The Man Who Solved the Market by Gregory Zuckerman — Book Summary, Notes, and Takeaways
High-Level Thoughts
Fascinating read about the most profitable financial market participant in history. Insights into their process, Simons' long-winding road to becoming the most successful hedge fund manager in history, and an epic chronicle of Bob Mercer, the most influential person behind the Trump election.
Chapter Three
One expert who advised Simons and Baum on their trading made so much selling pounds that he named his son Sterling.
The last straw came when he was caught running an unauthorized, high-stakes trading operation out of his dorm room. Friends pooled their cash and handed it to Hullender, who purchased stock options before a market rally in 1978, turning $200 into $2,000 in a matter of days. Soon, everyone in the dorm wanted in on the operation, throwing money at Hullender, who began repackaging stock options purchased through a brokerage account at Merrill Lynch and reselling them to eager students.
“Sometimes I look at this and feel I’m just some guy who doesn’t really know what he’s doing,” Simons said.
In 1982, Simons changed Monemetrics’ name to Renaissance Technologies Corporation, reflecting his developing interest in these upstart companies. Simons came to see himself as a venture capitalist as much as a trader. He spent much of the week working in an office in New York City, where he interacted with his hedge fund’s investors while also dealing with his tech companies.
In his eighties, Baum enjoyed walking two miles from his home to Witherspoon Street, near Princeton University’s campus, stopping to smell budding flowers along the way.
Chapter Four
“If you make money, you feel like a genius,” he told a friend. “If you lose, you’re a dope.”
Simons considered the possible influence of sunspots and lunar phases on trading, but few reliable patterns resulted. Straus had a cousin who worked at AccuWeather, the weather forecasting company, so he made a deal to review Brazilian weather history to see if it could predict coffee prices, another effort that proved a waste of time. Data on public sentiment and the holdings of fellow futures traders also yielded few dependable sequences.
Ax began staging intense weight-loss competitions and became determined to trounce his officemates. Once, just before the initial weigh-in, Ax packed on several pounds gorging on melon, calculating that he’d quickly shed the new weight, since melon is laden with water. Another time, Ax furiously biked to work in the sun, hoping to lose weight, arriving so drenched in perspiration that he placed his underwear in an office microwave to dry; minutes later, the microwave burst into flames as a staffer ran for a fire extinguisher.
Ax concluded it was time to bring in someone with experience developing stochastic equations, the broader family of equations to which Markov chains belong. Stochastic equations model dynamic processes that evolve over time and can involve a high level of uncertainty. Straus had recently read academic literature suggesting that trading models based on stochastic equations could be valuable tools.
A bit later, René Carmona, a professor at nearby University of California, Irvine, got a call from a friend. “There’s a group of mathematicians doing stochastic differential equations who are looking for help,” the friend said. “How well do you know that stuff?” A forty-one-year-old native of France who later became a professor at Princeton University, Carmona didn’t know much about markets or investing, but stochastic differential equations were his specialty. These equations can make predictions using data that appears random; weather-forecasting models, for example, use stochastic equations to generate reasonably accurate estimates.
Perhaps they could find instances in the remote past of similar trading environments, then they could examine how prices reacted. By identifying comparable trading situations and tracking what subsequently happened to prices, they could develop a sophisticated and accurate forecasting model capable of detecting hidden patterns.
“I can’t get comfortable with what this is telling me,” Simons told the team one day. “I don’t understand why [the program is saying to buy and not sell].” Later, Simons became more exasperated. “It’s a black box!” he said with frustration. Carmona agreed with Simons’s assessment, but he persisted. “Just follow the data, Jim,” he said. “It’s not me, it’s the data.”
Chapter Five
“When I heard MIT didn’t have a football team, I knew it was the school for me,” he says.
“My impression was that it was a game in which rich people play around with each other, and it doesn’t do the world much good,” Berlekamp says. “It still is my impression.”
