With Gregory Zuckerman, Author of The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, Contributor to The Wall Street Journal
If you earned a 7% annual rate of return, a $50,000 investment would grow to be worth $200,000 in 20 years. So, what happens if your money earns a 20% annual return for 20 years? Well, then your $50,000 would grow to $1.9 million in 20 years. But now imagine if your investments earned 66% annually. In that case, your starting stake of just $50,000 would, after 20 years, be worth $1.2 billion—that’s billion with a “b”. There’s only one money manager Steve’s ever heard of who has managed that trick, Jim Simons.
To learn the inside story of this one-of-a-kind investor, Steve spoke with Greg Zuckerman, the author of a book on Simons, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Greg is a special writer at The Wall Street Journal and a three-time winner of the Gerald Loeb Award, the highest honor in business journalism.
Before Jim Simons
When Jim Simons first turned his skills as a mathematician to investing in the early 1980s, fundamental analysis was still king in the financial markets. This was back when personal computers were first starting to appear (anyone remember the Commodore 64?), and technical analysis was not nearly as popular as it is now. The term “quant trading,” which refers to computerized trading strategies crafted from quantitative analysis of large amounts of data, didn’t even exist. Most technical analysts still had to go through the laborious process of drawing out charts by hand.
Jim Simons, who held a Ph.D. in mathematics and was one of the most renowned mathematicians in the world and head of the math department at Stony Brook University, decided he wanted to do something different as well as make more money. He left his prestigious teaching position and turned his attention to the financial markets.
A Unique Approach To Technical Analysis
Jim Simons decided to approach investing by creating mathematical models using algorithms and automated trading, the kind of thing that’s commonplace at big investing firms nowadays but that was completely unheard of back then. Greg explained, “His colleagues, his family, they all thought he was making a huge mistake. Why give up a promising career in mathematics to try to beat the market? Back then, there were basically two lines of thought. (A), that the market was random (the random walk theory). And (B), that, well, maybe if you were somebody special like Warren Buffett or Peter Lynch, you might be able to get an edge by talking to management and endlessly examining balance sheets and such.”
But Jim Simons didn’t buy into either one of those market theories. What he decided to do was look for patterns in market price movements. If he could identify patterns that repeated, then he figured he could make big money in the markets. What he did differently from everyone else was to collect more pricing information, more data, than anyone else had ever even thought of doing. Basically, Jim was using “big data” about 20 years before anyone else was. He’d go to the Federal Reserve in Manhattan and get price data going back decades, for all kinds of different assets. Later on, he was even analyzing data going back centuries, even back to the 1700s.
One of the first trading models he developed by studying patterns in this detailed data told him to buy potato futures contracts. Without even realizing that’s what was happening, he was buying potatoes so aggressively that he ended up cornering the market. Unfortunately, that brought a bunch of panicked regulators down on him, who forced him to liquidate a lot of his positions at a huge loss. So that first attempt at mastering the markets didn’t turn out so well.
Finding The Key To Investing Riches
All in all, it took about 12 years of trying different things—12 years when he would make a lot of money, but then lose a lot of money—before Jim finally found the secret that would make his Renaissance Technologies’ hedge fund a consistent winner, producing unbelievable returns, returns higher than 50% per year.
The key to success turned out to be identifying short-term patterns in the markets. What he eventually found were correlations within the markets broken down into small time periods. He began looking at things like, “If gold goes up on a Friday afternoon, what does it do Monday morning?” Looking at those kinds of correlations is where he finally found the repeating patterns from which to build reliable trading models. Jim and his team divided up each trading day into five-minute bands. And then they’d look at, for example, the market action in the 43rd band in relation to the action that had occurred in the 23rd band, looking for correlations that consistently repeated.
Steve noted that, by 1997, Jim and company were managing about $7 million, and by then they had formulated a three-step process they used to identify the consistently repeating patterns they were looking for.
Steve also stated that, “I think it’s interesting that when it all comes down to it, the thing that really made the difference was intuition, which is a human behavior or human attribute that computers don’t have, at least not yet.” Greg agreed with Steve’s point, explaining that one of the key differences that has made Jim’s hedge fund much more successful than those of his competitors is that, regardless of what the models and algorithms say to do, according to Greg, “There are a few times in crises when Jim becomes really conservative and he pulls back and reduces his positions. People within the firm give him a lot of credit for saving the firm in those moments of crisis.” In the end, it’s Jim’s intuition that has kept the firm making big profits and avoiding big losses.
