The $25B Day Trader that Even Warren Buffett Acknowledges
Summary
TLDRJim Simons utilized advanced mathematics and machine learning to achieve exceptional returns from short-term stock market predictions with his Medallion hedge fund. By gathering massive amounts of data and hiring brilliant mathematicians to uncover obscure patterns, Simons developed algorithms exploiting human biases. The fund averages nearly 40% annual returns, proving quant trading works yet is infeasible for regular investors. Simons suggests long-term value investing remains a sound approach.
Takeaways
- š² Jim Simons turned $0 into $25 billion through Renaissance Technologies and the Medallion hedge fund, which utilizes advanced mathematics and machine learning to make short-term stock market predictions
- š The Medallion fund averages annual returns of 66% before fees by identifying patterns in massive amounts of market data that most investors miss
- š¤Æ Medallion executes up to 300,000 trades per day, holding positions for only minutes or seconds, to capitalize on short-term inefficiencies
- š” The fund's edge comes from mathematics, data analysis and predictive modeling - areas where humans struggle to compete
- š Simons and his team focus strictly on probabilities, not business fundamentals or qualitative analysis
- š¤ Simons himself was critical for hiring creative thinkers from non-traditional backgrounds, not just Wall Street experts
- šŖ The Medallion fund has been closed to outside investors since 1993 - only Renaissance employees and family can invest
- š¤ Medallion's profits likely come from other speculators and traders, not long-term investors or index funds
- ā Regular investors should not try to mimic Medallion's strategies from home
- š Value investing still works for small investors - the machines have not yet caught up here
Q & A
What was the main secret behind the success of Renaissance Technologies' Medallion fund?
-The main secret was using tons of data and advanced mathematics to develop automated trading algorithms and machine learning models to predict short-term market movements.
What is statistical arbitrage and how did Medallion use it?
-Statistical arbitrage involves identifying market-wide factors that explain stock movements and betting on stocks that have moved the least in the direction predicted by the factors, while betting against those that have moved more than predicted. This is a form of mean reversion.
Who were some of the key mathematicians that contributed to Medallion's success?
-Some key mathematicians were Lenny Baum, James Ax, RenƩ Carmona, Elwyn Berlekamp, Robert Mercer and Peter Brown. Each specialized in different areas like Markov models, algebra, stochastic calculus, game theory and computer science.
What does the Kelly criterion determine?
-The Kelly criterion determines the optimal size of a trading position in order to maximize long-term growth of capital based on the win rate and payoff ratio.
Why hasn't Renaissance's long-term stock fund RIEF been as successful as Medallion?
-Medallion's forecasting methods work very well for short-term predictions like weather forecasts, but degrade over longer time periods. RIEF hasn't matched Medallion's performance since it aims for longer-term stock market forecasts.
Who are likely the counterparties providing profits for Medallion?
-It is likely short-term traders, both large and small, who provide profits for Medallion. These are speculators making emotional decisions without advanced algorithms.
What was Jim Simons' first hedge fund called and why?
-His first hedge fund launched in 1978 was called Monemetrics, combining the words money and econometrics, hinting that he would use math to analyze markets.
How has Medallion's trading volume and frequency changed over time?
-In 2000, Medallion executed 150,000-300,000 trades per day. Today with advances in technology, the volume is likely even higher, with average hold times as low as seconds or minutes.
What is Renaissance's motto that hints at their philosophy?
-Their motto is: "Bad ideas is good. Good ideas is terrific. No ideas is terrible." This shows their focus on creativity and idea generation.
What was the conclusion for individual investors regarding trading vs investing?
-The conclusion was that individuals should still opt for long-term value investing over short-term trading or technical analysis, since that is an area machines have yet to dominate.
Outlines
š From $0 to $25 billion: Simons' journey to building Renaissance Technologies
This paragraph chronicles Jim Simons' background and journey in founding Renaissance Technologies. It covers his early interest in math, getting a PhD, working as a codebreaker, publishing mathematical papers, leading a math department, founding his first hedge fund, and ultimately creating the wildly successful Renaissance Technologies and its Medallion fund in 1988.