Several years earlier, Kelly had published a paper describing a system he’d developed to analyze information transmitted over networks, a strategy that also worked for making various kinds of wagers. To illustrate his ideas, Kelly developed a method he had devised to profit at the racetrack. Kelly’s system proposed ideal bets if one somehow obtained enough information to disregard the posted odds and could instead rely on a more accurate set of probabilities—the “true odds” for each race.
Berlekamp came to realize that much of human interaction is colored by shades of gray that he sometimes found difficult to discern. Mathematics, by contrast, elicits objective, unbiased answers, results he found calming and reassuring.
Axcom’s model usually focused on two simple and commonplace trading strategies. Sometimes, it chased prices, or bought various commodities that were moving higher or lower on the assumption that the trend would continue. Other times, the model wagered that a price move was petering out and would reverse, a reversion strategy.
veteran analyst at Merrill Lynch named John Murphy had published a book called Technical Analysis of the Financial Markets, explaining, in simple terms, how to track and trade price trends.
To keep those investors in the fold, Simons shut down Limroy in March 1988, selling off the venture investments to launch, together with Ax, an offshore hedge fund focused solely on trading. They named their hedge fund Medallion, in honor of the prestigious math awards each had received.
Chapter Six
Junk-bond king Michael Milken pocketed over one billion dollars in compensation between 1983 and 1987 before securities violations related to an insider trading investigation landed him in jail.
Berlekamp hadn’t worked on Wall Street and was inherently skeptical of long-held dogmas developed by those he suspected weren’t especially sophisticated in their analysis.
“If you trade a lot, you only need to be right 51 percent of the time,” Berlekamp argued to a colleague. “We need a smaller edge on each trade.”
Monday’s price action often followed Friday’s, for example, while Tuesday saw reversions to earlier trends. Laufer also uncovered how the previous day’s trading often can predict the next day’s activity, something he termed the twenty-four-hour effect.
“People persist in their habits longer than they should,” he says.
“Jim, if you think we’re going to be up 80 percent, and I think we can do 30 percent, you must think the company is worth a lot more than I do,” Berlekamp said. “So why don’t you buy me out?” Which is exactly what Simons did. In December 1990, Axcom was disbanded; Simons purchased Berlekamp’s ownership interest for cash, while Straus and Ax traded their Axcom stakes for shares in Renaissance, which began to manage the Medallion fund.
Chapter Seven
The roots of Simons’s investing style reached as far back as Babylonian times, when early traders recorded the prices of barley, dates, and other crops on clay tablets, hoping to forecast future moves.
“That which has been is that which shall be . . . there is nothing new under the sun.”
To this day, Gann analysis remains a reasonably popular branch of technical trading.
Tsai launched his own firm, the Manhattan Fund, a much-hyped darling of the era. Tsai built a war room featuring sliding and rotating charts tracking hundreds of averages, ratios, and oscillators. He kept the room a frigid fifty-five degrees, trying to ensure that the three full-time staff members tasked with updating the figures remained fully alert and attentive.
They usually were tasked with building models to place values on complicated derivatives and mortgage products, analyze risk, and hedge, or protect, investment positions, activities that became known as forms of financial engineering.
During the 1980s, Professor Benoit Mandelbrot—who had demonstrated that certain jagged mathematical shapes called fractals mimic irregularities found in nature—argued that financial markets also have fractal patterns.
Mandelbrot’s work would reinforce the views of trader-turned-author Nassim Nicholas Taleb and others that popular math tools and risk models are incapable of sufficiently preparing investors for large and highly unpredictable deviations from historic patterns—deviations that occur more frequently than most models suggest.
Bachelier’s thesis, describing the irregular motion of stock prices, had been overlooked for decades, but Thorp and others understood its relevance to modern investing.
Team members didn’t know a thing about the stocks they traded and didn’t need to—their strategy was simply to wager on the re-emergence of historic relationships between shares, an extension of the age-old “buy low, sell high” investment adage, this time using computer programs and lightning-fast trades.