That’s what really makes the difference because Jim has gone through some periods where he lost $80 million in one day and $90 million the next day. But the amazing thing about his overall performance is that, despite those disastrous days, we’re talking about a firm that has, incredibly, managed to average a 66% annual return since 1988. That’s for more than 30 years now.
Keeping Your Head When All Those Around You Are Losing Theirs
Greg made a key observation about another one of the factors in Jim’s success, the fact that his trading models tend to work best when other trading strategies are failing. He said, “The models do really well when everybody else panics. A lot of the reason why Jim Simons is the greatest moneymaker in modern financial history is that he does really well in market turns and market panics. So, in 2008 when everyone else was losing lots of money, Jim’s firm almost doubled their money. Partly because they have this data that maps out market panics going back hundreds of years, they kind of have a better sense than the rest of us as to when the panic will end and when to get back in the market.”
Steve summed up Jim Simon’s success by pointing out that his focus on analyzing huge amounts of data (doing what is now known as “quant trading”) was ahead of its time and just the right approach for the world we live in. He mentioned the incredible rate at which the amount of available data is growing, saying he’d recently seen a statistic indicating that, “More information has been collected in the last two years than in all of history before then.” Greg agreed with Steve’s point, telling listeners, “Today, everyone is trying to do what Jim Simons has done. About 31% of all trading is done by quant traders. Fundamental investors are struggling. It’s hard to beat the market and that’s partly because the market is just more efficient than ever before. There’s more information flying through, more and more data, and it’s hard for an individual to digest.” He added, “There are risks to this kind of trading, but it’s an approach that’s better able to handle the massive data and the fast pace of investing today.”
If you want to read the full, amazing story of Jim Simon’s transition from math teacher to possibly the most successful investor in history, pick up Greg Zuckerman’s book, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. And you can get more investing information by reading Greg’s articles in The Wall Street Journal.
Disclosure: The opinions expressed are those of the interviewee and not necessarily of the radio show. Interviewee is not a representative of the radio show. Investing involves risk and investors should carefully consider their own investment objectives and never rely on any single chart, graph or marketing piece to make decisions. Content provided is intended for informational purposes only, is not a recommendation to buy or sell any securities, and should not be considered tax, legal, investment advice. Please contact your tax, legal, financial professional with questions about your specific needs and circumstances. The information contained herein was obtained from sources believed to be reliable, however their accuracy and completeness cannot be guaranteed. All data are driven from publicly available information and has not been independently verified by the radio show.
Steve Pomeranz: If your money doubles every 10 years when you earn a 7% annual rate of return—and I’m talking in estimates here about a 7% annual rate of return—a $50,000 investment would be worth $200,000 in 20 years. What happens when your money earns a 20% annual return for 20 years? Well, essentially it doubles every three and a half years. So 50,000 would grow to $1.9 million. Now, this is fantastic. It’s an incredible rate of return to get 20%, and it’s essentially the Warren Buffett wealth creation story because he’s earned rates just north of 20%. But what if your investments earned 66% annually? What would that be worth in 20 years? Well, the answer is your 50,000 would be worth $1.2 billion. Now that’s another category altogether, and there’s only one money manager that I’ve ever heard of that has ever earned these cosmic returns. Here to tell us about that person is Gregory Zuckerman. He’s the author of The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution.
Greg is a special writer at The Wall Street Journal and a three-time winner of the Gerald Loeb award, which is the highest honor in business journalism. Welcome to our program, Greg.
Gregory Zuckerman: Hey, great to be here.
Steve Pomeranz: First of all, it’s a great book, it’s a story well told. Congratulations. And it’s an unlikely story in the sense that it took one of the world’s preeminent mathematicians to leave a prestigious career in academia and then doggedly pursue mathematical investing. Take us back to the early sixties and the story of Jim Simons.
