Jim Simons and His Quants

The award-winning writer and Wall Street Journal journalist Greg Zuckerman has done it again. Following the success of his best-selling The Frackers in 2013 (on Financial Times’ 2014 list of the best summer books in business) and The Greatest Trade Ever from 2009 (a New York Times and Wall Street Journal best seller), he has now ventured even further into the dark corners of finance. Investigating the most secretive of Wall Street’s hedge funds — Jim Simons’s Renaissance Technologies and its quantitative trading fund Medallion — Zuckerman tells a story of a relentlessly assiduous man and the firm he built. 

If we are to believe the calculations in Zuckerman’s appendix — outlandish, but consistent with anecdotes and previous accounts — Medallion averaged 39.1 percent annualized returns from 1988 to 2018, net of fees. And those are extraordinary fees, too: a 5 percent management fee, and a 20 percent performance fee (raised to 44 percent in 2002). Making (minor) losses in only one-fifth of the months in that period, the fund has seen individual years returning much more than that: 98.2 percent (Zuckerman reports 82.4 percent) in the crisis year of 2008 when the S&P 500 lost 38.5 percent is a particularly eye-popping result. 

In comparison, other vastly successful fund managers and investors are not even close: George Soros’s Quantum Fund averaged 32 percent a year from 1969 to 2000; Peter Lynch’s Magellan Fund saw annual average returns of 29 percent between 1977 and 1990; and the Oracle of Omaha, Warren Buffett, saw his Berkshire Hathaway return 20.5 percent between 1965 and 2018 (higher if we exclude its post-crisis performance). 

As Renaissance and Simons have always been secretive — about their lives, their performance, and especially about their trading techniques and strategies — it is rather mysterious how Zuckerman obtained those numbers. Since 2005 the fund hasn’t been open to outside investors, making verification of its returns even harder to come by, and as a privately held company only regulators and auditors have had access to its books. He references “Medallion Annual Reports” and “investors,” suggesting that he acquired non-public information from somebody inside Renaissance. 

In The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, we are told the story of how it happened. We meet colorful characters, financial success, and business matches made in heaven — but also ceaseless struggling, heartbreaking losses, friendships, marriages, and partnerships with flourishing beginnings and abrupt endings. The unofficial title could have been “the revenge of the geeks,” as almost all the Renaissance employees, founders, and co-managers featured by Zuckerman are extraordinary mathematicians, programmers, or physicists — the kind of iconic nerds who stayed clear of sports and won math competitions at the age of 10. 

When asked about his childhood, a long-term former employee of Renaissance, David Magerman, recalls that the school’s computer lab was “where we nerds hid from the football players.” (p. 184). For many of the characters Zuckerman introduces us to, this was a common life trajectory: incredibly bright math geeks who suffered in high school and were determined to one day rise up, change the world, or acquire wealth and status. In that sense, the Medallion fund’s extreme returns are just deserts in a poetic underdog story worthy of the big screen.

For all their academic, professional, and intellectual achievements, the accelerated Ivy degrees and millions and billions of dollars rolling by, the protagonists of the story are not without their share of misery. Simons himself lost his son in a traffic accident, and a few years later another in a scuba diving accident; Ax, an algebraist and co-founder of Renaissance’s predecessor, estranged his children, with whom he only regained contact decades later. 

If I may levy one piece of criticism against Zuckerman’s writing, it is the exorbitant use of a dictionary (who uses words like “cherubic,”“disconsolate,” or “lying supine?”). Much more irritating is Zuckerman’s publisher’s uncanny referral to Simons as “a modern-day Midas who remade markets in his own image,” seemingly oblivious to the mixed metaphors and the incongruity with their subjects. 

The allusion to the divine makes little sense (God made man in his image; Simons neither made or was made by markets, nor did he approach the status of a deity, despite some of his employees’ long-lasting loyalty). The misuse of Midas, the Greek legend of the dangers of avarice, seems wholly inappropriate if not outright cruel. Midas was granted his wishes of turning everything he touched into gold, but accidently fossilized his daughter and ultimately died from starvation when his food turned into gold. 