Simons’s biggest competition figured to come from David Shaw, another refugee of the Morgan Stanley APT group.
One programmer, Jeffrey Bezos, worked with Shaw a few more years before piling his belongings into a moving van and driving to Seattle, his then-wife MacKenzie behind the wheel.
Chapter Eight
Sussman convinced his bosses to shell out $2,000 for an early-generation electronic calculator so he could quickly determine which bond was most attractive. Calculator in hand, Sussman made the firm millions of dollars in profits, a windfall that opened his eyes to how technology could render an advantage.
Laufer had finished his undergraduate work at the City College of New York and graduate school at Princeton University in two years each,
To make it all easier to digest, they had broken the trading week into ten segments—five overnight sessions, when stocks traded in overseas markets, and five day sessions. In effect, they sliced the day in half, enabling the team to search for repeating patterns and sequences in the various segments. Then, they entered trades in the morning, at noon, and at the end of the day.
The group coined a name for the difference between the prices they were getting and the theoretical trades their model made without the pesky costs. They called it The Devil.
“I don’t know why planets orbit the sun,” Simons told a colleague, suggesting one needn’t spend too much time figuring out why the market’s patterns existed. “That doesn’t mean I can’t predict them.”
Among those identified: loss aversion, or how investors generally feel the pain from losses twice as much as the pleasure from gains; anchoring, the way judgment is skewed by an initial piece of information or experience; and the endowment effect, how investors assign excessive value to what they already own in their portfolios.
“Humans are most predictable in times of high stress—they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past . . . we learned to take advantage.”
Simons had long been driven by two ever-present motivations: proving he could solve big problems, and making lots and lots of money.
Chapter Nine
As Fidelity came to dominate mutual funds, it began sending young analysts to call on hundreds of companies each year. Lynch’s successors, including Jeffrey Vinik, used the trips to gain their own, entirely legal, information advantage over rivals.
Each day, Gross wore open-collared, custom-made dress shirts with a tie draped loosely around his neck, a style adopted after vigorous exercise and yoga sessions left him overheated and unwilling to knot his tie once in the office.
Stanley Druckenmiller was one of the traders on the ascent. A shaggy-haired Pittsburgh native who had dropped out of a PhD program in economics, Druckenmiller was a top-performing mutual-fund manager before taking over George Soros’s billion-dollar hedge fund, the Quantum Fund.
“We can teach you about money,” Patterson explains. “We can’t teach you about smart.”
“We suffered socially and psychologically for being right,” says Aaron Brown, a member of the emerging coder crew who became a senior executive of the quant-trading world.
In mathematical terms, Brown, Mercer, and the rest of Jelinek’s team viewed sounds as the output of a sequence in which each step along the way is random, yet dependent on the previous step—a hidden Markov model. A speech-recognition system’s job was to take a set of observed sounds, crunch the probabilities, and make the best possible guess about the “hidden” sequences of words that could have generated those sounds.
Bayesians will attach a degree of probability to every guess and update their best estimates as they receive new information. The genius of Bayesian statistics is that it continuously narrows a range of possibilities.
Mercer got to the office at six o’clock in the morning and met Brown and other colleagues for lunch at 11:15 a.m. Mercer consumed the same thing almost every day: a peanut-butter-and-jelly or tuna sandwich packed in a reusable Tupperware container or a used, folded brown paper bag, which fellow researchers interpreted as a sign of frugality.
Mercer and Brown helped mentor Goldsmith, which she appreciated. But Mercer also shared his opinion with her that women belonged at home, taking care of children, not in the working world. Brown, whose wife had been appointed head of public health for New York City, viewed himself a progressive. He valued Goldsmith’s contributions and told her she was like a daughter to him. Yet, that didn’t stop Brown from allowing inappropriate jokes to flow amid the group’s locker-room
It turned out that the chess machine’s name made people think of something else—famed 1972 pornographic film Deep Throat, a movie at the forefront of what is known as the Golden Age of Porn (details to follow in my next book).