š² Medallion fund averages 39% returns using short-term predictive algorithms
This paragraph explains how Medallion achieves its market-beating returns. It uses machine learning and algorithms to make automated short-term predictions based on identifying repeatable patterns in quantitative data. The fund trades frequently, holding positions for only days or even minutes, and profits likely stem from human biases and misjudgements in the markets.
š Medallion requires massive amounts of data to feed its algorithms
This paragraph highlights Medallion's relentless pursuit of data to drive its algorithms. It gathers all forms of quantifiable data, going back decades, to identify signals and correlations. Over 300 employees focus solely on cleaning and preparing data for the predictive models.
š¤Æ Medallion leverages advanced mathematics from leading experts
This paragraph discusses the advanced mathematics powering Medallion's models. Simons hired mathematicians who were specialists in key areas like game theory, algebra, and computer science. Names like Lenny Baum, James Ax, Elwyn Berlekamp and others are mentioned as instrumental contributors.
ā Don't try this at home - Medallion preys on amateur traders
This closing paragraph concludes that Medallion's outsized returns likely come from successfully predicting and profiting against emotional, undisciplined retail traders. It compares Medallion's returns to index and fundamental investors to make this point. The author advises against attempting to replicate Medallion's approach as an individual.
Mindmap
Keywords
š”Medallion fund
š”mean reversion
š”data
š”algorithms
š”short-term
š”probabilities
š”data science
š”quantitative
š”strategies
š”predictions
Highlights
Simons enrolled at MIT in 1955 and was even able to skip the first year of mathematics, thanks to his extensive high school curriculum.
Together with two friends he decided to go from Boston to Buenos Aires, riding scooters. They named the trip āBuenos Aires or Bustā.
In 1978 Simons had had enough of the world of academia though. He wanted new challenges and solving the market, conquering the world of trading, was something which no one had done before, which sparked his enthusiasm.
Medallion didnāt charge the usual rip-off fee of 2/20 that other Wall Streeters did, no, no. They charged 5/20, eventually raising that to 5/44! This is insane numbers, but in Renaissanceās case, it proved to be worth it.
The secret to Renaissanceās and the Medallion fundās success, has been to employ tons of data and advanced mathematics, to develop automated trading algorithms.
Medallionās profits probably stem from human biases and misjudgements.
One of the former CEOs of Renaissance Technologies, Robert Mercer, said it best: "Thereās no data like more data." This became something of a mantra at Renaissance.
As I said before, this book might as well have been called āThe Men who Solved the Marketā, as Simons definitely couldnāt do this alone. He employed various mathematicians who were specialists within their fields.
Since 2003, Medallion has returned an average of 73.7% per year, before fees, while the S&P 500 has returned on average 10.6% per year.
Renaissanceās profits stem from fellow speculators, both large and small. The people who do not have a trading algorithm, which trades without being influenced by emotions.
The forecasting methods that Renaissance uses are similar to weather predictions, ā very useful for saying what is likely to happen in the coming hours or perhaps days, but not useful for longer time periods than that.
Value investing is an area where the machines havenāt caught up to us yet. If you want to learn how to invest in a Warren Buffett way, one of the best methods for learning his secrets, would be to study his most important investments of all time.
Simons came to this conclusion himself, that these must be the people who Medallion is "taking" profits from, and it is good for you to know as an investor too.
What was it that agent Smith said now again?: "Never send a human to do a machineās job."
As a smaller and private investor, Iād still opt for the value investing approach of Warren Buffett, over a day trading, technical analysis approach, any day.
Transcripts
It has been said that a good investor must always strive to crush his most cherished beliefs.
Well, during Berkshire Hathawayās 2021 annual shareholder meeting
Warren Buffett and Charlie Munger crushed one of mine.
What do you think of quants?
Jim Simonsā Medallion fund has done 39% net of fees for three decades which, proves that it works.
But they were very smart.
