guys at Renaissance dominate their fields. It’s like David Donahu who ran Artman at Stanford. So not just a Stanford PhD. It’s the head of the department. And the people that they hire are people that generally speaking, they’re not really out to get rich. They are out to solve problems. And they get excited about an intellectual challenge. So I mean, listen, once they’re there, they’re out to get rich. The money captivates everybody. But before they get there, a lot of these people are just looking for a new challenge. As a result, you have academics who are at the top of their field, and they come there and they make a ton of money. So they don’t need to go to another firm. Yes, I do write about some examples. A few of these foreign scientists did leave and that was a mess, but those are exceptions to the rule. Generally speaking, the average person who goes to RenTech doesn’t really care so much about getting rich or about investing even. They get there, get rich, start to fall in love with the money to some extent, but then they’re so wealthy when they leave, they’re not going to go to another firm. They could. They could go to one. It happens. But generally speaking, they have made so much money that they’ll go back to academia, they’ll go to some nonprofit, do some philanthropy, do something fun. So I’m not sure if it’s a conscious decision on Jim’s part. Frankly. I think it was partly fortuitous, but because they do so well, they don’t risk losing people to other places on Wall Street. If you’ve done really well at RenTech for like five or ten years, why are you going to go work at another hedge fund or even start your own? You’ve already made your millions. And you didn’t really set off to make millions anyway. So you’ve made your millions and now you want to go do something else. Its really a fortuitous thing, but he can be really open with his IP internally and they are more so than any other firm. Everybody can see the IP, everyone can see the code. Even junior people within RenTech. And the danger there is obviously, they can leave and know all that stuff, but the IP, generally speaking, remains there because these people, when they leave, they don’t go to a rival.
Jack: I guess if you’ve made as much money as you ever wanted and you’ve also worked for the best firm on Wall Street, what’s the point of going somewhere else?
Greg: Exactly. And again, I don’t think you set out to create something like that, but it is what resulted in a perfect scenario for them.
Jack: I wanted to ask you about the common edges in investing and how they relate to RenTech. If you want to gain an edge in investing, you typically would try to get information others don’t have (an informational edge), you would analyze that information better than everyone else (an analytical edge), or you would take advantage of others behavior or behave better than others yourself (a behavioral edge). The first two edges in particular are very difficult to achieve these days. Information flows very freely and most providers that sell information are willing to sell it to anyone who will pay. And the rise in computing power and skill level on Wall Street has made analyzing something better than other market participants very difficult. But despite that, it seems like RenTech has been able to consistently maintain both an informational and behavioral edge. Would you agree with that? Which of those edges do you think has been more important to them?
Greg: So honestly I have a better perspective on the years up until 2010. So I would say until about 2000 one of their many advantages was data. And they had better data. And not just better, cleaner data. They were cleaningdata before anyone knew what it was, in the eighties and nineties. That whole process that everyone understands today, the importance of cleaning data, they were doing it early on. And they have better data. They’ve got data going back to the 1700s. And so for a long time they had much better data and more accurate data than everybody else and that was a true advantage. Today, I would argue, that’s not as much of an advantage because people have caught up. It’s easier to get this kind of data. People understand the importance of making it accurate and clean. And people generally say they use the old stuff just out of curiosity. Someone’s got some project of some kind and they look at the stuff from the 1800s or whatever, and let’s say some panic market panic or some corner of the commodity market back then. How did investors react? So I think the data advantage for a long time helped them. They also were doing machine learning before everybody else, which helped them, again, until recently. So their commitment, and it took a while, as you read, Jim himself was kind of like, this is a black box. I can’t figure it out. I don’t know why the model is buying what it’s buying. And it did lead them in to all kinds of problems. The cornering of the potato market and that kind of thing. So they did have a commitment to this approach before everybody else, machine learning and their model, and that’s an advantage. Today, the best companies in general have these dynamic mathematical models. You look at like 10 cent and Amazon and Netflix, etc. So they were early there. That was a true advantage. That stuff all gave them a huge advantage for many years. But today, though, everybody can get alternative data and RenTech was early at looking at that kind of stuff. But again, I think other people today have access to some of that same data. Maybe they have all of it. I do believe that RenTech has better data than almost anybody. They just consume. They’re a sponge. Any kind of data, they will take, they will buy, and they test everything. Anything you could think of is the way it’s described to me. So they’re as good as anybody else, if not better than everybody else. But what people will truly credit is their engineering. Meaning that other firms, like an AQR, Two Sigma, they have many models, many portfolios With Renaissance, it’s generally one. I mean, you could argue that maybe the equities will differ from the non-equities, but they have few models and systems and there’s a real advantage vs. other firms to have one system they can input stuff into. And internally they give that a lot of credit. Just the engineering of creating a single system that has all of these kinds of inputs in terms of how to hedge, when to buy, how to buy. Putting it all together into one model. So it’s not so much that they have better data today than ever. Its how they can amalgamate it and consume it and apply it better.
