How Jim Simons Made 66% A Year By Using Quant Strategies (The Medallion Fund)

Last Updated on January 24, 2021 by Oddmund Groette

Jim Simons recently stepped back as the chairman of Rennaissance Technologies, the asset management group that manages the most profitable fund ever: The Medallion Fund. Simons is the founder, biggest shareholder, and manager of the fund, even though he most likely did very little on the strategies’ hands-on development.

The Medallion Fund is famous for its 100% quantitative approach to reap profits in the markets. The fund has gradually employed more and more people but only accepted employees with a math or physics background – many with PhDs. Using quantified models, the fund managed to return on average 66.1% gross before fees from 1988 until 2018. This is a remarkable track-record, significantly better than Warren Buffet’s, the only difference being Buffett has managed to grow exponentially and over a longer time frame.

Gregory Zuckerman has written the bestseller The Man Who Solved The Market. The book is mainly about Jim Simons, but it’s also the history of the Medallion Fund. I just finished reading the book, and below I summarize my main takeaways from the book:

Jim Simons and how the Medallion Fund started:

When he started in the 70s, Simons was not very successful. The early systems, mainly in commodity futures, showed some promise, but they (Simons, Ax, and Baum) lacked practical experience and nearly cornered one market (potatoes?).

Ironically, one of the main problems initially was that they didn’t have 100% trust in their systems. Simons several times interfered and overruled the systems, usually to the detriment of the strategy.

The breakthrough didn’t come until 1988 when Simons set up a new fund: The Medallion Fund. So far, they had only traded futures, reasonably successfully, but Simons believed the big money was in the stock market. After failing to develop any stable equity systems, the breakthrough came in 1993/94 when Bob Mercer and Peter Brown were hired from IBM. However, big money didn’t come until the turn of the millennium when equity trading became the best cash generator. Bob Mercer is the most known of these two, mainly because he supported Donald Trump for the presidency in 2016, which led to his departure from the firm in 2017.

The performance of the Medallion Fund:

In Appendix 1 of Gregory Zuckerman’s The Man Who Solved The Market, Zuckerman has been so kind to assemble the annual returns for the fund:

Most of the owners and investors in the fund are its employees. Jim Simons’s wealth is estimated at 20-30 billion! Likewise, the lieutenants, Peter Brown and Bob Mercer, are most likely billionaires as well.

How did the Medallion Fund make such extraordinary returns?

No one really knows their exact strategies, except for the extremely secretive managers and owners. The only thing we know is that they used zillionbytes of data to find correlations and relationships. Moreover, the strategies had short time frames, lasting from day trades to no more than a couple of weeks. The logic was simple: if they based their strategies on, for example, annual data, they only have 100 observations over 100 years. This is too little to make any meaningful models, and thus they looked for short-term patterns involving huge datasets and factors.

The Medallion Fund is not a high-frequency fund but likes to think of itself as a casino. As we all know, the casino has a pretty stable income because of the statistical advantage. But for this advantage to work, the casino needs a high turnover. The same logic is behind Medallion. To have any meaningful statistical advantage, you need a lot of observations to make a significant prediction. This is the main reason for their short-term strategies.

Medallion makes only a tiny profit per trade. They use leverage to boost returns:

The Medallion Fund’s leverage:

The Medallion fund has at all times used leverage, probably substantial leverage many times the equity. In 2007 the fund was in serious problems, but somehow narrowly escaped. The quote below is taken from pages 257-258:

On Wednesday, things got scary. Simons, Brown, Mercer, and about six others hustled into a central conference room…. One basket of stocks had already plunged so far that Renaissance had to come up with additional collateral to forestall a sale…. If losses grew, and they couldn’t come up with anough collateral, the banks would sell Medallion’s positions and suffer their own huge losses.

The book indicates the fund was just hours from margin calls. Perhaps it was just a coincidence that Medallion didn’t end up as LTCM – the geniuses who failed? Who knows, if the market had continued against them, Medallion perhaps would have been on the graveyard. They were lucky to escape “tail-risk” – the markets turned around at the last moment. Some lenders would have suffered alongside Medallion, creating rip-on effects through the financial system.

