The Man Who Solved The Market Quotes

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.

Simons shared a few life lessons with the school’s audience: “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.

Scientists and mathematicians are trained to dig below the surface of the chaotic, natural world to search for unexpected simplicity, structure, and even beauty.

Simons and his team are among the most secretive traders Wall Street has encountered, loath to drop even a hint of how they’d conquered financial markets, lest a competitor seize on any clue. Employees avoid media appearances and steer clear of industry conferences and most public gatherings. Simons once quoted Benjamin, the donkey in Animal Farm , to explain his attitude: “‘God gave me a tail to keep off the flies. But I’d rather have had no tail and no flies.’ That’s kind of the way I feel about publicity.

Instead of beating up the bad teachers, we focus on celebrating the good ones,

Sixth

1991,

Any time you hear financial experts talking about how the market went up because of such and such—remember it’s all nonsense,” Brown later would say.

Truth in life is broad and nuanced; you can make all kinds of arguments, such as whether a president or person is fantastic or awful,” he says. “That’s why I love math problems—they have clear answers.

mass times its acceleration—is a differential equation because acceleration is a second derivative with respect to time. Equations involving derivatives with respect to time and space are examples of partial differential equations and can be used to describe elasticity, heat, and sound, among other things.

CHAPTER EIGHT

Scientists and mathematicians need to interact, debate, and share ideas to generate ideal results.

Our job is to survive,” Simons said. “If we’re wrong, we can always add [positions] later.” Brown seemed shocked by what he was hearing.

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.

Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future.

The strategies were often based on the idea that prices tend to revert after an initial move higher or lower. Laufer would buy futures contracts if they opened at unusually low prices compared with their previous closing price, and sell if prices began the day much higher than their previous close. Simons made his own improvements to the evolving system, while insisting that the team work together and share credit.

When the Axcom team started testing the approach, they quickly began to see improved results. The firm began incorporating higher dimensional kernel regression approaches, which seemed to work best for trending models, or those predicting how long certain investments would keep moving in a trend.

For all the unique data, computer firepower, special talent, and trading and risk-management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market—and how foolish it is for most investors to try.

The options also were a way of shifting enormous risk from Renaissance to the banks. Because the lenders technically owned the underlying securities in the basket-options transactions, the most Medallion could lose in the event of a sudden collapse was the premium it had paid for the options and the collateral held by the banks. That amounted to several hundred million dollars. By contrast, the banks faced billions of dollars of potential losses if Medallion were to experience deep troubles. In the words of a banker involved in the lending arrangement, the options allowed Medallion to “ring-fence” its stock portfolios, protecting other parts of the firm, including Laufer’s still-thriving futures trading, and ensuring Renaissance’s survival in the event something unforeseen took place. One staffer was so shocked by the terms of the financing that he shifted most of his life savings into Medallion, realizing the most he could lose was about 20 percent of his money.

Monemetrics would invest a bit of money for Simons, testing strategies in a variety of markets. If the tactics looked profitable, Simons would place the same trades in Limroy, which was much bigger and would invest for outsiders as well as for Simons. Baum would share in the 25 percent cut the firm claimed from all its trading profits.

Elsewhere, statisticians were using similar approaches—called kernel methods—to analyze patterns in data sets. Back on Long Island, Henry Laufer was working on similar machine-learning tactics in his own research and was set to share it with Simons and others. Carmona wasn’t aware of this work. He was simply proposing using sophisticated algorithms to give Ax and Straus the framework to identify patterns in current prices that seemed similar to those in the past.

One day, Carmona had an idea. Axcom had been employing various approaches to using their pricing data to trade, including relying on breakout signals. They also used simple linear regressions, a basic forecasting tool relied upon by many investors that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear. Plot crude-oil prices on the x-axis and the price of gasoline on the y-axis, place a straight regression line through the points on the graph, extend that line, and you usually can do a pretty good job predicting prices at the pump for a given level of oil price.

It’s important to remember that market participants have always tended to pull back and do less trading during market crises, suggesting that any reluctance by quants to trade isn’t so very different from past approaches. If anything, markets have become more placid as quant investors have assumed dominant positions. Humans are prone to fear, greed, and outright panic, all of which tend to sow volatility in financial markets. Machines could make markets more stable, if they elbow out individuals governed by biases and emotions. And computer-driven decision-making in other fields, such as the airline industry, has generally led to fewer mistakes.

His early research wasn’t especially original. Ax identified slight upward trends in a number of investments and tested if their average price over the previous ten, fifteen, twenty, or fifty days was predictive of future moves. It was similar to the work of other traders, often called trenders, who examine moving averages and jump on market trends, riding them until they peter out. Ax’s predictive models had potential, but they were quite crude. The trove of data Simons and others had collected proved of little use, mostly because it was riddled with errors and faulty prices. Also, Ax’s trading system wasn’t in any way automated—his trades were made by phone, twice a day, in the morning and at the end of the trading day.