Gregory Zuckerman: Sure. So Jim Simons is a remarkable individual. Even if he had never invested a dime in the market, I would argue that he’d be worthy of a book. He grew up middle-class in the suburbs of Boston in Newton, Massachusetts and was always fascinated by mathematics. He had a passion for it, everybody told him not to pursue it. Back then, you really couldn’t make much money doing math, but he decided to go where his heart led him and that was math. He got a PhD. He started teaching at MIT and at Harvard, and he wanted to make a little more money and do something a little different, and he left to break code for the government, work for the United States in a think tank in Princeton. It’s really interesting work, fighting the Russians there. And then he led a math department at Stony Brook. And then he decided to go leave it all, leave academia, even though he was the one of the most acclaimed mathematicians of his generation and still is among the most important geometers for the past hundred years. He left it all to try to figure out the market.
It was a challenge, he wanted to make a lot of money, he thought he could be the one, and he thought he’d do it with a different approach—not your usual intuition, judgment, and talking to executives, looking at annual reports, financial information. He wanted to do it with creating models, creating automated trades, using algorithms, all that kind of stuff they do in Silicon Valley today and all across Wall Street. He was trying to do this in the 1980s, and then it turn a corner in the 1990s and he was off to the races.
Steve Pomeranz: All right. Let’s talk about the environment back then. First of all, there is this little joke in the book: what’s the difference between a mathematics PhD and a large pizza? And the answer is, a large pizza can feed a family of four. So, yeah. By the way, I told that to a mathematics professor not too long ago, got a good laugh out of it. So, yeah. So back then, and really almost to the present day, investing was done in a way that we call fundamentals. Looking at a company and its balance sheets, looking at the business and how it compared to its competitors. Ben Graham wrote a book about it in the 40s and Warren Buffett found him and found the book and became his Bible. And a lot of successful careers and a lot of money has been made using this fundamentalist idea of investing. Some call it value investing. Back when Jim Simons started, nobody was thinking about having computers run models, and actually his colleagues thought he was really making a big mistake. Right?
Gregory Zuckerman: Yeah, that’s exactly right. His colleagues, his family, they all thought he was making this huge mistake. Why give up a promising career in mathematics to try to beat the market when it’s really impossible to do so? That’s kind of the thinking. I mean back then, there were two lines of thought. A, that the market was sort of random. Random walk. You can’t really beat the market. And B, well if you may be some people like the Warren Buffett’s and the Ben Grahams and later the Peter Lynch’s by talking to management and examining balance sheets and such. And he just had a very different perspective, Jim Simons. He in some ways is a cousin of technical analysis.
In other words, he decided to look for patterns and the assumption being if I can detect some patterns beneath the surface of the market and it could be the equity market, could be currencies, first he started with currencies. And the assumption being that those patterns will continue, then I’m on to something. As long as those patterns repeat in the future then, and I bet on them, I can make money that way.
Steve Pomeranz: Well, technical analysis tries to do that, but it has been shown to be quite ineffective. Perhaps it was the fact that there wasn’t enough data to be able to crunch in order to start to see patterns. Reminds me of looking at weather patterns and how random they seem, but if you have the data, you may start to see identifiable trends. And how did he do in the early years with this?
Gregory Zuckerman: Yeah. And to your point, what he decided to do, what Jim Simons decided to do is collect data, collect more pricing information than anyone else in history ever before. And he was doing the obvious things like looking in the newspaper and charting everything down there. But he also sent people to the Federal Reserve in lower Manhattan to jot down prices for all kinds of different commodities and other tradable assets going back decades, and then later they got even centuries. They had information, they have a lot of information going back to the 1700s. So they, in some ways, were the early data scientists, everybody talks about big data today, the importance of big data. And Jim Simons was doing that years before anyone had that concept, but it took them a while to figure out how to make money from it all.
So they developed an early trading system, a model as they call it, automated to tell them what to buy, what to sell. And early on, it told them to buy a lot of potatoes, contracts for Maine potatoes as it were, and they cornered the market inadvertently. They didn’t realize what they were doing. Simons let his machines go, they followed the instructions of the machines and they bought up too many potato contracts and regulators called them up in a panic. Jim, what do you think you’re doing here? You’ve cornered the market. And they forced them to sell the contracts and they ended up losing a lot of money. So it was too early. The systems weren’t there yet; the technology wasn’t there. So for a while, Jim Simons tried to invest like everybody else, reacting to the news, anticipating where interest rates and earnings and currencies were going, and he made some money. He lost some money, made a lot of money in gold and silver, then lost some and he realized it wasn’t for him and he literally had pain in his stomach.