Jack: Many of the readers of our blog are individual investors and obviously you have to be careful about learning too many lessons from a firm like this because the things that they do would not be advisable for individual investors to do and would be impossible to do even if they wanted to. But despite that, I am wondering if you think there are some lessons an individual investor can take from the RenTech story?
Greg: I think the lesson here is that you don’t want to compete with them and you can’t. If you’re going to do this, you can’t do it short term because they’ve mapped it all out. They’re aware of what the hidden patterns are. They take advantage of you when you panic and when you get greedy. But they have never been very successful as longer-term investors, even six months out, which would suggest to me that’s where people should be focusing on. You don’t want to be run over by them or competing with them. You don’t want to be on the other side of them at the poker table.
But when it comes to the longer-term kind of stuff, I do think that there are still opportunities. It’s also the case that reinforces that you want to find some niche. As you said, you can’t really get a competitive advantage, information advantage, broadly speaking. But I think that there aresmall approaches, small corners of the market. You know, let’s say you are, I don’t know, whatever, a biotech expert, you’re familiar with a product, you’re excited about a product, or you’re skeptical about a product that’s coming. The RenTech guys can’t do that stuff. They don’t try to do that stuff. And there’s still opportunities there. And different niches, distressed debt and others, where quants like RenTech can’t really play. I think that the largest lesson to me, be it for investors or just for citizens is the dangers of believing in stories, falling in love with stories, and the importance of at least having some rules-based system that you hardly ever veer off from. And an individual can do that too. As long as you develop a system that works and you stick to it, and that’s what they do too, this is a total reminder of the importance of that approach as opposed to decision making with your gut and with your intuition, which as you know, frankly is what they do in the White House and elsewhere. So to me, it’s a reminder of the importance of developing and relying on a system.
Jack: That makes complete sense. Just one more question. If you look at the work of Michael Mauboussin, he has shown that the fact that there are fewer active managers has actually made the game tougher because it is the weaker hands that are being eliminated. And the last few years have seen exponential growth in the role that computers play in investing. Not only are human managers relying on them more, but we now have reached the point where computers are doing more of the thinking themselves. It would seem on the surface like these factors would be a serious threat to the ability of Medallion to produce future returns similar to the ones they have historically. Based on your study of them, do you think they will be able to produce the same kind of returns in the future as they have in the past?
Greg: So I’m personally skeptical. Its probably my nature, but partly because how can you keep it going? One the reasons I’m skeptical is because the nature of the market has changed as you suggest. And a lot of their games of the past have been taking advantage of investors. Early on, they joke about it being dentists and later they thought it was maybe institutions, but if there’s no weak hands left, its harder to take advantage. But also the nature of the market has changed. So as people go to passive index funds, etc, the market itself is changing. So I would think that they would have fewer opportunities. Their counter argument to me is that as long as things change gradually, then they can adjust. And there’s still enough others out there that they can take advantage of, and people are still panicking and getting greedy. And the proof’s in the pudding and they’ve outperformed this year as well. So, so far so good. And it’s important to remember it’s not just performance. It’s a crazy Sharpe Ratio too. So I’m personally skeptical, but so far they’ve been able to do it.
Jack: Yeah, it will be the ultimate test. If they can continue these kinds of returns in this type of environment, it will be the ultimate validation of their system. Not that they need validation given what they have done so far. Thank you again for taking the time to talk to us today. If investors want to find out more about you and the book, where are the best places for them to go?
Greg: By email, Gregory.Zuckerman@wsj.com, on Twitter at @GZuckerman or on LinkedIn at: gregory-zuckerman.