The young man making a hash of his visit to the Garden of Allah that December evening was a mess of contradictions. He was a cofounder of one of the most successful startups ever, but he didn’t want to be seen as a businessman. He craved the advice of mentors, and yet resented those in power. He dropped acid, walked barefoot, wore scraggly jeans, and liked the idea of living in a commune, yet he also loved nothing more than speeding down the highway in a finely crafted German sports car. He had a vague desire to support good causes, but he hated the inefficiency of most charities. He was impatient as hell and knew that the only problems worth solving were ones that would take years to tackle. He was a practicing Buddhist and an unrepentant capitalist. He was an overbearing know-it-all berating people who were wiser and immensely more experienced, and yet he was absolutely right about their fundamental marketing naïveté. He could be aggressively rude and then truly contrite. He was intransigent, and yet eager to learn. He walked away, and he walked back in to apologize. At the Garden of Allah he displayed all the brash, ugly behavior that became an entrenched part of the Steve Jobs myth. And he showed a softer side that would go less recognized over the years. To truly understand Steve and the incredible journey he was about to undergo, the full transformation that he would experience over his rich life, you have to recognize, accept, and try to reconcile both sides of the man.

The problem is that so many investors focus so much on these types of news that nearly all of their results cluster very near their average.

Soros and other people influence politics as much as you do, but they aren’t vilified like you are.

seemingly detached from human emotion.

By 1997, Medallion’s staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals. 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.

They ultimately settled on a mix of sensible signals, surprising trades with strong statistical results, and a few bizarre signals so reliable they couldn’t be ignored. “We ask, ‘Does this correspond to some aspect of behavior that seems reasonable?’” Simons explained a few years later.

Simons’s team appeared to have discovered something of a holy grail in investing: enormous returns from a diversified portfolio generating relatively little volatility and correlation to the overall market. In the past, a few others had developed investment vehicles with similar characteristics. They usually had puny portfolios, however. No one had achieved what Simons and his team had—a portfolio as big as $5 billion delivering this kind of astonishing performance.

It’s one thing to have good ideas, it’s another to recognize when others do.

Watching the traders buy big blocks of shares, Bamberger observed that prices often moved higher, as might be expected. Prices headed lower when Morgan Stanley’s traders sold blocks of shares. Each time, the trading activity altered the gap, or spread, between the stock in question and the other company in the pair, even when there was no news in the market. An order to sell a chunk of Coke shares, for instance, might send that stock down a percentage point or even two, even as Pepsi barely moved. Once the effect of their Coke stock selling wore off, the spread between the shares reverted to the norm,

In the early twentieth century the problem of production had been solved; after that it was the problem of consumption that plagued society. In the 1950's and '60's, consumer commodities and farm products began to pile up in vast towering mountains all over the Western World. As much as possible was given away--but that threatened to subvert the open market. By 1980, the pro tem solution was to heap up the products and burn them: billions of dollars' worth, week after week. Each Saturday, townspeople had collected in sullen, resentful crowds to watch the troops squirt gasoline on the cars and toasters and clothes and oranges and coffee and cigarettes that nobody could buy, igniting them in a blinding conflagration. In each town there was a burning-place, fenced off, a kind of rubbish and ash heap, where the fine things that could not be purchased were systematically destroyed. The Quizzes had helped, a trifle. If people couldn't afford to buy the expensive manufactured goods, they could still hope to win them. The economy was propped up for decades by elaborate give-away devices that dispensed tons of glittering merchandise. But for every man who won a car and a refrigerator and a TV set there were millions who didn't. Gradually, over the years, prizes in the Quizzes grew from material commodities to more realistic items: power and prestige. And at the top, the final exalted post: dispenser of power--Quizmaster, and that meant running the Quiz itself.

Baum

members, explaining

fucking

a smaller, nearby office, trading his own account, favored raggedy sweaters, wrinkled trousers, and worn Hush Puppies shoes. To compensate for his worsening eyesight, he hunched close to his computer, trying to ignore the smoke wafting through the office from Simons’s cigarettes. Their traditional trading approach was going so well that, when the boutique next door closed, Simons rented the space and punched through the adjoining wall. The new space was filled with offices for new hires, including an economist and others who provided expert intelligence and made their own trades, helping to boost returns. At the same time, Simons was developing a new passion: backing promising technology companies, including an electronic dictionary company called Franklin Electronic Publishers, which developed the first hand-held computer.