When he made money, he felt great; when he lost money, as he says, “I felt like a dope.” And he realized he had to go back to the automated systems, creating models, developing algorithms this whole new way. And he was a pioneer in this whole approach.
Steve Pomeranz: You know that he had hired a person by the name of Leonard Baum, who was a currency trader and was very successful. But he would suffer, I mean he would earn tremendous rates of return, but he would suffer these huge losses too because—there’s a quote in there, “He had the buy low part down, but he could never bring himself to sell high.” And which is a common problem from all traders if you really think about it. So by 1984 Simon was losing millions of dollars and he stopped all trading. Why was that?
Gregory Zuckerman: Yeah, that’s true. So Lenny Baum is really one of the greats when it comes to mathematics. And Simons was really impressive in how he was able to lure these superstars in math and science to his farm, including Lenny Bauman. But Baum quickly made money using sort of his instinct and his intuition and didn’t want to spend much time on the whole creating models and using mathematics. And as you suggest, he was really good at buying at the right time, and he was not very good at selling and he likes to sell, he’s a pretty optimistic guy. And it was driving Simons crazy. So they went back and forth and it was over. I write in the book about it. Basically, for about 12 years, Simons and various mathematicians try to figure out the best approach to trading the market, to beating the market. And it really took them until 1990 when they turn the corner and they discovered a new approach and then they’re off to the races.
Steve Pomeranz: Yeah, what was that?
Gregory Zuckerman: Well, the new approach was basically short-term trading. They realized they had a lot of data and they had more data about few day periods, even shorter than that. And they generally and even until today in 2020, they will trade holding—their holding period is about two days on average. Sometimes it’s less, sometimes it’s more internally. They say it’s moments to months, but it’s a whole new way of trading where it’s not high frequency, as some people think of these fast traders, it’s not quite that. It’s two-day period, two-day pattern. Then that’s where they decided to look for correlations within the market and when you have more data you can find more correlations between more investments. If gold goes up on a Friday afternoon, what does it do Monday morning? What about silver? What if this currency or commodity is going up on Friday morning?
Does it also do so Friday afternoons? They broke up the day’s trading into bands, as they call them, and eventually there were five-minute bands. So you look at the 43rd band versus the 23rd band. You wouldn’t necessarily think there’s any correlation, but sometimes there is, and these are scientists, so they don’t look for spurious correlations; they look for correlations that’ll repeat consistently, and that’s a whole different way of looking at things. It’s a cousin of technical analysis, but it’s done in a much more sophisticated manner.
Steve Pomeranz: By 1997, they had $700 million they were managing, but he was really frustrated. As a matter of fact, he pointed out that they weren’t keeping up with the competition and he mentioned Bernie Madoff as the competition. Of course, a few years later he became suspicious and he pulled his money out of Madoff’s fund. But by 1997, he settled on a three-step process to identify these anomalous patterns, as you mentioned. Make sure the anomalies were statistically significant and then see if the pricing behavior could be explained in a reasonable way. But then still this type of investing would go in and out of favor and then something called long-term capital happened. Tell us about that.
Gregory Zuckerman: Yeah, that’s true. So basically, around 1996, 1997, the Jim Simons and his colleagues had a breakthrough. Until then, they had made good money and currencies and commodities and bond futures. They could not figure out how to profit in the world of stocks. And people within the firm said, okay, big deal Jim, we’re doing well with this other stuff so we can figure out equities, who cares? But Jim Simons was compelled to be one of the greats, if not the greatest money-maker trader investor in modern day history and he wanted to be a billionaire. And you couldn’t do that unless you manage a lot of money and you couldn’t manage a lot of money unless you figured out equities. And he hired some people from IBM, they took a whole different approach. I write about it in a book and they basically figured out equities. But as you suggest, in 1998 some other types of big hedge funds who had similar, not identical, but some similar approaches, started running into huge trouble. Long-term Capital was one of them.