Perhaps Simons did suffer some cosmic retribution from his wealth and professional success, probably in ways common to many successful businessmen (say, not paying enough attention to family and friends) or maybe losing family members in tragic accidents. Invoking ancient mythological justice as a punishment for his unending strive for puzzle-solving seems rather callous. 

Fortunately, the quality of Zuckerman’s investigative book isn’t much affected by the sloppiness of his editor. Frequently featuring chapter-long tangents portraying this or that leading member or co-executive of the firm, the book feels like an extended long read at the Wall Street Journal or the New Yorker, supplying the reader with a comprehensive feel for life inside Renaissance Technologies. 

In his first careers — plural, as Simons was a man reaching success in many fields — he had extraordinary success as an academic mathematician (developing the Chern-Simons theory and winning the Oswald Veblen Prize in Geometry in 1976) and as a Cold War cryptologist and code-breaker, and he later built a world-class mathematics department at Stony Brook University. When those challenges had been conquered and the work seemed too mundane, Simons, at the age of 40, looked toward financial markets; “He was confident,” writes Zuckerman, that “he could conquer the world of trading.” (p. 41).  

And conquer it he would, pursuing data-mining and machine-learning algorithms strategies before they were mainstream. Indeed, he and his team were often met with hostility: 

Technical traders became targets of derision, their strategies viewed as simplistic and lazy at best, voodoo science at worst … most of their rules arose from a mysterious combination of human pattern recognition and reasonable sounding rules of thumb, raising questions about their efficacy. (p. 124)

For most of the 1980s and 1990s, you didn’t even seem to need them, as legendary investors like George Soros and Peter Lynch managed to make billions of dollars posting equally impressive returns, using human judgment and a style of investing we would today call event-driven macro trading. 

Renaissance’s hiring strategy was to stay away from economists and those with a Wall Street trading background — partly because they rarely possessed the quantitative and programming skills he desired, but more importantly because they wouldn’t have the network (and thus the temptation) to join a competing firm, sharing Simons’s secrets with his competitors. 

One thing Zuckerman’s 360-page book is short on is balancing the exultant financial story. In one of the scant moments where he does, he invokes the objections to mathematically sophisticated trading models from Nassim Nicholas Taleb, the trader-turned-public intellectual whose One Big Idea is

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 frequently than most models suggest. (p. 126)

However much we want to attribute success to Simons’s mathematical foresight and endurance, we cannot derive prescience from the outcomes of a process alone; we need to observe the actual process, the distribution from which the returns are drawn. Judging from Zuckerman’s investigation, Simons and his programmers don’t even know that themselves (p. 59); they fed an incessantly optimizing computer algorithm endless amounts of market data, and decades later it kept rewarding its creators and those entrusting their money with Renaissance. Commenting on the returns, Richard Dewey and Ciamac Moallemi at Bloomberg wrote: 

Even more confounding is that the proliferation of other quantitative hedge funds in recent years hasn’t caused Medallion’s performance to deteriorate. Usually, when one quantitative investor finds a profitable trade, rivals sniff it out, too, and all the competition eventually kills the opportunity. 

To mention just three iconic events that broke more than one quant fund with until-then-impressive returns and sophisticated (Taleb would call them “fragilized”) models: Black Monday (October 19, 1987); the collapse of Long-Term Capital Management (1998); and the so-called Quant Quake (August 6–10, 2007), where the trading strategies of numerous quant funds suddenly fell off a cliff, bankrupting most of them.

Medallion’s predecessor, Axcom, avoided Black Monday; the market volatility following the collapse of LTCM actually benefited Medallion; and while the Quant Quake took Simons and the Medallion team by surprise – Zuckerman reports that Simons even overruled the trading algorithm (pp. 325-27) – they narrowly got through it and ended the month in the black.

Zuckerman isn’t wrong when he writes that “Simons is arguably the most successful trader in the history of modern finance.” (p. xvi). His returns, over such a long time period, speak for themselves. The interesting thing is to find out how Renaissance did it — and that is anybody’s guess. We merely know that Simons and Renaissance built a black-box money machine, and nobody knows what’s in it. 

What that means for the future of financial markets and trading algorithms is the next chapter of this great financial challenge we’ve living through. A great start to understand how it began is to pick up Zuckerman’s latest painstaking achievement.

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