When a young employee gasped at his blue language, Simons flashed a grin. “I know—that is an impressive rate!” A few times a week, Marilyn came by to visit, usually with their baby, Nicholas. Other times, Barbara checked in on her ex-husband. Other employees’ spouses and children also wandered around the office. Each afternoon, the team met for tea in the library, where Simons, Baum, and others discussed the latest news and debated the direction of the economy. Simons also hosted staffers on his yacht, The Lord Jim, docked in nearby Port Jefferson. Most days, Simons sat in his office, wearing jeans and a golf shirt, staring at his computer screen, developing new trades—reading the news and predicting where markets were going, like most everyone else. When he was especially engrossed in thought, Simons would hold a cigarette in one hand and chew on his cheek. Baum, in a smaller, nearby office, trading his own account, favored raggedy sweaters, wrinkled trousers, and worn Hush Puppies shoes. To compensate for his worsening eyesight, he hunched close to his computer, trying to ignore the smoke wafting through the office from Simons’s cigarettes. Their traditional trading approach was going so well that, when the boutique next door closed, Simons rented the space and punched through the adjoining wall. The new space was filled with offices for new hires, including an economist and others who provided expert intelligence and made their own trades, helping to boost returns. At the same time, Simons was developing a new passion: backing promising technology companies, including an electronic dictionary company called Franklin Electronic Publishers, which developed the first hand-held computer.

When a young employee gasped at his blue language, Simons flashed a grin. “I know—that is an impressive rate!” A few times a week, Marilyn came by to visit, usually with their baby, Nicholas. Other times, Barbara checked in on her ex-husband. Other employees’ spouses and children also wandered around the office. Each afternoon, the team met for tea in the library, where Simons, Baum, and others discussed the latest news and debated the direction of the economy. Simons also hosted staffers on his yacht, The Lord Jim, docked in nearby Port Jefferson. Most days, Simons sat in his office, wearing jeans and a golf shirt, staring at his computer screen, developing new trades—reading the news and predicting where markets were going, like most everyone else. When he was especially engrossed in thought, Simons would hold a cigarette in one hand and chew on his cheek. Baum, in a smaller, nearby office, trading his own account, favored raggedy sweaters, wrinkled trousers, and worn Hush Puppies shoes. To compensate for his worsening eyesight, he hunched close to his computer, trying to ignore the smoke wafting through the office from Simons’s cigarettes. Their traditional trading approach was going so well that, when the boutique next door closed, Simons rented the space and punched through the adjoining wall. The new space was filled with offices for new hires, including an economist and others who provided expert intelligence and made their own trades, helping to boost returns. At the same time, Simons was developing a new passion: backing promising technology companies, including an electronic dictionary company called Franklin Electronic Publishers, which developed the first hand-held computer. 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. Simons also took time to care for his children, one of whom needed extra attention. Paul, Simons’s second child with Barbara, had been born with a rare hereditary condition called ectodermal dysplasia. Paul’s skin, hair, and sweat glands didn’t develop properly, he was short for his age, and his teeth were few and misshapen. To cope with the resulting insecurities, Paul asked his parents to buy him stylish and popular clothing in the hopes of fitting in with his grade-school peers. Paul’s challenges weighed on Simons, who sometimes drove Paul to Trenton, New Jersey, where a pediatric dentist made cosmetic improvements to Paul’s teeth. Later, a New York dentist fitted Paul with a complete set of implants, improving his self-esteem. Baum was fine with Simons working from the New York office, dealing with his outside investments, and tending to family matters. Baum didn’t need much help. He was making so much money trading various currencies using intuition and instinct that pursuing a systematic, “quantitative” style of trading seemed a waste of

Three years later, at the age of sixty-nine,

I could have been killed!

It was almost as if Ax invited me there to attract women,

I like kids,

It sounded like someone had got to him,” Cooper says. “Even a smart guy can get the details right but the big picture wrong.

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.

Ax, and Straus didn’t believe the market was truly a “random walk,” or entirely unpredictable, as some academics and others argued.

Berlekamp also argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple times, and your portfolio could be doomed. Make a lot of trades, however, and each individual move is less important, reducing a portfolio’s overall risk.

Spring flew by and summer quickly arrived; my belly grew right alongside the daylilies, zinnias, and tomatoes Marlboro Man’s mom had helped me plant in a small garden outside the house. For Marlboro Man, the coming of the baby proved to be an effective diversion from the aftermath of the previous fall’s market woes. More and more, it looked like Marlboro Man might have to sell some of his land in order to keep the rest of the ranch afloat. As someone who didn’t grow up on a ranch, I failed to feel the gravity of the situation. You have a problem, you have an asset, you sell the asset, you solve the problem. But for Marlboro Man, it could never be that simple or sterile. For a ranching family, putting together a ranch takes time--sometimes years, even generations of patiently waiting for this pasture or that to become available. For a rancher, the words of Pa in Gone With the Wind ring beautifully and painfully true: Land is the only thing worth working for…worth fighting for, worth dying for. Because it’s the only thing that lasts…The thought of parting with a part of the family’s ranch was a painful prospect; Marlboro Man felt the sting daily. To me it seemed like an easy fix; to Marlboro Man, it was a personal failure. There was nothing I could do to make it better except to be there to catch him in my arms every night, which I willingly and eagerly did. I was a soft, lumpy pillow. With heartburn and swollen ankles.