Yes they got very rich.
Very, very smart.
Very smart and very rich, yes.
And very high grade, by the way.
Jim Simons.
But we are not trying to make money trading stocks.
We donāt think we know how to do it.
Charles Darwin used to say that any time he found evidence that contradicted his previous convictions
he had to write it down in the first 30 minutes because otherwise,
the mind would reject the evidence for cherished beliefs.
Well, how about reading a whole book and making a video about it?
During the last few years, Iāve read tons of books on personal finance and investing,
and I settled down on a conclusion that
value investing and fundamental analysis is the way to go,
while day trading and studying price charts is just pure bogus.
Or, you know, bots trying to sell you something.
Enter Jim Simons.
Jim Simons is the worldās richest mathematician.
Forbes estimates his wealth to be at a staggering $24.6b currently.
He gained most of this wealth through conquering the world of trading by starting
the āquant revolutionā with his company Renaissance Technologies and the Medallion fund.
The Medallion fund has the best track-record of any hedge fund in history.
It has averaged a 62.9% return per year before fees,
and 37.2% net of fees, verses 11% for the S&P 500.
That is the most impressive investment record
Iāve ever heard about, itās even better than Warren Buffettās,
although it has been accomplished with a much smaller capital.
So, should you abandon value investing to become a day trading quant?
Letās not get ahead of ourselves here, but weāll get to that.
In this video, we shall take a closer look at the Medallion fundās success
and reveal a few of its secrets.
Youāll have to stay put for that,
but I think that a quote from agent Smith from the movie The Matrix
may be used to set the stage:
āNever send a human to do a machineās job.ā
This is a top 5 takeaways summary of The Man who Solved the Market.
Written by Gregory Zuckerman.
And this is The Swedish Investor, bringing you the best tips and tools for reaching financial freedom
through stock market tradā¦ investing.
Takeaway number 1: From $0 to $25b
Before getting into the howās of Simonsās incredible success,
letās first have a look at the whatās.
What did Jim Simons do to go from $0 in 1938 to $25b in 2021?
Simons was born in 1938.
Early on, he began to read a lot.
I know, surprise, surprise!
Jim Simons is another one of those successful people who read a lot.
To be honest I do not think that the medium through
which you consume information matters to much,
what matters is that you strive for more knowledge.
Simons enrolled at MIT in 1955 and was even able to skip the first year of mathematics
thanks to his extensive high school curriculum.
He decided early on what he wanted from life - coffee, cigarettes and lots and lots of maths.
Simons was quite the adventures type.
Together with two friends he decided to go from Boston to Buenos Aires, riding scooters.
They named the trip āBuenos Aires or Bustā.
Well, they busted in Bogota in Colombia, but it must have been a crazy trip nonetheless.
While getting his PhD at the university of California, Berkley,
Simons had his first dabble in stocks.
He got up early each morning to drive to Merrill Lynchās office in Los Angeles, just in time
for the opening of the Chicago exchange.
He would stand there to watch prices flash by and make a few trades.
Simons got his PhD by age 23, in 1961.
At age 26 he got a job as a code-breaker at a US intelligence unit, targeting old Soviet Russia.
Simons learned something important about hiring people here.
The unit worked very well while primary focusing on hiring people for their creativity,
ambition and brainpower, rather than any specific expertise or education.
Another important lesson from this place was its motto:
āBad ideas is good.
Good ideas is terrific.
No ideas is terrible.ā
In 1968 Simons published a mathematical paper on something which I find quite difficult
to pronounce, let alone understand:
āMinimal Varieties in Riemannian Manifoldsā.
To this day, the paper has been cited 1722 times, which counts as an incredible success
for a paper on geometry.
Also in 1968, Simons was asked to build and lead a maths department at Stony Brook University.
Itās been said that the extroverted mathematician will look at your shoes during a conversation
rather than his own.
Well, Simons was extroverted, period.
And he had an unusual knack for leading his fellow mathematicians.
In 1978 Simons had had enough of the world of academia though.
