The Money Making Machine

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Have you ever wondered what it takes to be successful trading financial markets? The ability to analyze a mountain of financial literature? a knack for reading charts? Well, not if we go by the track record of the world’s richest mathematician, Jim Simons. All you probably need to make billions from markets is the good old math.

This video, along with Jim Simon’s interesting story, is also a review of the book “The Man Who Solved the Market” by Gregory Zuckerman.

Before we look at the story, if you find this type of content useful why not look at our library of stock trading material, covering strategies, books, theories and back test studies. You can also get my own personal breakout strategy devised from over 30 years of experience. Ok let’s get into the story….

From 1988 to 2018, Jim Simons, with his team, delivered average annual returns of 66% before fees and 39% after fees trading financial markets. To put this in perspective, a 10,000 investment, would have grown to 1.4 million net of fees in 15 years and to 271 million in 31 years. That’s an incredible return considering that the same money invested in the NASDAQ, one of the best performing indexes, would have grown to a much lower £27 million.

Simons’s firm, Renaissance Technologies raked in trading gains of more than $100 billion in its flagship Medallion Fund. As a result, Jim’s personal fortune also swelled to more than $28 billion.

The craziest part of the story is that Jim and his team had no background in trading or investing, instead, they were a set of mathematicians, physicists, statisticians, and computer scientists bunched together to literally solve the markets.

The returns at the Medallion Fund were so good that investors queued up even when Medallion charged a jaw-dropping 5% fixed and 44% performance fee to the investors in the fund, which is sharply higher than the typical 2% fixed and 20% performance fee structure prevalent in the hedge fund industry.

So, what made Medallion so special and how did Jim Simons make so much money from math? Let’s find out.

Simons was a math buff since he was 3 years old and was always preoccupied with numbers, shapes, and slopes. He went on to pursue his love for math, receiving a bachelor’s degree in mathematics from the Massachusetts Institute of Technology in 1958 and a PhD in mathematics from the University of California, Berkeley in 1961.

Simons' first short-lived encounter with financial markets was in the late 1950s when he was working on his doctoral thesis.

In 1964, Simons took an offer from the Institute for Defense Analyses (IDA), an elite research organization that hired mathematicians from top universities to assist the National Security Agency. At IDA, the idea to build mathematical and statistical models to solve the markets was seeded in Simon’s mind.

At the IDA, Simons and his colleagues were tasked with securing US communications and making sense of the stubbornly impenetrable Soviet code. It was high-tech stuff that gave Simons the adrenaline rush he always craved for.

While working at the IDA, Simons learned how to develop mathematical models to discern and interpret patterns in seemingly meaningless data. He began using mathematical tools like statistical analysis and probability theory, which would influence his work at the IDA and later in Renaissance.

Simons also learned an important thing about hiring and leading people at the IDA. The unit hired people, mostly doctorates, for their brainpower, creativity, and ambition rather than for any specific expertise or background. The IDA let researchers find problems to work on and be clever enough to solve them.

Simons accomplished a lot at the IDA, earning accolades from his team and seniors, but he was later fired in 1968 due to a disagreement with his bosses regarding the US’ involvement in the Vietnam war.

Post getting fired from the IDA, Simons was appointed chairman of the math department at Stony Brook University, where he spent 10 years building a world-class mathematics university department.

In 1977, the speculation bug finally bit Simons when he made close to a 10x return, trading sugar using a quantitative econometric model developed by a friend Charles Freifeld. Though the return was possibly luck, because prices plummeted soon after, contrary to what the model predicted. Simons was however sure that the idea of using mathematic models to trade markets held promise.

The world of currencies had then begun to float freely and Simon’s thought it was the perfect time to dabble in currency trading. He started his trading firm Monometrics, a name combining “money” and “econometrics” and signifying that Simons would use math and financial data to conquer the world of trading.

Being an alien to the world of financial markets, Simons enjoyed a unique perspective on markets. As per him, markets follow some defined patterns and have a non-random structure, which makes it difficult to rely on traditional research, savvy, and insight. He considered markets as one chaotic system that can be statistically modeled with vast amounts of data to find tradable patterns.

Unsure of where to start, Simons turned to his ex-colleague at IDA, Leonard Baum, with whom he wrote a research paper on predicting price movements.

Baum, excited by the intellectual challenge, helped Simons build a model that directed Monometrics to buy currencies if they moved a certain level below their recent trend line, and sell if they veered too far above it. The system made money and seemed to work well. Simons subsequently raised close to $4 million to launch a hedge fund named Limroy.

With some early winnings in currency trading based on data and hunches, Simons and Baum assembled a small team to build sophisticated models that would identify profitable trades in currency, commodity, and bond markets.

Baum’s system didn’t work well for long, and the losses piled up, forcing Simons to move to the traditional style of trading, looking for undervalued investments while reacting to market-moving news, trading with $30 million of his own and clients' money.

The traditional way of trading left Simons deeply unsettled as he hit a few bad trades in a row.

Baum left Simons on differences in trading style and Simons turned to James Ax, an ex-colleague at Stony Brooks to solve the markets with him.

The team kept on collecting vast amounts of data and built simple trend-following models, similar to that of which trader Paul Tudor Jones followed successfully. The approach delivered reasonable results, But Simon’s wanted more.

Thanks to the returns and Simons’ technology investments in Limroy, Monometrics had 100 million dollars under its fold, with Simons's personal fortune close to $50 million.

As investors got uncomfortable with Simons’ technology investments, Simons shut down Limroy to found the hedge fund Medallion, a name that the fund carries to this day.