Long-term Capital was founded by a group of PhDs, just like Jim Simons and his colleagues, and they were more economists than they were mathematicians and scientists, but they were looking to try to use models and that’s exactly what Jim Simons did. So here they were doing really well late 1990s, Jim Simons and his colleagues look and like everything was easy, and they had figured it out, and yet, lo and behold, the market imploded because of the Long-term Capital, and Simons got nervous. He’s like, well, geez, these guys ran into these deep problems, maybe we will too. So they figured it out, they realized they honed what they were doing, they tweaked it a little bit and they had a different approach than Long-term Capital. So they were reassured to some extent, Long-term Capital, in some ways bought into their own models a little too much.
They doubled down when they had problems and Simons never does that kind of thing. He believes in his models and he doesn’t usually override them, but there are a few times in crises when he becomes really conservative and he pulls back on the lever and he reduces his positions. And I wrote about it in the book, but people within the firm give him a lot of credit for saving the firm in those moments of crisis.
Steve Pomeranz: Well, I think it’s interesting that when it all comes down to it, the thing that really made the difference, maybe you’re suggesting, was intuition, which is a human behavior or human attribute that computers don’t have, at least not yet.
Gregory Zuckerman: Yeah, it’s a good point though,right. They don’t usually override the models and they avoid using intuition judgment. But there are times when Simons did apply it and it helped them a lot.
Steve Pomeranz: I’m speaking with Gregory Zuckerman, the author of The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. So then, moving further, we’re now in 2000, they’re managing about $4 billion, they were making 150,000 to 300,000 trades per day. Holy cow! How do you even do that?
Gregory Zuckerman: You do it with computers and with technology and with an automated system. You can’t just figure out where things are going and make that many trades and, to be clear, what they were doing is not, as I said earlier, high frequency. It kind of looks like high frequency because they’re trading so much, but what they do is they put on their position slowly and in small chunks. So it looks like fast trading, but often it’s to try to make sure other people don’t realize what they’re doing and they go into the market and they get excited. Let’s say about, I don’t know, technology shares and how they’re going to outperform versus industrial companies, and they buy up a lot of technology shares. Well, that’ll move the market, that I’ll move prices, other people will realize what they were doing. So a lot of what they do is to slowly edge into trades and to keep in mind to let your audience have a better understanding.
They don’t predict where things are going, what they do is look for relationships and changes in relationships. In other words, a group of stocks versus another group of stocks, a group of stocks versus an index, group of stocks versus a factor. And that’s what they do constantly all day long they made adjustment, it’s called market neutral. That way if the market goes down or it goes up, it doesn’t really affect how they’re performing. They’re all about the relationships between investments.
Steve Pomeranz: Yeah, they had had those years. I mean the 2000 tech bubble, they lost 90 million in one day, then 80 million the next day and they had prior to that, never lost more than $5 million. So it seemed, at that point, you write in the book, that the machines seemed out of control and then the losses were now approaching 300 million.
Gregory Zuckerman: Yeah, that’s exactly right. They’ve had a few periods in their history, very few, but a few which are were disastrous. And just to give you a sense of their returns, I don’t think we’ve talked about them yet. This is a firm that’s had 66% a year annually since 1988, so they haven’t had many bad periods, but when they do, they’re not just bad, it’s a real crisis. Why? Because let’s say you and I have a portfolio and it goes down. It’s not fun, it can be worrisome, but we at least know why it’s going down. Let’s say you own a lot of, I don’t know, pharmaceutical shares, Warren becomes president or polling improves and so pharma stocks go down. You know why you’re not happy, but you know why, the difference with Jim Simons and his colleagues, these quantitative investors as they call them, quants, a lot of it is machine learning.
The machine, the system learns on its own and buys and sells without anybody telling them what to do. And so when they’re losing money, when they’re losing a lot of money in a few days, it’s scary. It’s downright scary because you don’t know why. They didn’t know why they were losing that much money. And that’s what happened.
Steve Pomeranz: I think that would be the most frightening part of all of this, is you’re losing hundreds of millions a day and you don’t know why.
Gregory Zuckerman: Yeah, can you imagine? Yeah. It takes them a while to figure it out too.