He wanted new challenges and solving the market, conquering the world of trading,
was something which no one had done before, which sparked his enthusiasm.
He called his first hedge fund āMonemetricsā,
which was a play of words combining āmoneyā and āeconometricsā.
Simons was hinting that he would use math to analyse the financial markets and score big time.
Simons utilized his unusual combination of being an exceptionally skilled mathematician himself
while possessing some incredible leadership and interpersonal skills to hire
and get the most out of many fellow mathematicians.
He realized early on that he wouldnāt solve this puzzle by himself.
In fact, the book might as well have been called āThe Men Who Solved the Marketā,
but youāll hear more about these people and their contributions later, because thatās more
about the howās than the whatās of this incredible trading success.
In 1982 Simons renamed the company to Renaissance Technologies,
a name that it holds to this day.
In 1988 Simons launched the Medallion fund, which is the most successful hedge fund of all time
in terms of returns on capital.
While others were still relying on instinct and intuitions for their trades, Simons employed
automated algorithms, tons of data and advanced mathematics,
but again we are getting ahead of ourselves.
Medallion didnāt charge the usual rip-off fee of 2/20 that other Wall Streeters did,
no, no.
They charged 5/20, eventually raising that to 5/44!
This is insane numbers, but in Renaissanceās case, it proved to be worth it.
In 1990 the Medallion fund had its first year surpassing a 50% return.
It scored as high as 77.8% before fees for the twelve months.
Simons kept up his leadership skills.
He created a culture of unusual openness at Renaissance.
Moreover, he used smart monetary incentives, where people were paid bonuses,
but only if the company reached certain levels of profit.
This money was paid out over many years to keep people in the firm.
Renaissance had almost no employee turnover.
Simons also had an important role to play in the hiring process.
He wanted people who had little or no connection to Wall Street
and generally accepted business dogmas.
In 1993 the Medallion fund closed to outside investors, from now on it was only available
to employees of Renaissance and their families.
In the year 2000, Medallion had its first year exceeding a 100% return,
achieving a stunning +128.1%.
In 2003, stocks had officially taken over as the most important trading instrument of the firm
from previously having focused on currencies, commodities and bonds.
In 2005, Jim Simons received a personal gain of $1.5b, which was the highest compensation
among any hedge fund manager that year.
Simons retired as CEO of Renaissance in 2009 and handed over the role to two of his colleagues
- Robert Mercer & Peter Brown.
Simons stayed as Chairman, but eventually left that post too, just recently in 2021,
but he remains on the board of directors.
He earned a cool $2.6b with his financial stake in the company in 2020,
reaching an estimated personal wealth of $24.6b.
Okay, letās now get into how Jim Simons (and his colleagues, I should add!) was able
to achieve these stellar results in the Medallion fund.
Takeaway number 2: Medallion is a short-termpredictive algorithm
The secret to Renaissanceās and the Medallionfundās success
has been to employ tons of data and advanced mathematics
to develop automated trading algorithms.
Renaissance was one of the pioneers of using machine learning
and applying it to the world of investing.
Today, this black box algorithm is an exceptional short-term predictor of market movements.
The Medallion fund holds on to positions for an average of a day or so,
but sometimes as little as minutes or seconds.
It executed 150,000 ā 300,000 trades per day back in 2000,
and probably even more of them today.
One employee expressed that Medallionās goal is the following:
ā[To] scrutinize historic price information to discover sequences that might repeat,
under the assumption that investors will exhibit similar behaviour in the future.ā
Simons understood quite early on that the stock market moves because of a complex process
with many, many inputs.
Some of these inputs may be difficult or even impossible to understand.
They may not be related to traditional fundamentals
such as earnings, dividends, interest rates or similar,
but there may be some other, more obscure reason for certain moves.
However, eventually, they will all be reflected in pricing data, so Simons decided to study that data.
What type of human behaviour is it that Medallionis able to take advantage of?
Well, to Simons, it didnāt matter as long as the patterns reappeared with a certain
degree of statistical significance, but it can be interesting to speculate a little.
