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April 11 - May 22, 2021
Simons never took a single finance class, didn’t care very much for business, and, until he turned forty, only dabbled in trading. A decade later, he still hadn’t made much headway. Heck, Simons didn’t even do applied mathematics, he did theoretical math, the most impractical kind.
Simons saw two of his professors, renowned mathematicians Warren Ambrose and Isadore Singer, in deep discussion after midnight at a local café. Simons decided he wanted that kind of life—cigarettes, coffee, and math at all hours.
For more recent pricing data, Simons tasked his former Stony Brook secretary and new office manager, Carole Alberghine, with recording the closing prices of major currencies. Each morning, Alberghine would go through the Wall Street Journal and then climb on sofas and chairs in the firm’s library room to update various figures on graph paper hanging from the ceiling and taped to the walls.
Piggy Basket produced a row of numbers. The sequence “0.5, 0.3, 0.2,” for example, would signify that the currency portfolio should be 50 percent yen, 30 percent deutsche marks, and 20 percent Swiss francs. After the Piggy Basket churned out its recommendations for about forty different futures contracts, a staffer would get in touch with the firm’s broker and deliver buy-and-sell instructions based on those proportions. The system produced automated trade recommendations, rather than automated trades, but it was the best Simons could do at the time.
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.
math, a world that is more competitive than most realize. Mathematicians usually enter the field out of a love for numbers, structures, or models, but the real thrill often comes from being the first to make a discovery or advance. Andrew Wiles, the Princeton mathematician famous for proving the Fermat conjecture, describes mathematics as a journey through “a dark unexplored mansion,” with months, or even years, spent “stumbling around.” Along the way, pressures emerge. Math is considered a young person’s game—those who don’t accomplish something of significance in their twenties or early
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Using an Apple II computer,
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.
Ax had also been concerned that rapid trading would push prices enough to cut into any gains, a cost called slippage, which Medallion couldn’t measure with any accuracy.
eighteenth-century Japanese rice merchant and speculator named Munehisa Homma, known as the “god of the markets,” invented a charting method to visualize the open, high, low, and closing price levels for the country’s rice exchanges over a period of time. Homma’s charts, including the classic candlestick pattern,
It took a little while for the finance industry to come up with a nickname for those designing and implementing these mathematical models. At first, they were called rocket scientists by those who assumed rocketry was the most advanced branch of science, says Emanuel Derman, who received a PhD in theoretical physics at Columbia University before joining a Wall Street firm. Over time, these specialists became known as quants, short for specialists in quantitative finance.
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
Nor was it the enormous fees the fund charged clients or the $100 million it supposedly managed. It was the way Simons was racking up the alleged profits, relying on a computer model that he and his employees themselves didn’t fully understand.
Later, economist Richard Thaler used psychological insights to explain anomalies in investor behavior, spurring the growth of the field of behavioral economics, which explored the cognitive biases of individuals and investors. 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.”
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.
“Know what you own” was Lynch’s mantra.
Fidelity analyst at the time. “We would also eat in the company cafeteria … or at a nearby restaurant, so we could ask the waiter questions about the company across the street.”
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.
One day, a data-entry error caused the fund to purchase five times as many wheat-futures contracts as it intended, pushing prices higher. Picking up the next day’s Wall Street Journal, sheepish staffers read that analysts were attributing the price surge to fears of a poor wheat harvest, rather than Renaissance’s miscue.
Each week, Simons decided, Brown, Mercer, and other senior executives would be assigned three papers to read, digest, and present—a book club for quants with a passion for money rather than sex or murder.
Indeed, talented quants can be among the least comfortable working with others. (A classic industry joke: Extroverted mathematicians are the ones who stare at your shoes during a conversation, not their own.)
One strategy with enduring success: betting on retracements. About 60 percent of investments that experienced big, sudden price rises or drops would snap back, at least partially, it turned out. Profits from these retracements helped Medallion do especially well in volatile markets when prices lurched, before retracing some of that ground.
They only steered clear of the most preposterous ideas. “Volume divided by price change three days earlier, yes, we’d include that,” says a Renaissance executive. “But not something nonsensical, like the outperformance of stock tickers starting with the letter A.”