Soon after, Simons and Ax had a falling out as Ax focused more on long-term bets, which were cash guzzlers while Simons advocated short-term bets. Ax sold his stake to Elwyn Berlekamp, a mathematics researcher who had been working with Ax for some time.

Berlekamp always advocated for short-term trading and committed himself to the firm’s short-term trading models. The 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.

The idea was to trade frequently with a slight statistical edge. Rather than having a few big winners, have many small winners and even smaller losers. The law of large numbers would be on the fund’s side, just as it is for casinos.

Simons and the team left the trading signals entirely on the system, stopping instinct-based trades. The enormous amount of data they gathered threw out many short-term anomalies that repeated with a certain consistency. For example, Monday’s price action often followed Friday’s, while Tuesday saw reversions to earlier trends. So, the model would buy late in the day on Friday and sell early Monday, cashing on what they called the “weekend effect”.

While some anomalies had clear reasons, some were repeated with consistency without clear explanations. Simons and his researchers didn’t bother about “why” certain patterns repeated. If the patterns were repeated frequently enough, they were included in the system.

By late 1989, Berlekamp and his team were reasonably sure about their trading system. They were trading bonds, commodities, and currencies with their revamped system. Most of which were short-term trades, the average holding period for trades came down to one and a half days from one and a half weeks.

The results were almost immediate, and Medallion scored 55.9% gains in 1990.

Enthused by the results, Simons started focusing a lot more on the hedge fund.

Soon after, Berlekamp got tired of Simons's frequent nibbles to tinker with the system and exited the firm, selling his stake at six times the price he paid to Ax sixteen months earlier.

After Berlekamp left, Simons asked Henry Laufer, a well-regarded mathematician at Stony Brook to take up the head researcher role. Laufer along with Patterson, another mathematician, further improved the Medallion trading system by adding an early form of machine learning, prioritizing trades based on probability, and position sizing the trades accordingly based on the profit potential.

The results swelled at Medallion with a 25% return in June 1994 alone, and a 71% return in the entire year. The great returns attracted investors by the truckload, taking Medallion’s fund size to $600 million in 1995.

Medalion stopped accepting new money in 1993 due to the markets not being deep enough to handle any more money.

Simons's only way out of this problem was to master stock trading. Stock markets had much more depth and could take several billions of dollars in trade.

Medalion did not have big success trading stocks. At the same time, some of Simons’ rivals like David Shaw had a significant lead in successfully trading stocks using a scientific approach, managing over $200 million, four times what Simons was managing at the time.

Simons developed a stock trading model with Robert Frey, David Shaw’s ex-colleague at Morgan Stanley’s. The model mostly had disappointing results. Frey deployed a market-neutral, reversion-to-mean strategy to trade the stock market, focused on whether relationships between clusters of stocks returned to their historic norms. For example, it would sell extended securities and buy oversold securities and wait for them to revert to their fair values.

Laufer, Patterson, and other staff saw and slandered the fledgling stock trading system. To give it another shot, Simons, hired Peter Brown, a mathematician, and Robert Mercer, a computer scientist from IBM’s speech recognition group.

Brown and Mercer improved Frey’s system by developing an elaborate stock-trading system that featured a half-million lines of code. With some tinkering and fixing a few bugs, the system was up and running, ready to bring in billions of dollars in profits to Renaissance.

By 1997, Medallion had a three-step process to discover trading signals.

1. Identify anomalous patterns in historic pricing data;

2. Make sure the anomalies were statistically significant, consistent over time and nonrandom; and

3. See if the identified pricing behavior could be explained in a reasonable way.

By 2000 the fund managed to grow to $4 billion. Medallion’s stock trades were not high-frequency scalping trades, and were neither typical swing or positional trades. The idea was to focus on opportunities that occurred frequently and lasted for a period ranging from minutes to a few days. The fund kept on accumulating smaller returns, enhanced by leverage, and building huge returns overall.

The market-neutral portfolio of longs and shorts kept the portfolio risk extremely low. Medallion’s sharp ratio, a measure of risk-adjusted return, averaged 2 in the 1990s, double that of S&P500. The ratio further soared to 6 in 2003 with some improvements in the trading system. This essentially meant that there was very little risk of the fund losing money over a whole year.

Medallion’s returns soared with a 98.5% return in 2002 and a 33% return in 2001, significantly beating the S&P 500 which delivered 0.2%, whilst other hedge funds averaged 7.3%.

In the subprime mayhem of 2008, Medallion's market-neutral system resulted in returns of 82%, earning Simons over $2 billion in personal profits.

The Medallion fund bought out its last investor in 2005, making the fund an investment vehicle for Renaissance employees and their families only. The fund continued to rake in super-performance afterward, making the Renaissance employees and their families multi-millionaires.

Apart from using lots of data, mathematics, and computers to solve the markets, the thing that brought Renaissance huge success was Jim and his team’s ability to hire smart people, motivate them to solve pressing financial market puzzles, and retain them through adequate and plentiful compensation.

Renaissance employees received bonuses every six months, but only if Medallion surpassed a certain profit level. It didn’t matter if staff uncovered new signals, cleaned data, or did other lower-profile tasks; if they distinguished themselves, and Medallion thrived, they were rewarded.

As Simons repeatedly advocated for the Medallion fund, the system was god at cashing in on short-term anomalies, and It is the fellow short-term traders and speculators who make these small short-term gains a possibility for Medallion, and other smart traders, although these opportunities disappear if the time horizon is increased.

This brings us to an important point; one of the key factors for medallion’s consistent returns is to take smaller profits and compound them over and over. Even when you are right half of the time, you can build a fortune with a favorable risk reward ratio.

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