Last Updated on June 19, 2022 by Quantified Trading
Jim Simons recently stepped back as the chairman of Renaissance Technologies, the asset management group that manages the most profitable fund ever: The Medallion Fund. What are the secrets and trading lessons of the Medallion Fund’s trading strategies? How has Jim Simons managed to get a whopping 66% return on average for over 30 years? What kind of trading or investment strategies has the Medallion Fund and Jim Simons employed?
The magic behind Jim Simons’ trading strategies consists of collecting an enormous amount of data and analyzing the data to find statistical patterns and non-random events in a wide range of markets. Furthermore, Jim Simons and Renaissance Technologies have managed to put together a hard-working and secretive team that generates plenty of testable strategies. The employees have skin in the game, and unfortunately for us outsiders, few of Medallion’s strategies end up outside their offices. However, most of the annual gain is a result of leverage.
Who is Jim Simons? “The greatest trader on Wall Street”
Jim Simons was born in 1938 and studied mathematics at MIT. Later he got his Ph.D. and used his math abilities to break codes for the US National Security Agency (NSA – this was during the Cold War) and teach at MIT.
For his contribution to math and finance, he was elected to the National Academy of Sciences of the USA in 2014.
However, Jim Simons is not known for his mathematical contributions, but for his track record as a hedge fund manager. During his working career, Simons spent considerable time trying to use quantitative models to predict the markets.
In 1978 he quit his job/academia and founded Monemetrics, a hedge fund. At that time quant was an unknown word, and Simons’ fund employed both fundamental and technical approaches. He was moderately successful but felt “gut wrenched” by the emotional swings in the market.
Simons decided to use a purely systematic approach instead to avoid the emotional rollercoasters. Like most traders, he was liable to the most common trading biases. He didn’t really succeed until the early 80s when he managed to put together a decent team of so-called quants. He needed math geniuses and “quants”, not MBAs, and his first employees were from universities or NSA.
The management company that made it all possible is Renaissance Technologies:
Renaissance Technologies – the owner of the Medallion Fund
To our knowledge, Renaissance Technologies manages four funds: Renaissance Institutional Equities Fund, Renaissance Institutional Diversified Alpha, Renaissance Institutional Diversified Global Equity Fund, and the one most famous of them all: The Medallion Fund. All funds are open to outside investors, except for the Medallion Fund which was closed for outsiders years ago (1993?).
The fund which is closed for outside investors, the Medallion Fund, performs the best. This reminds us of the famous quote that Benjamin Graham made:
People who invest make money for themselves; people who speculate make money for their brokers.
The Medallion Fund has outside investors, but for the most part, it manages money for the insiders. Do they keep the best strategies for themselves?
Perhaps, but the main reason for the lower returns for the other funds are different strategies and time frames. The mandate is quite simply different.
Renaissance Technologies is all about quantitative investing over a wide range of asset classes: equities, futures, commodities, forex, perhaps even crypto. Only quants are employed: mathematicians and physicists are the main scientists behind the exceptional performance of the group.
The managers use math and statistical models to both predict and execute trades – all automatic – looking for what the models say are non-random events and not likely caused by chance.
Simons is the founder, biggest shareholder, and manager of the group, even though he most likely did (or do) very little on the strategies’ hands-on development.
Unfortunately, for us outsiders, it’s hard to find any specific trading strategies that are or have been used by Renaissance Technologies or the Medallion Fund (see below). Jim Simons shuns the spotlight and keeps a very low profile, and the employees must sign strict non-disclosure agreements.
We can only make educated guesses but we know for sure they only use quantitative strategies with no discretionary interference.
The Medallion Fund – the best performing fund ever?
The best performing fund in Renaissance Technologies is the Medallion Fund – probably the most profitable fund ever. Presumably, it has made over 100 billion dollars for its owners from 1988 until 2018.
By using quantified trading strategies, the Medallion Fund managed to return on average 66.1% gross before fees from 1988 until 2018. Because it’s such a profitable fund, it charges huge fees on the unit owners: the net returns are “only” 39%. This is a remarkable track record, significantly better than Warren Buffet’s, the only difference being Buffett has managed to grow exponentially (compounded) and over a longer time frame.
What kind of strategies does the Medallion Fund employ?
Unfortunately, we don’t know for sure. The only thing we know for certain is that The Medallion Fund (and all of Renaissance Technologies) uses 100% quantitative strategies to reap profits in the markets. Simons and his team use historical data and look for anomalies and inefficiencies in the markets that have been repeated many times.
We know they use this process when they started out:
- Find a pattern that seems like an anomaly.
- The pattern must be statistically significant. It must have many trades and signals.
- Don’t override the computer (you obviously can’t simulate or backtest that).
- “There’s no data like more data”.
- Don’t ask why. There are so many variables to explain an outcome, and most traders underestimate the vast variables that influence asset prices. No one really knows why. Thus, it doesn’t make sense to go around asking “why”.
- Presumably, the win ratio is pretty low at about 51%.
- Simons and the Medallion Fund conceal their trades. If an asset shows an anomaly at 11 AM, they conceal their trades by not buying exactly at 11 AM.
- They use leverage because of their extreme diversification. Leverage is the main reason for the returns.
Jim Simons has said numerous times that the secret sauce is to have a bunch of bright people throwing ideas around. Add computer power and skin in the game into the mix, and you have some very powerful variables. Trading is all about backtesting trading ideas all the time.
