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The fact that Gann wrote eight books and penned a daily investment newsletter, yet managed to share few details of his trading approach and, by some accounts, died with a net worth of only $100,000 raises other questions.2
“He was a financial astrologer of sorts,” concludes Andrew Lo, a professor at the MIT Sloan School of Management.
The Manhattan Fund was crushed in the 1969–70 bear market, its performance and methods ridiculed. By then, Tsai had sold out to an insurance company and was busy helping turn financial-services company Primerica into a key building block for the banking power that became Citigroup.3
Some top, modern traders, including Stanley Druckenmiller, consult charts to confirm existing investment theses. Professor Lo and others argue that technical analysts were the “forerunners” of quantitative investing.
Still, a Chicago-based trader named Richard Dennis managed to build a trading system governed by specific, preset rules aimed at removing emotions and irrationality from his trades, not unlike the approach Simons was so excited about.
As Renaissance staffers struggled to improve their model throughout the 1980s, they kept hearing about Dennis’s successes. At the age of twenty-six, he already was a distinctive presence on the floor of the Chicago Board of Trade, enough so to warrant a sobriquet: the “Prince of
the Pit.” Dennis had thick, gold-framed glasses, a stomach that protruded over his belt, and thinning, frizzy hair that fell “like a beagle’s ears around his face,...
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uninitiated become market mavens. Some of the turtles saw striking success. Dennis himself is said to have made $80 million in 1986 and managed about $100 million a year later. He was crushed in 1987’s market turbulence, however, the latest
After squandering about half his cash, Dennis took a break from trading to focus on liberal political causes and the legalization of marijuana, among other things.
“There is more to life than trading,” he told an interv...
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Over time, these specialists became known as quants, short for specialists in quantitative finance. For years, Derman recalls, senior managers at banks and investment firms, many of whom prided themselves on maintaining an ignorance of computers, employed the term as a pejorative.
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.
This theory suggested that markets will deliver more unexpected events than widely assumed, another reason to doubt the elaborate models produced by high-powered computers. 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 freq...
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Skeptics sniffed—one told the Journal that “the real investment world is too complicated to be reduced to a model.” Yet, by the late 1980s, Thorp’s fund stood at nearly $300 million, dwarfing the $25 million Simons’s Medallion fund was managing at the time.
Thorp never was accused of any impropriety, and the government eventually dropped all charges related to Princeton/Newport’s activities, but publicity related to the investigation crippled his fund, and it closed in late 1988, a denouement Thorp describes as “traumatic.” Over its nineteen-year existence, the hedge fund featured annual gains averaging more than 15 percent (after charging investors various fees), topping the market’s returns over that span.
When the traders prepared to buy and sell big chunks of shares for clients, acquiring a few million dollars of Coca-Cola, for example, they protected themselves by selling an equal amount of something similar, like Pepsi, in what is commonly referred to as a pairs trade.
It wouldn’t be clear for many years, but Morgan Stanley had squandered some of the most lucrative trading strategies in the history of finance.
A handful of investors and academics were mulling factor investing around that same time, but Frey wondered if he could do a better job using computational statistics and other mathematical techniques to isolate the true factors moving shares.
Sussman suggested that Shaw start his own hedge fund, rather than work for Goldman Sachs, offering a $28
million initial seed investment. Shaw was swayed, launching D. E. Shaw in an office space above Revolution Books, a communist bookstore in a then-gritty part of Manhattan’s Union Square area. One of Shaw’s first moves was to purchase two ultrafast and expensive Sun Microsystems computers.
Shaw, a supercomputing expert, hired math and science PhDs who embraced his scientific approach to trading. He also brought on whip-smart employees from different backgrounds. English and philosophy majors were among Shaw’s favorite hires, but he also hired a chess master, stand-up comedians, published writers, an Olympic-level fencer, a trombone player, and a demolitions specialist.
