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That move would lay the groundwork for a historic breakthrough.
I strongly believe, for all babies and a significant number of grownups, curiosity is a bigger motivator than money.
“Try to get on a great team,” he says.
(Decades later, the relay team’s anchor, Jack Wadsworth Jr., then working as an investment banker, led the initial public offering for an upstart company called Apple Computer.)
The Massachusetts Institute of Technology became an obvious choice. “When I heard MIT didn’t have a football team, I knew it was the school for me,” he says.
As a freshman, he was selected to participate in an advanced calculus class taught by John Nash, the game theorist and mathematician who later would be immortalized in Sylvia Nasar’s book A Beautiful Mind.
They named their hedge fund Medallion, in honor of the prestigious math awards each had received. Within six months, Medallion was suffering. Some of the losses could be traced to a shift in Ax’s focus.
Soon, Ax was searching for a more isolated spot, eventually renting a seaside estate in Malibu.
Ax never truly enjoyed the company of others, especially his co-workers.
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.
Approaching his forty-ninth birthday, Berlekamp also bore little physical resemblance to the masters of the universe reaping Wall Street’s mounting spoils. Berlekamp had come to value physical fitness, embracing a series of extreme and unsafe diets and grueling bicycle rides.
The team forged ahead, with Berlekamp focused on implementing some of the most promising recommendations Ax had ignored. Simons, exhausted from months of bickering with Ax, supported the idea. “Let’s bank some sure things,” Berlekamp told Simons.
These were legitimate concerns that had led to something of an unwritten rule on Wall Street: Don’t trade too much. Beyond the costs, short-term moves generally yield tiny gains, exciting few investors. What’s the point of working so hard and trading so frequently if the upside is so limited?
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.
Just as casinos handle so many daily bets that they only need to profit
from a bit more than half of those wagers, the Axcom team wanted their fund to trade so frequently that it could score big profits by making money on a bare majority of its trades.
“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.”
Prices for some investments often fell just before key economic reports and rose right after, but prices didn’t always fall before the reports came out and didn’t always rise in the moments after. For whatever
reason, the pattern didn’t hold for the US Department of Labor’s employment statistics and some other data releases. But there was enough data to indicate when the phenomena were most likely to take place, so the model recommended purchases just before the economic releases and sales almost immediately after them.
Sifting through Straus’s data, Laufer discovered certain recurring trading
sequences based on the day of the week. 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. The Medallion model began to buy late in the day on a Friday if a clear up-trend existed, for instance, and then sell early Monday, taking advantage of what they called the weekend effect.
They did have theories. Berlekamp and others developed a thesis that locals, or floor traders who buy or sell commodities and bonds to keep the market functioning, liked to go home at the end of a trading week holding few or no futures contracts, just in case bad news arose over the weekend that might saddle them with losses.
“We’re in the insurance business,” Berlekamp told Straus.
“The time scale doesn’t seem to matter,” Berlekamp said to a colleague one day, with surprise. “We get the same statistical anomaly.”
frequency, at least according to most economists at the time who had embraced the efficient market hypothesis. Under this view, it’s impossible to beat the market by taking advantage of price irregularities—they shouldn’t exist. Once irregularities are discovered, investors should step in to remove them, the academics argued.
“People persist in their habits longer than they should,” he says.
The currency moves were part of Medallion’s growing mix of tradeable effects, in their developing parlance.
These trends and oddities sometimes happened so quickly that they were unnoticeable to most investors. They were so faint, the team took to calling them ghosts, yet they kept reappearing with enough frequency to be worthy additions to their mix of trade ideas. Simons had come around to the view that the whys didn’t matter, just that the trades worked.
quickly parse all the pricing data in Straus’s collection, generating thousands of statistically significant observations within the trading data to help reveal previously undetected pricing patterns.
By trading frequently, the Medallion team figured it would be worthwhile to hold on to all the guppies they were collecting.
The firm implemented its new approach in late 1989 with the $27 million Simons still managed. The results were almost immediate, startling nearly everyone in the office. They did more trading than ever, cutting Medallion’s average holding time to just a day and a half from a week and a half, scoring profits almost every day.
One afternoon, Berlekamp shared his frustrations with Simons, who called a trader on the floor of the Chicago Board of Trade to get his take on their problems. “Don’t you know, Jim?” the trader told him, with a chuckle. “Those guys are crooks.”
Simons immediately eliminated Canadian dollar contracts from Medallion’s trading system. A few months later, in early 1990, Simons called Berlekamp with even more unsettling news.
Their system became mostly short-term moves, with long-term trades representing about 10 percent of activity.
One day, Axcom made more than $1 million, a first for the firm.
Medallion scored a gain of 55.9 percent in 1990, a dramatic improvement on its 4 percent loss the previous year. The profits were especially impressive because they were over and above the hefty fees charged by the fund, which amounted to 5 percentfn1 of all assets managed and 20 percent of all gains generated by the fund.
“Jim believed the fund should be managed systematically, but he was fussing around when he had time, five to ten hours a week, trading gold or copper, thinking he was learning something,” Berlekamp says. Much like Baum and Ax before him, Simons couldn’t help reacting to the news. Berlekamp pushed back. “Like I said, Jim, we’re not going to adjust our positions,” a peeved Berlekamp told Simons one day.
Hanging up, Berlekamp turned to
colleague: “The system will determine what we trade.” Simons never ordered any major trades, but he did get Berlekamp to buy some oil call options to serve as “insurance” in case crude prices ...
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fund’s overall positions back by a third as Middle East hostilitie...
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In the spring of 2019, Berlekamp died from complications of pulmonary fibrosis at the age of seventy-eight.
Berlekamp, Ax, and Baum had all left the firm, but Simons wasn’t especially concerned. He was sure he had developed a surefire method to invest in a systematic way, using computers and algorithms to trade commodities, bonds, and currencies in a manner that can be seen as a more scientific and sophisticated version of technical trading, one that entailed searching for overlooked patterns in the market.
Simons hadn’t spent much time delving into financial history, though. Had he done so, Simons might have realized that his approach wasn’t especially novel. For centuries, speculators
had embraced various forms of pattern recognition, relying on methods that bore similarity to some of the things Renaissance was doing. The fact that many of these colorful characters had failed miserably, or were outright charlatans, didn’t augur well for Simons.
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. In the middle of the sixteenth century, a trader in Nuremberg, Germany, named Christopher Kurz won acclaim for his supposed ability to forecast twenty-day prices of cinnamon, pepper, and other spices. Like much of society at the time, Kurz relied o...
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such as the fact that prices often move in long-p...
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Homma’s charts, including the classic candlestick pattern, resulted in an early and reasonably sophisticated reversion-to-the-mean trading strategy.
In the 1830s, British economists sold sophisticated price charts to investors. Later that century, an American journalist named Charles Dow, who devised
the Dow Jones Industrial Average and helped launch the Wall Street Journal, applied a level of mathematical rigor to various market hypotheses, birthing modern technical analysis, which relies on the charting of distinct price trends, trading volume, and other factors.
To this day, Gann analysis remains a reasonably popular branch of technical trading.