Steve Pomeranz: Yeah. And so then you have to decide, do we stop everything? Do we sell all the positions? I mean, the market’s going down, there’s things that happen that the models don’t cover. In 2008, for example, the models that people were using assume that there was always going to be a buyer when you went to sell. But the buyers disappeared and so prices just fell into an abyss. I don’t think that was in the models. And they actually suffered during ’08 but they had a, they pulled it together because, at that point, I guess they were confident enough to trust the model.
Gregory Zuckerman: Yeah. And frankly, their models do really well when everybody else panics. I mean a lot of the reason why Jim Simons is the greatest moneymaker in modern financial history is he takes advantage of the behavioral mistakes that we all make. The panic, the fear, the greed, and they do really well in market turns and market panics. So in 2008, when everyone was losing lots of money, they almost doubled their money. So there are small periods when their models have to adjust. But in general, partly because they have this data that maps out market panics going back hundreds of years, they kind of have a better sense of when they will end, when they’ll continue, when to get back in the market than the rest of us.
Steve Pomeranz: So bringing it up to current date, I mean it’s a great book and it’s 300 pages, so we can’t cover everything in the book. So let’s wrap this up with the idea. so it seems to me that, at this point in time, it looks like this investing strategy or the world of investing has come full circle. Way back when fundamental investing was important and technical investing was considered to be kind of a side show, kind of a little bit of a circus, maybe a little help, but not much. Now fundamental type of investing is really very much out of favor. And we see that most money managers, we’ve seen this for a while, don’t outperform the index. So the whole idea of investing on a fundamental basis and trying to beat a plain old average index seems to have gone by the wayside and then up come these quant funds with even more and more data. I mean the data is doubled.
There’s a statistic in there about the data doubling in the last two years or something like that. More information has been collected on the last two years than in history, I don’t have it in front of me. You probably know what I’m talking about.
Gregory Zuckerman: Yeah.
Steve Pomeranz: The increase in data is exploding and also the mathematical abilities of computers to be able to crunch those numbers. So where are we headed in your view right now? Are these quant funds the place to be?
Gregory Zuckerman: So, as you suggest, it’s a new era, today everyone is trying to do what Jim Simons has done. And that’s partly why I wrote this book, to understand why the world is shifting the way it is. And today about 31% of all trading is done by quant traders. And as you also say, fundamental investors are struggling. It’s hard to beat the market and that’s partly because the market is just more efficient than ever before. There’s more information flying through, there’s data as you say, that it’s hard for the individual to digest. Comes much more quickly and it’s also hard to get an advantage, to get an edge even if you’re a professional. It’s a good thing the Feds have leveled the playing field in a lot of ways, it’s hard to get tipped off by somebody on Wall Street, tipped off by a company. There are all kinds of things that they’ve done and you get really well paid—overpaid I would argue—investors on wall street, I talked about them, I write about them at The Wall Street Journal all day long, who are struggling for the last few years.
And at the same time, the people like Jim Simons continue to do well. His firm using these quantitative methods, now they’re not for everyone and they don’t always work. They’ve done a little bit better than everybody else in terms of this , the sector has, but Simons and his colleagues still are beating the market. They were up 40%, 50% last few years annually. So you don’t want to go overboard, it’s not for everybody. There are downsides, there are risks to this trading, quants do go through their crises like everybody else, but it is an approach that’s better able to handle this massive data coming through the fast pace of investing today and can deal with the fact that it’s hard to get an edge, it’s just really for professional investors. So this is where it’s at, which is why I wrote this book.
Steve Pomeranz: Let’s leave it there. My guest, Greg Zuckerman, author of The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Great book. Thank you so much for joining me, Greg.
Gregory Zuckerman: Oh great to be here.
Steve Pomeranz: And maybe you guys know that my mission is always to educate you and to bring you the most up to date information on what’s going on in the marketplace to help you. We love your questions too. You may have some questions about what we discussed today. If anything occurs to you, write them down and go to our website, which is stevepomeranz.com where we welcome your questions. So if you have any question, whether it’s about investing or it’s about taking care of your kids or your grandkids or yourself, your retirement, your future, write us a question. Go to stevepomeranz.com, go to the contact section and let us know how we can help. That’s stevepomeranz.com.