Simons and the Medallion Fund also employ the same tactics and strategies we recommend on this website which is to don’t override your signals and make sure you diversify and trade uncorrelated trading strategies:
- What is the Holy Grail in trading?
- Why build a portfolio of quantified strategies
- Advantages With Quantified And Mechanical Trading Strategies
- What does correlation mean in trading?
- Why is max drawdown important in trading? What is a good drawdown percentage?
The Medallion Fund’s holding time is short, on average probably just a few days, to allow for many trading signals to be sure they are not trading random models. Certain Twitter feeds state Medallion does more than 150 000 trades per day. The fund is not investing – they are trading. And they don’t override their models. (Further below in the article, some hints are revealed.)
Because of short-term trading, Simons decided early on to cap the fund to avoid becoming too big. The main goal is good returns, not assets under management.
Furthermore, they most likely use a lot of pairs trading and market-neutral strategies. They recruited most of the pairs trading desk in Morgan Stanley when pairs trading was still in its birth (80s?).
Manpower and datapower are needed to find all the patterns and anomalies the managers are looking for. The fund has gradually employed more and more people but only accepted employees with a math or physics background – most of them with PhDs. Anecdotal evidence is not used and God forbid discretionary trading.
Because the Medallion Fund only can turn around a certain amount of money before their strategies deteriorate, a lot of cash has been handed back to the unitholders. If they had not returned cash back to the owners, 100 000 invested in 1988 would now be worth an unbelievable 4 010 907 000 000 USD (not considering the management fees)!
This is the magic of compounding, but it only works up to a certain point where it becomes impossible to compound because of the size, hence the return of funds to the owners.
The Man Who Solved The Market – Gregory Zuckerman
Gregory Zuckerman has written the bestseller The Man Who Solved The Market – How Jim Simons Launched The Quant Revolution. The book is mainly about Jim Simons, but it’s also the history of the Medallion Fund.
Unfortunately, the book reveals very little about Jim Simons’ strategies, and we can only extract ideas by reading between the lines. This is not Zuckerman’s fault but due to the secrecy of the managers.
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 James Simons started in the 70s, Simons was not very successful. Jim Simons’ early strategies, mainly in commodity futures, showed some promise, but they (Simons, Ax, and Baum) lacked practical experience and nearly cornered one market (potatoes?). They were fantastic good in math and statistics, but they lacked hands-on practice and understanding of the markets.
Ironically, one of the main problems initially was that they didn’t have 100% trust in their strategies. Simons several times interfered and overruled the systems and strategies, 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.
The idea behind the fund was to employ huge amounts of data to construct strategies in any market and time frame that generated a lot of observations. The reason why they wanted to have many different markets and time frames is because of diversification.
After failing to develop any stable equity strategies, the breakthrough came in the early 90s 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 later 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 Medallion 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 Medallion Fund’s exact strategies, except for the extremely secretive managers and owners. The only thing we know is that they use zillionbytes of data to find correlations and relationships in their search for statistical anomalies. However, Gregory Zuckerman indicates that their first trading strategies were mainly mean-reverting. We also suspect the Medallion Fund uses a lot of seasonal trading strategies.
Moreover, the Medallion Fund’s strategies had short time frames, lasting from day trades to no more than a couple of weeks. Why did the Medallion Fund have such a focus on developing mainly short-term quantitative investment/trading strategies?
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. To get statistical significance, they needed huge data samples.
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 the Medallion Fund. To have any meaningful statistical advantage, they need a lot of observations to make a significant prediction. This is the main reason for Medallion’s goal of developing mostly short-term strategies and not long-term investment strategies.
The Medallion Fund makes only a tiny profit per trade. They use leverage to boost returns and this explains much of the fantastic returns of the Medallion Fund. If we strip out the leverage, the return most likely would not be that fantastic:
The Medallion Fund’s leverage
The Medallion fund has at all times used leverage, probably substantial leverage many times the equity. On the internet we have seen numbers indicating their gearing is on average 10, even going up to 20 at times. Obviously, this explains a lot of the returns. 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 about James Simons’ trading strategies, but a few of their principles are worth mentioning.
My own personal lessons and takeaways, in just keywords, are these:
- Trade often
- Trade many markets to get uncorrelated returns
- Diversify to different markets and time frames
- Many data points are required to make a meaningful trading/investment strategy
- Aim for a “market neutral” portfolio
- Don’t worry about “why”
- 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
We have in previous articles written about the benefits of trading many uncorrelated strategies, just like the Medallion Fund does:
- Why build a portfolio of quantified strategies
- What is the Holy Grail in trading?
- What does correlation mean in trading?
Jim Simons and how to succeed with quant trading
In a speech directed to students, Jim Simons once gave the five guiding principles in life (see below). We believe they apply to aspiring quants as well:
- Don’t run with the pack. Be original.
- Find good partners. You can’t do it all by yourself.
- Be guided by beauties. Math is beauty. A well-run business is a beautiful thing.
- Persistence. Good things take time to materialize.
- You can’t avoid good and bad luck. Hope for good luck! (Jim Simons has lost two sons.)
Do you want Trading Edges delivered to you monthly?
As a friendly reminder, this website offers monthly Trading Edges, 100% quantified (both backtested and tested out of sample) with precise buy and sell signals. We might not be geniuses like the managers in the Medallion Fund, but we have scored decent over 20 years:
Excerpts, takeaways, and lessons from the book about Jim Simons and the Medallion Fund
Below are some excerpts, takeaways, and lessons from the book that gives some insight into the Medallion Fund’s trading strategies (and Renaissance Technologies):
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)
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