Medallionās profits probably stem from human biases and misjudgements.
We may have a few of the suspects in Daniel Kahnemanās famous book
Thinking Fast and Slow
Loss Aversion ā people hate losing more than they like winning
- Anchoring ā oneās judgement is skewed based on previous prices and experiences
And - The Endowment Effect ā you like what you already have more than what is objectively sound
One of the core strategies ever since the inception of the fund
has been to rely on mean reversion.
Early on, back in the 80s & 90s, the Medallion fund used simple linear regression models
to plot, for example, the price of crude oil vs the price of gasoline.
If you look at enough data points you can spot a trend line,
a linear relationship between the two assets.
When gasoline is cheap compared to oil, youāll buy gasoline and short oil and vice versa.
Then you wait for the prices to go back to ānormalā, reverting to the mean.
Today, Medallion uses a technique called āstatistical arbitrageā
which is about identifying a small set of quantifiable market-wide factors
that best explain certain stock market movements.
If, for instance, Exxon tends to move in tandem with petroleum prices and interest rates,
Renaissance identifies that.
Then they bet on the stocks that have moved the least in the direction of their market-wide factors
while betting against those that have moved more than the factors predicted.
Again, reversion to the mean.
Today these relationships often consist of multiple variables and the relationships
no longer have to be linear, so they are often difficult to identify for the naked eye.
To identify such relationships the Medallion fund needed data.
Mountains of data.
Takeaway number 3: Medallion requires TONS of data
One of the former CEOs of Renaissance Technologies, Robert Mercer, said it best:
āThereās no data like more data.ā
This became something of a mantra at Renaissance.
Any data that could be quantified and was deemed to have some potential
for predictive value was gathered.
Newspaper stories, internet posts, insurance claims, nothing is too obscure.
An employee named Sandor Straus noticed the need for data early on,
if Simonās wish for a fully automated algorithm was to become reality.
Back then, having more data than your competitors
meant buying books from the World Bank and magnetic tape from various exchanges.
The data went back as far as WW2.
Straus collected more than even Simons thought was necessary,
among other things, he started to collect the intraday tick prices,
betting that it would become useful to them at some point.
Straus even began to model data itself for a while.
There were gaps in the data at certain periods due to unexpected circumstances,
such as when a major flood had suspended Chicago trading.
Sometimes, modelling data was possible, just like it is possible to sometimes determine
the shape of a missing jigsaw puzzle piece.
Today more than 300 people are employed at Renaissance and they have more than 30 people
with PhDs with the primary focus of cleaning up different data feeds so that they have
the best data available for making short-term predictions.
While studying this much quantitative information, Medallion must be careful
as to not run into data overfitting.
If you look at enough data you are bound to find some signals that seem statistically significant
just by pure chance.
For example, a quant investor called David Leinweber had identified that US stock returns
could be predicted by combining the yearly butter production of Bangladesh,
the cheese production of the US
and the population of sheep in Bangladesh and the US (true story!).
For this reason, Medallion always starts to trade new signals with smaller amounts of cash,
gradually ramping up the capital committed as profits roll in.
Takeaway number 4: Medallion is based on advanced mathematics
As I said before, this book might as well have been called āThe Men who Solved the Marketā
as Simons definitely couldnāt do this alone.
He employed various mathematicians who were specialists within their fields.
Among the men who solved the market were:
Lenny Baum, who was an early employee that helped Simons
with something called Hidden Markov Processes.
James Ax, who held the largest stake in Medallionās predecessor, Axcom.
Ax was a great algebraist and exceptional at exploring correlations.
RenƩ Carmona helped incorporate some stochastic differential equations to the models.
He began applying so called Kernel Methods to analyse patterns in the data sets.
He was the first one to implement a full blackbox approach, where they allowed the computer
to teach itself which patterns were most important.
Elwyn Berlekamp, who helped with advanced game theory.
He even founded a branch of mathematics called combinatorial game theory.
