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July 18 - July 31, 2023
Whatever you wanted to call it, the results were extraordinary. After soaring 98.5 percent in 2000, the Medallion fund rose 33 percent in 2001. By comparison, the S&P 500, the commonly used barometer of the stock market, managed a measly average gain of 0.2 percent over those two years, while rival hedge funds gained 7.3 percent.
Investment professionals generally judge a portfolio’s risk by its Sharpe ratio, which measures returns in relation to volatility; the higher one’s Sharpe, the better.
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 2002, Medallion managed over $5 billion, but it controlled more than $60 billion of investment positions, thanks in part to the options helping the fund score a gain of 25.8 percent despite a tough year for the broader market.
The size limit meant Medallion sometimes identified more market aberrations and phenomena than it could put to use. The discarded trading signals usually involved longer-term opportunities. Simons’s scientists were more confident about short-term signals, partly because more data was available to help confirm them. A one-day trading signal can incorporate data points for every trading day of the year, for instance, while a one-year signal depends on just one annual data point. Nonetheless, the researchers were pretty sure they could make solid money if they ever had a chance to develop
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His researchers settled on one that would trade with little human intervention, like Medallion, yet would hold investments a month or even longer. It would incorporate some of Renaissance’s usual tactics, such as finding correlations and patterns in prices, but would add other, more fundamental strategies, including buying inexpensive shares based on price-earnings ratios, balance-sheet data, and other information.
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.”
In 2004, he helped launch Math for America, a nonprofit dedicated to promoting math education and supporting outstanding teachers. Eventually, the foundation would spend millions of dollars annually to provide annual stipends of $15,000 to one thousand top math and science teachers in New York’s public middle schools and high schools, or about 10 percent of the city’s teachers in the subjects. It also hosted seminars and workshops, creating a community of enthusiastic teachers. “Instead of beating up the bad teachers, we focus on celebrating the good ones,” Simons says. “We give them status
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Medallion’s long-term record was arguably the greatest in the history of the financial markets, a reason investors and others were becoming fascinated with the secretive firm.
Medallion still held thousands of long and short positions at any time, and its holding period ranged from one or two days to one or two weeks.
“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.”
It has to be automated as it's too close to the wire to allow humans to start poking into the trades
How the firm wagered was at least as important as what it wagered on. 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.
Once, Mercer rattled off an array of statistics to demonstrate that nature emits more carbon dioxide than humans. Later, when Cooper checked the data, it was accurate, but Mercer had overlooked the fact that nature absorbs as much carbon dioxide as it emits, which mankind does not. “It sounded like someone had got to him,” Cooper says. “Even a smart guy can get the details right but the big picture wrong.”
In February 2014, Mercer and other conservative political donors gathered at New York’s Pierre hotel to strategize about the 2016 presidential election. He told attendees he had seen data indicating that mainstream Republicans, such as Jeb Bush and Marco Rubio, would have difficulty winning. Only a true outsider with a sense of the voters’ frustrations could emerge victorious, Mercer argued. Others didn’t seem as convinced by his data. He and Rebekah began searching for an outsider to shake up Washington.
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.
The firm rarely fires employees, even when they’re unproductive, disinterested, or difficult. The risk is just too great. Even lackluster, midlevel researchers and programmers are privy to insights and understandings that may prove helpful to rivals.
In October 2018, when she was honored at a Washington, DC, gala, Mercer shared concerns about the level of discourse on college campuses, saying schools “churn out a wave of ovine zombies steeped in the anti-American myths of the radical left, ignorant of basic civics, economics, and history, and completely unfit for critical thinking.”
Today, even banking giant JPMorgan Chase puts hundreds of its new investment bankers and investment professionals through mandatory coding lessons. Simons’s success had validated the field of quantitative investing.
Simons’s phone call is a stark reminder of how difficult it can be to turn decision-making over to computers, algorithms, and models—even, at times, for the inventors of these very approaches. His conversation with Chhabra helps explain the faith investors have long placed in stock-and-bond pickers dependent on judgment, experience, and old-fashioned research.
Tempting to take hold of the wheel - self driving cars are going to be interesting...
Increasingly, it seemed, once-dependable investing tactics, such as grilling corporate managers, scrutinizing balance sheets, and using instinct and intuition to bet on major global economic shifts, amounted to too little.
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. It’s almost impossible to identify facts or figures not fully appreciated by rival investors.
Quant investors had emerged as the dominant players in the finance business. As of early 2019, they represented close to a third of all stock-market trades, a share that had more than doubled since 2013.6
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.
Creative investors test for money-making correlations and patterns by scrutinizing the tones of executives on conference calls, traffic in the parking lots of retail stores, records of auto-insurance applications, and recommendations by social media influencers.
Rather than wait for figures on agricultural production, quants examine sales of farm equipment or satellite images of crop yields.
If you seek a sense of the popularity of a new product, Amazon reviews can be scraped.
hedge-fund firm Two Sigma has built a computing system with more than one hundred teraflops of power—meaning it can process one hundred trillion calculations a second—and more than eleven petabytes of memory, the equivalent of five times the data stored in all US academic libraries.9 All that power allows quants to find and test many more predictive signals than ever before.
Years after Simons’s team at Renaissance adopted machine-learning techniques, other quants have begun to embrace these methods. Renaissance anticipated a transformation in decision-making that’s sweeping almost every business and walk of life.
For those reasons, there likely will remain pockets of the market where savvy traditional investors prosper, especially those focused on longer-term investing that algorithmic, computer-driven investors tend to shy away from.
Some of these practitioners have programmed their computers to buy when stocks get cheap, helping to stabilize the market.
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.
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.