“We didn’t want anyone with preconceived notions,” an early executive says.11
“I think people will buy things on the internet,” Shaw told a colleague. “Not only will they shop, but when they buy something … they’re going to say, ‘this pipe is good,’ or ‘this pipe is bad,’ and they’re going to post reviews.” 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. Along the way, Bezos worked on a laptop, pecking out a business plan for his company, Amazon.com. (He originally chose “Cadabra” but dropped the name because too many people mistook it for “Cadaver.”)12
Jim Simons didn’t have a clear understanding of the kind of progress Shaw and a few others were making. He did know, if he was going to build something special to catch up with those who had a jump on him, he’d need
some help. Simons called Sussman, the financier who had given David Shaw the support he needed to start his own hedge fund, hoping for a similar boost.
But, after a full decade in the business, he was managing barely more than $45 million, a mere quarter the assets of Shaw’s firm.
The meeting had import—backing from Sussman could help Renaissance hire employees, upgrade technology, and become a force on Wall Street.
Eyeing Simons across a long, narrow wooden table, Sussman couldn’t help smiling. His guest was bearded,
balding, and graying, bearing little resemblance to most of the investors who made regular pilgrimages to his office asking for money. Simons’s tie was slightly askew, and his jacket tweed, a rarity on Wall Street.
He came alone, without the usual entourage of handlers and advisors. Simons was just the kind of brainy in...
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Simons didn’t have much more luck with other potential backers. Investors wouldn’t say it to his face, but most deemed it absurd to rely on trading models generated by computers. Just as preposterous were Simons’s fees, especially his requirement that investors hand over 5 percent of the money he managed for them each year, well above the 2 percent levied by most hedge funds.
Academics who shift to trading often turn nervous and edgy, worried about each move in the market, concerns that hounded Baum when he joined Simons.
For Simons, Laufer’s geniality was a welcome relief after years of dealing with the complicated personalities of Baum, Ax, and Berlekamp.
Laufer began splitting the day in half, then into quarters, eventually deciding five-minute bars were the ideal way to carve things up.
Did the 188th five-minute bar in the cocoa-futures market regularly fall on days investors got nervous, while bar 199 usually rebounded? Perhaps bar 50 in the gold market saw strong buying on days investors worried about inflation but bar 63 often showed weakness?
Laufer’s five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects.
Certain trading bands from Friday morning’s action had the uncanny ability to predict bands later that same afternoon, nearer to the close of trading.
day. The team uncovered predictive effects related to volatility, as well as a series of combination effects, such as the propensity of pairs of investments—such as gold and silver, or heating oil and crude oil—to move in the same
direction at certain times in the trading day compared with others. It wasn’t immediately obvious why some of the new trading signals worked, but as long as they had p-values, or probability values, under 0.01—meaning they appeared statistically significant, with a low probability of being statistical mirages—they were added to the system. Wielding an array of profitable
“My father had a hard time in his late fifties, and that worried me,” recalls Patterson, who had two children at the time who were preparing to go to college. “I didn’t have enough money, and I didn’t want to go down that road.”
From the earliest days of the fund, Simons’s team had been wary of these transaction costs, which they called slippage.
“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.
“What you’re really modeling is human behavior,” explains Penavic, the researcher. “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.”
No one ever made a decision because of a number. They need a story.
Peter Lynch was a paragon of the fundamental approach. From 1977 to 1990, Lynch’s prescient stock picks helped Fidelity Investments’ Magellan mutual fund grow from a $100 million pip-squeak into a $16 billion power, averaging annual gains of 29 percent, beating the market in eleven of those years.
These were legal tactics at the time, even though smaller investors couldn’t access the same information. “The computer won’t tell you [if a business trend] is going to last a month or a year,” Lynch said.1
As Lynch and Vinik racked up big gains in Boston, Bill Gross was on the other side of the country, on the shores of Newport Beach, California, building a bond empire at a company called Pacific Investment Management Company, or PIMCO.