Berlekamp had also worked for John Larry Kelly Jr., the creator of the Kelly criterion.
The Kelly criterion determines how large or small a certain trading position should be.
It can be used for value investing too, by the way, something Iāve discussed previously
in a summary of The Dhandho Investor by Mohnish Pabrai.
And then there were of course Robert Mercer and Peter Brown who were appointed
co-CEOs of Renaissance in 2009.
They both brought even more experience with Hidden Markov Processes to the group,
but most of all, they were exceptional computer scientists.
Simons headhunted both of them from IBMās former speech recognition team.
Takeaway number 5: Donāt try this at home!
Hereās a little thought experiment for you.
Since 2003, the Medallion fund has primarily been trading in stocks,
although they also do trades in commodities, currencies and bonds.
Also since 2003, Medallion has returned an average of 73.7% per year, before fees,
while the S&P 500 has returned on average 10.6% per year.
The returns could be explained by the fact that Medallion uses leverage, but to say that
that explains the full overperformance, I think would be a little bit foolish.
No, Medallion is also just on the right side of trades.
This leaves one questioning: Who does it ātakeā these profits from?
Who is on the other side of the trade?
According to a 2019 CNBC article, index investors control nearly half the US stock market.
If you are an index investor and youāve been investing without buying and selling
too much since 2003, you would have received those 10.6% in average returns.
Medallion cannot have āstolenā profits from the index investors, as they will get
the average almost by definition.
Then we have the long-term investors who invest based on fundamentals
and hold over longer time periods.
Thatās the Warren Buffetts of the investing world.
This represents an area where Renaissance and its mathematicians havenāt been able
to produce any above-average profits yet.
In 2005, Renaissance founded a new fund called Renaissance Institutional Equities Fund,
or RIEF, to take in outside capital without risking the profits of Medallion.
Supposedly, this fund would make similar long-term predictions as the Medallion fund
has been doing successfully for the short-term ones.
To this day though, it hasnāt been able to do that.
Thatās a 9.1% return on average for all the full years since RIEFās inception,
verses 9.9% for the S&P.
The forecasting methods that Renaissance uses are similar to weather predictions
ā very useful for saying what is likely to happen in the coming hours or perhaps days,
but not useful for longer time periods than that.
Therefore, it is unlikely that itās the acolytes of Warren Buffett
who have been the pray of Medallion either.
Whoās left?
It is the traders.
Renaissanceās profits stem from fellow speculators, both large and small.
The people who do not have a trading algorithm
which trades without being| influenced by emotions,
who do not have access to the same amount of data, who do not have access to some of
the greatest mathematicians of our time, but who decides to take a short-term gamble
in the stock market, nonetheless.
The people who buy a course in day trading from a āguruā on Udemy and draw trendlines
on a head and shoulder pattern.
Simons came to this conclusion himself, that these must be the people who Medallion is
ātakingā profits from, and it is good for you to know as an investor too.
Do not think that you can do this at home.
The only thing that will happen is that youāll hand over your hard-earned money
to Renaissance or some of the other quants.
What was it that agent Smith said now again?
āNever send a human to do a machineās job.ā
So: - Jim Simons became one of the richest people
on the planet through his Medallion fund
- The fund is a short-term algorithm which
scrutinize historic price information to discover sequences that might repeat,
under the assumption that investors will exhibit similar behaviour in the future
- To do this, the fund requires tons of quantitative data
- Moreover, Simons needed the help of a few of the worldās greatest mathematicians
- Finally, donāt think that you can do this at home.
As a smaller and private investor, Iād still opt for the value investing approach of Warren Buffett
over a day trading, technical analysis approach, any day.
Value investing is an area where the machines havenāt caught up to us yet.
If you want to learn how to invest in a Warren Buffett way,
one of the best methods for learning his secrets
would be to study his most important investments of all time.
In this video, Iāve summarized the 25 most important ones.
Sure, itās a long video, but hopefully thereās a lot of meat in there.
Cheers guys, hope to see you again soon!
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