The failure of companies in a free market, then, is not a defect of the system, or an unfortunate by-product of competition; rather, it is an indispensable aspect of any evolutionary process. According to one economist, 10 percent of American companies go bankrupt every year.4 The economist Joseph Schumpeter called this “creative destruction.” Now, compare this with centrally planned economies, where there are almost no failures at all. Companies are protected from failure by subsidy. The state is protected from failure by the printing press, which can inflate its way out of trouble. At first, this may look like an enlightened way to go about solving the problems of economic production, distribution, and exchange. Nothing ever fails and, by implication, everything looks successful. But this is precisely why planned economies didn’t work. They were manned by intelligent planners who decided how much grain to produce, how much iron to mine, and who used complicated calculations to determine the optimal solutions. But they faced the same problem as the Unilever mathematicians: their ideas, however enlightened, were not tested rapidly enough—and so had little opportunity to be reformed in the light of failure. Even if the planners were ten times smarter than the businessmen operating in a market economy, they would still fall way behind. Without the benefit of a valid test, the system is plagued by rigidity. In markets, on the other hand, it is the thousands of little failures that lubricate and, in a sense, guide the system. When companies go under, other entrepreneurs learn from these mistakes, the system creates new ideas, and consumers ultimately benefit.

When I heard MIT didn’t have a football team, I knew it was the school for me,” he says.

It didn’t take Baum long to develop an algorithm directing Monemetrics to buy currencies if they moved a certain level below their recent trend line and sell if they veered too far above it. It was a simple piece of

The lunch went well, and the foundation appeared close to writing a big check to RIEF. To cap things off, a thick, iced vanilla cake was placed in the middle of the table. Everyone eyed the dessert, preparing for a taste. Just then, Simons walked in, setting the room ablaze. “Jim, can we take a picture?” asked one of the health organization’s investment professionals. As the small talk got under way, Simons began making odd motions with his right hand. The foundation executives had no clue what was happening, but nervous RIEF staffers did. When Simons was desperate for a smoke, he scrabbled at his left breast pocket, where he kept his Merits. There was nothing in there, though, so Simons called his assistant on an intercom system, asking her to bring him a cigarette. “Do you mind if I smoke?” Simons asked his guests. Before they knew it, Simons was lighting up. Soon, fumes were choking the room. The Robert Wood Johnson representatives—still dedicated to building a culture of health—were stunned. Simons didn’t seem to notice or care. After some awkward chitchat, he looked to put out his cigarette, now down to a burning butt, but he couldn’t locate an ashtray. Now the RIEF staffers were sweating—Simons was known to ash pretty much anywhere he pleased in the office, even on the desks of underlings and in their coffee mugs. Simons was in Renaissance’s swankiest conference room, though, and he couldn’t find an appropriate receptacle. Finally, Simons spotted the frosted cake. He stood up, reached across the table, and buried his cigarette deep in the icing. As the cake sizzled, Simons walked out, the mouths of his guests agape. The Renaissance salesmen were crestfallen, convinced their lucrative sale had been squandered. The foundation’s executives recovered their poise quickly, however, eagerly signing a big check. It was going to take more than choking on cigarette smoke and a ruined vanilla cake to keep them from the new fund.

His early research wasn’t especially original. Ax identified slight upward trends in a number of investments and tested if their average price over the previous ten, fifteen, twenty, or fifty days was predictive of future moves. It was similar to the work of other traders, often called trenders, who examine moving averages and jump on market trends, riding them until they peter out. Ax’s predictive models had potential, but they were quite crude.

existing mantra at Renaissance: Never place too much trust in trading models. Yes, the firm’s system seemed to work, but all formulas are fallible. This conclusion reinforced the fund’s approach to managing risk. If a strategy wasn’t working, or when market volatility surged, Renaissance’s system tended to automatically reduce positions and risk. For example, Medallion cut its futures trading by 25 percent in the fall of 1998. By contrast, when LTCM’s strategies floundered, the firm often grew their size, rather than pull back.

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 it all happen. All day, Medallion’s computers sent automated instructions to the banks, sometimes an order a minute or even a second. After a year or so, Medallion exercised its options, claiming whatever returns the shares generated, less some related costs.