Chapter Ten
“I wanted to be the most indispensable person in the company,” he explains. Magerman tricked Renaissance’s systems administrator and created a backdoor way to launch his monitoring system.
Chapter Eleven
Simons pointed to the huge, steady gains that another investor was making trading equity options at his growing firm, Bernard L. Madoff Investment Securities. “Look at what Madoff is doing,” Simons told Patterson. The criticism grated on Patterson, who gave Simons a tart retort: “Maybe you should hire Bernie.” (A few years later, Simons would become suspicious of Madoff’s extraordinary results and pull money he had invested in Madoff’s fund. In 2008, Madoff would acknowledge running history’s largest Ponzi scheme.)
Extroverted mathematicians are the ones who stare at your shoes during a conversation, not their own.)
All staffers enjoyed full access to each line of the source code underpinning their moneymaking algorithms, all of it readable in cleartext on the firm’s internal network. There would be no corners of the code accessible only to top executives; anyone could make experimental modifications to improve the trading system.
“You know your formula from the beginning of the year. It’s the same as everyone else’s with just a couple of different coefficients, depending on your position,” says Glen Whitney, who was a top manager of Renaissance’s infrastructure. “You want a bigger bonus? Help the fund get higher returns in whatever way you can: discover a predictive source, fix a bug, make the code run faster, get coffee for the woman down the hall with a great idea, whatever . . . bonuses depend on how well the fund performs, not if your boss liked your tie.”
As a group of researchers chatted in the lunchroom in 1997, one asked if any of his colleagues flew first-class. The table turned silent. Not a single one did, it seemed. Finally, an embarrassed mathematician spoke up. “I do,” he admitted, feeling the need to offer an explanation. “My wife insists on it.”
Identify anomalous patterns in historic pricing data; make sure the anomalies were statistically significant, consistent over time, and nonrandom; and see if the identified pricing behavior could be explained in a reasonable way.
Recurring patterns without apparent logic to explain them had an added bonus: They were less likely to be discovered and adopted by rivals, most of whom wouldn’t touch these kind of trades.
Chapter Twelve
Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks. While most options are valued based on an individual stock or financial instrument, basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up—it’s like owning the shares without actually doing so. Indeed, the banks were legal owners of shares in the basket, but, for all intents and purposes, they were Medallion’s property. The fund’s computers told the banks which stocks to place in the basket and how they should be traded. Brown himself helped create the code to make
On Wall Street, traders often are most miserable after terrific years, not terrible ones, as resentments emerge—yes, I made a ton, but someone wholly undeserving got more!
In July 2003, Belopolsky and Volfbeyn delivered a bombshell: They were joining Millennium Management, a rival firm run by billionaire hedge-fund manager Israel Englander, who had promised them the chance to make an even larger fortune.
Chapter Thirteen
As a newly hired sales team began pitching the fund, named the Renaissance Institutional Equities Fund, or RIEF, they made it clear the fund wouldn’t resemble Medallion. Some investors ignored the disclaimer, considering it a mere formality. Same firm, same researchers, same risk and trading models, same returns, they figured. By 2005, Medallion sported annualized returns of 38.4 percent over the previous fifteen years (after those enormous fees), a performance that RIEF’s sales documents made sure to note. The new fund’s returns would have to be somewhat close to Medallion’s results, the investors figured. Plus, RIEF was only charging a 1 percent management fee and 10 percent of all performance of any gains, a bargain compared to Medallion.
Simons answered that his genes had been tested, and he had the unique ability to handle a habit that proved harmful to most others. “When you get past a certain age, you should be in the clear,” he said.
On the way home from the Bermuda trip, as staffers lined up to board the return flight, someone suggested they clear the way for a pregnant woman. Some Renaissance scientists refused. They didn’t have anything against the woman, but if she truly wanted to board early, she logically would have arrived early, they said. “It was like being with a bunch of Sheldons,” says an outsider on the trip, referring to the character on the television show The Big Bang Theory.