Once, as he worked on a complicated project late in the evening, full of manic energy despite the hour, Brown picked up the phone to call a junior associate at home with a pressing question. A colleague stopped Brown before he could dial. “Peter, you can’t call him,” he said. “It’s two a.m.” Brown looked confused, forcing the colleague to explain himself. “He doesn’t get paid enough to answer questions at two a.m.” “Fine, let’s give him a raise, then,” Brown replied. “But we have to call him!”
LTCM’s models weren’t prepared for several shocking events in the summer of 1998, however, including Russia’s effective default on its debt and a resulting panic in global markets. As investors fled investments with risk attached to them, prices of all kinds of assets reacted in unexpected ways. LTCM calculated it was unlikely to lose more than $35 million in a day, but it somehow dropped $553 million on one Friday in August of that year. Billions evaporated in a matter of weeks.
D. E. Shaw didn’t seem likely to feel much impact from the troubles. By 1998, the hedge fund started by former Columbia University computer-science professor David Shaw with backing from investor Donald Sussman had grown to several hundred employees. Building on the statistical-arbitrage stock strategies Shaw had developed at Morgan Stanley, his company claimed annual returns of 18 percent on average since launching. On some days, it was responsible for about 5 percent of all trading on the New York Stock Exchange.
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.
Competitors generally had about seven dollars of financial instruments for each dollar of cash. By contrast, Medallion’s options strategy allowed it to have $12.50 worth of financial instruments for every dollar of cash, making it easier to trounce the rivals, assuming it could keep finding profitable trades.
That’s because the options were exercised after a year, allowing Renaissance to argue they were long-term in nature. (Short-term gains are taxed at a rate of 39.5 percent while long-term gains face a 20 percent tax.)
“I’m not sure we’re the best at all aspects of trading, but we’re the best at estimating the cost of a trade,” Simons told a colleague a couple years earlier.
Astronomers, who are accustomed to scrutinizing large, confusing data sets and discovering evidence of subtle phenomena, proved especially capable of identifying overlooked market patterns. Elizabeth Barton, for example, received her PhD from Harvard University and used telescopes in Hawaii and elsewhere to study the evolution of galaxies before joining Renaissance.
“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.”
If Medallion discovered a profitable signal, for example that the dollar rose 0.1 percent between nine a.m. and ten a.m., it wouldn’t buy when the clock struck nine, potentially signaling to others that a move happened each day at that time. Instead, it spread its buying out throughout the hour in unpredictable ways, to preserve its trading signal.
Today, the fastest-moving firms often hold an edge. In late August 2018, shares of a small cancer-drug company called Geron Corporation soared 25 percent after its partner, Johnson & Johnson, posted a job listing. The opening suggested that a key regulatory decision for a drug the two companies were developing might be imminent, a piece of news that escaped all but those with the technology to instantly and automatically scour for job listings and similar real-time information.
IBM has estimated that 90 percent of the world’s data sets have been created in the last two years alone, and that forty zettabytes—or forty-four trillion gigabytes—of data will be created by 2020, a three-hundred-fold increase from 2005.8
The rage among investors is for alternative data, which includes just about everything imaginable, including instant information from sensors and satellite images around the world.
To explore these new possibilities, hedge funds have begun to hire a new type of employee, what they call data analysts or data hunters, who focus on digging up new data sources, much like what Sandor Straus did for Renaissance in the mid-1980s.
For all the advantages quant firms have, the investment returns of most of these trading firms haven’t been that much better than those of traditional firms doing old-fashioned research, with Renaissance and a few others the obvious exceptions. In the five years leading up to spring of 2019, quant-focused hedge funds gained about 4.2 percent a year on average, compared with a gain of 3.3 percent for the average hedge fund in the same period.
By the summer of 2019, Renaissance’s Medallion fund had racked up average annual gains, before investor fees, of about 66 percent since 1988, and a return after fees of approximately 39 percent.
What Renaissance does is try to anticipate stock moves relative to other stocks, to an index, to a factor model, and to an industry.