Key takeaways from the Medallion fund:

What can quant traders learn from the book? As mentioned, there are no hands-on specifics, but a few of their principles are worthwhile mentioning. My own personal takeaways, in just keywords, are these:

  • Trade often
  • Trade many markets
  • Many data points are required to make a meaningful strategy
  • Aim for a “market neutral” portfolio
  • Scale in, scale out
  • Math trumps intuition
  • The logical strategies are arbed away
  • Mean reversion is the lowest hanging fruit
  • Leverage bites
  • Most quant traders fail

Below are some excerpts from the book that gives some insight into their thinking and strategies:

By then, Simons had spent twelve full years searching for a successful investing formula. Early on, he traded like others, relying on intuition and instinct, but the ups and downs left Simons sick to his stomach. (Page 2)

We’re gonna do things better than humans can, Berlekamp responded. (Page 3)

Here’s what was really unique: The paper didn’t try to identify or predict these states using economic theory or other conventional methods, nor did the researchers seek to address why the market entered certain states. Simons and his colleagues used mathematics to determine the set of states best fitting the observed pricing data; their model then made its bets accordingly. The why’s didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states. (Page 29)

“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep”, Simons said. “A pure system without humans interfering.” (Page 56)

If a currency went down three days in a row, what were the odds of it going down a fourth day? Do gold prices lead silver prices? Might wheat prices predict gold and other commodity prices? Simons even explored whether natural phenomena affected prices. (Page 57)

Berlekamp hadn’t worked on Wall Street and was inherently skeptical of long-held dogmas developed by those he suspected weren’t especially sophisticated in their analysis. He advocated for more short-term trades. (Page 108)

Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future. Simon’s team viewed the approach as sharing some similarities with technical trading. The Wall Street establishment generally viewed this type of trading as something of a dark art, but Berlekamp and his colleagues were convinced it could work, if done in a sophisticated and scientific manner – but only if their trading focused on short-term shifts rather than longer-term trends.  (Page 108)

Berlekamp also argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple of 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. (Page 108)

Simons and his researchers didn’t believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to the anomalies signaling opportunity. They also didn’t think it made sense to worry about why these phenomena existed. All that mattered was that they happened frequently enough to include in their updated trading system, and that they could be tested to ensure they weren’t statistical flukes. (Page 109)

Some of the trading signals they identified weren’t especially novel or sophisticated. But many traders had ignored them. (Page 112)

Simons was a mathematician with a limited understanding of the history of investing.  (Page 119)

“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.” (Page 153)

“Any time you hear financial experts talking about how the market went up because of such and such – remember it’s all nonsense”, Brown later would say.  (Page 199)

By 1997, though, more than half of the trading signals Simon’s team was discovering were nonintuitive, or those they couldn’t fully understand. (Page 203)

“If there were signals that made a lot of sense that were very strong, they would have long-ago been traded out”, Brown explained. “There are signals that you can’t understand, but they’re there, and they can be relatively strong.” (Page 204)

The obvious danger with embracing strategies that don’t make sense: the patterns behind them could result from meaningless coincidences. If one spends enough time sorting data, it’s not hard to identify trades that seem to generate stellar returns but are produced by happenstance. (Page 204)

Never place too much trust in trading models. Yes, the firm’s system seemed to work, but all formulas are fallible…….”LTCM’s basic error was believing its models were the truth, Patterson says. page (Page 213)

“There’s no data like more data,” Mercer told a colleague, an expression that became the firm’s hockey mantra. (Page 221)

In 2002, Medallion managed over $5 billion, but it controlled more than $60 billion of investment positions…. (Page 225)

On Wall Street, traders often are most miserable after terrific years, not terrible ones, as resentment emerge… (Page 233)

All models are wrong, but some are useful. (Page 245)

The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. (Page 272)

Mercer likely wasn’t sharing his firm’s exact trading edge – his larger point was that Renaissance enjoyed a slight advantage in it collection of thousands of simultaneous trades, one that was large and consistent enough to make an enormous fortune. (Page 272)

The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. we find them across time, across risk factors, across sectors and industries.” (Page 273)

For all the advantages quant firms have, the investment returns 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. (Page 313)

They propose hypotheses and then test, measure, and adjust their theories, trying to let data, not intuition and instinct, guide them. (Page 316)

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. (Page 317)

Simons shared a few life lessons with the school’s audience: “Work with the smartest people you can, hopefully smarter than you…be persistent, don’t give up easily.” (Page 326)


Disclosure: We are not financial advisors. Please do your own due diligence and investment research or consult a financial professional. All articles are our opinion – they are not suggestions to buy or sell any securities.