On the afternoon of Monday, August 6, all the quant traders were hit with sudden, serious losses. At AQR, Asness snapped shut the blinds of the glass partition of his corner office and began calling contacts to understand what was happening. Word emerged that a smaller quant fund called Tykhe Capital was in trouble, while a division of Goldman Sachs that invested in a systematic fashion also was suffering. It wasn’t clear who was doing the selling, or why it was impacting so many firms that presumed their strategies unique. Later, academics and others would posit that a fire sale by at least one quant fund, along with abrupt moves by others to slash their borrowing—perhaps as their own investors raised cash to deal with struggling mortgage investments—had sparked a brutal downturn that became known as “the quant quake.” During the stock market crash of 1987,
It turned out that the firm’s rivals shared about a quarter of its positions. Renaissance was plagued with the same illness infecting so many others. Some rank-and-file senior scientists were upset—not so much by the losses, but because Simons had interfered with the trading system and reduced positions. Some took the decision as a personal affront, a sign of ideological weakness and a lack of conviction in their labor.
The hotel’s concierge recommended the group try dune bashing, a popular form of off-roading in which four-wheel-drive vehicles climb and then slide down steep sand dunes at high speeds and dangerous angles, much like a desert roller coaster.
“I got it!” Simons yelled to Robert. “There’s a principle in physics: We can’t tip over unless the tires have traction! We’re in sand, so the tires have nothing to grab on to!”
The lesson was obvious: One could outsmart the market. It just took diligence, intelligence, and a whole lot of gumption. Simons’s quantitative models, nerdy mathematicians, and geeky scientists, while effective, were too hard to understand, their methods too difficult to pull off, most decided.
Chapter Fourteen
Stepping down from Renaissance gave Simons—who, by then, was worth about $11 billion—more time on his 220-foot yacht, Archimedes. Named for the Greek mathematician and inventor, the $100 million vessel featured a formal dining room that sat twenty, a wood-burning fireplace, a spacious Jacuzzi, and a grand piano. Sometimes, Simons flew friends on his Gulfstream G450 to a foreign location, where they’d join Jim and Marilyn on the super-yacht.
Simons relished tackling big problems. Soon, he was working with Marilyn to target two areas in dire need of solutions: autism research and mathematics education.
In some ways, the Renaissance machine was more powerful than before Magerman quit. The company now employed about 250 staffers and over sixty PhDs, including experts in artificial intelligence, quantum physicists, computational linguists, statisticians, and number theorists, as well as other scientists and mathematicians.
“We’re right 50.75 percent of the time . . . but we’re 100 percent right 50.75 percent of the time,” Mercer told a friend. “You can make billions that way.”
Renaissance staffers deduced that there is even more that influences investments, including forces not readily apparent or sometimes even logical. By analyzing and estimating hundreds of financial metrics, social media feeds, barometers of online traffic, and pretty much anything that can be quantified and tested, they uncovered new factors, some borderline impossible for most to appreciate. “The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. We find them across time, across risk factors, across sectors and industries.”
“I’m happy going through my life without saying anything to anybody,” Mercer told the Wall Street Journal in 2010.7
“Bob talked about the need to protect oneself from the government, and the need to have guns and gold,” says an early investor in the Medallion fund. “I didn’t think he was for real.”
Like many conservatives, Mercer also had an equally intense loathing of Bill and Hillary Clinton.
Then, in 2011, the Mercers met conservative firebrand Andrew Breitbart at a conference. Almost immediately, they were intrigued with his far-right news organization, Breitbart News Network, expressing interest in funding its operations. Breitbart introduced the Mercers to his friend, Steve Bannon, a former Goldman Sachs banker, who drew up a term sheet under which the Mercer family purchased nearly 50 percent of Breitbart News for $10 million.
In 2015, Cambridge Analytica discussed ways to help the leaders of Leave.EU, the political group that supported the UK’s withdrawal from the European Union. Bannon was included as part of the email traffic between the two groups, though it’s not clear he read or responded to the emails. The following month, Leave.EU publicly launched a campaign to persuade British voters to support a referendum in favor of an exit from the European Union. Cambridge Analytica officials would deny charging for doing work for Leave.EU.14
Soon, the Mercers shifted their support to Trump, by then the party’s effective nominee. They launched a super PAC to oppose Hillary Clinton, charging Kellyanne Conway, a veteran Republican pollster, with running the organization. Eventually, they’d become Trump’s largest financial backers.
“It’s bad,” Trump acknowledged. “No, it’s not bad—it’s over,” she told Trump. “Unless you make a change.” She told Trump she had a way for him to turn the election around. “Bring in Steve Bannon and Kellyanne Conway,” she said. “I’ve talked to them; they’ll do it.”18 The next day, Bannon took an Uber to the Trump National Golf Club in Bedminster, New Jersey. After impatiently waiting for Trump to finish a round of golf, eat some hot dogs, and then finish an ice-cream treat, Bannon made his pitch. “No doubt you can win,” Bannon told Trump. “You just have to get organized.” Before
“We are completely indifferent to Mr. Trump’s locker-room braggadocio,” they said. “We have a country to save, and there is only one person who can save it. We, and Americans across the country and around the world, stand steadfastly behind Donald J. Trump.”
Then, the results began to turn. Around one o’clock, Trump turned to Bossie, feeling elated: “Dave, can you believe this? We just started this to have some fun.” At 2:20 a.m., Conway received a call from an Associated Press editor. “What state are you calling?” she asked. “We’re not calling a state,” he said. “We’re calling the race.”19
Chapter Fifteen
Rebekah Mercer was emerging as a public figure in her own right. Early that year, GQ magazine named Mercer the seventeenth most powerful person in Washington, DC, calling her “the First Lady of the alt-right.” The family’s political clout, along with its ongoing support for the president-elect, seemed assured.
Chapter Sixteen
Part of the problem was that traditional, actively managed funds no longer wielded an information advantage over their rivals. Once, sophisticated hedge funds, mutual funds, and others had the luxury of poring over annual reports and other financial releases to uncover useful nuggets of overlooked information. Today, almost any type of corporate financial figure is a keystroke or news feed away, and can be captured instantly by machines.
“Say you’re trying to predict how stocks will perform over a one-year horizon,” Richard Dewey, a veteran quant, says. “Because we only have decent records back to 1900, there are only 118 nonoverlapping one-year periods to look at in the US.”10
If machine learning and other computer models become the most influential factors in markets, they may become less predictable and maybe even less stable, since human nature is roughly constant while the nature of this kind of computerized trading can change rapidly.
In June 2019, Renaissance managed a combined $65 billion, making it one of the largest hedge-fund firms in the world, and sometimes represented as much as 5 percent of daily stock-market trading volume, not including high-frequency traders.
Simons and his colleagues generally avoid predicting pure stock moves. It’s not clear any expert or system can reliably predict individual stocks, at least over the long term, or even the direction of financial markets. What Renaissance does is try to anticipate stock moves relative to other stocks, to an index, to a factor model, and to an industry.
Epilogue
In 2014, Simons recruited Princeton University astrophysicist David Spergel, who is known for groundbreaking work measuring the age and composition of the universe. Simons tasked Spergel with answering the eternal question of how the universe began. Oh, and please try to do it in a few years, while I’m still around, Simons said.
“Work with the smartest people you can, hopefully smarter than you . . . be persistent, don’t give up easily. “Be guided by beauty . . . it can be the way a company runs, or the way an experiment comes out, or the way a theorem comes out, but there’s a sense of beauty when something is working well, almost an aesthetic to it.”