Welcome back to Patrick Boyle on Finance. As most of my regular viewers know I have
been a trader for over twenty years, and from the very start of my career I have taken a
quantitative approach to researching and executing trading strategies. A quant trader is just a trader that builds
statistical models to test trading strategies rather than relying on intuition and experience. Quants try to take a scientific approach and
work out what drives price changes in markets. This approach really appealed to me in my
early years as a trader, because I had no experience to rely on, and I was able to look
at what other people did, and test all of the different approaches to see which rules
worked and which ones didn’t. Today the approach still appeals to me, both
because I have become good at testing systems, but also because I feel that removing emotion
from trading tends to improve returns. When you look at the returns of different
quant traders, you will often notice that there is not a lot of overlap, and that is
because different traders look for different types of trade that appeal to them. Different traders will have different risk
management rules, trade different financial products or just have differing opinions as
to what the cut off is for a good trading signal, thus as long as they are not trading
a cookie cutter strategy, you wouldn’t really expect them to be highly correlated. As traders and investors, we can learn a lot
about how to approach a problem based on how people have tackled similar problems in the
past, so, let’s take a look at the history of quantitative trading. You might expect me to begin this story in
the 1960’s, but we are going to go back a bit further in time than that, because quant
trading involves collecting and analyzing price data, and the first historical examples
of this being done are the thousands of clay tablets that archeologists unearthed in central
Turkey dating back to around 2000B.C. Ancient Babylonian traders recorded the prices of
agricultural crops, sliver and gold on clay tablets so that they could be analyzed and
used to forecast future price moves. They stored these clay tablets in private
archives. A massive fire destroyed the building where
they were stored, which baked and preserved the clay tablets, leaving a record of ancient
price histories with a level of detail which wouldn’t be matched until the merchant houses
of the Italian Renaissance began to document their trading activities. So as far back in history as we can go, we
see traders trying to learn something from price data. We also learn the importance of backing up
your data. Next up we have Thales, the ancient Greek
mathematician, astronomer, and pre-Socratic philosopher who achieved riches from an olive
harvest by predicting the weather. According to Aristotle’s account, Thales
put down a deposit during the winter on all the olive-presses in Miletus, which would
allow him exclusive use of the presses after the harvest. Because the harvest was in the future, and
nobody could be sure whether the harvest would be plentiful or not, he was able to secure
the contracts for a very low price. From the olive press owners’ point of view,
they were protecting themselves against a poor harvest by earning at least some money
up front regardless of how things turned out. Thales’ bet paid off, big time. There was a huge harvest and heavy demand
for the olive presses. Thales held the monopoly and was able to rent
them out at a huge profit. Either he was an expert forecaster, or he
had calculated that a bad harvest would not lose much money for him, whereas the upside
of a good harvest might be enormous. “Thus he showed the world that philosophers
can easily be rich if they like, but that their ambition is of another sort”, according
to Aristotle. Of course today we have the counterexample
of George Soros, who wanted to become a philosopher, but instead became rich. Sometimes things just don’t work out for
you. Our next historical example is Christopher
Kurz, a sixteenth-century Antwerp trader, who claimed to be able to forecast prices
of commodities up to 20 days in advance using a technical trading system based on back-tested
astrological signals. The link between quantitative analysis and
astrology are a bit comical in this day and age, but at the time, astrology was a way
of life, applied to wide-ranging areas of human endeavor including warfare and medicine. Thales had also made his meteorological predictions
based on the movements of the stars and planets. If Kurz had just based his research on astrology,
he would not really have made the list, but he also tried to back test his signals deducing
certain credible principles along the way, such as the idea that prices of agricultural
commodities often move in long persisting trends. To this day most commodity trading advisors
are classified as trend followers. I’m sure a few of them are probably astrologers
too. Our next example comes from the Dojima Rice
exchange in Japan. The Dojima exchange was initially a marketplace
where people came to trade physical rice, but in 1710 a system of using coupons which
promised delivery of rice at a future point in time became popular. From this, a secondary market of coupon trading
emerged (The Dojima Rice Exchange had become the first futures exchange). The biggest speculator at the time was Muna–Hisa
Homma. Other traders at the time referred to him
as the god of markets. He developed the “Japanese candlestick”
charting method which plots open, high, low, close market prices over a given length of
time, formulating his own version of technical analysis, which remains popular to this day. In my early days as a trader I tested hundreds
of candlestick patterns and didn’t find many that were predictive, but collecting
and organizing this data to search for patterns was a definite step in the right direction. We are told that Homma’s “ultimate principle,”
was that “when goods become extremely expensive, they then must become inexpensive again.” So it would appear that he was a mean reversion
trader rather than a trend follower. Stories from the time claim that Homma managed
to establish a network of employees spaced every four miles along the road between Sakata
and Osaka (a distance of just under 400 miles) to communicate market prices. This can be thought of as an early version
of the fast data lines that high frequency traders use today. For our next example we move to London in
the 1800’s where there was a roaring trade in detailed price charts that economists prepared
and sold to financial speculators for analysis. Later in the United States, Charles Dow, who
devised the Dow Jones Industrial average and helped launch the Wall Street Journal popularized
modern technical analysis. While technical traders are often targets
of derision today, some top traders like Stanley Druckenmiller and Paul Tudor Jones are known
to rely on technical analysis to confirm their investing ideas. Professor Andrew Lo of MIT argues that technical
analysts were the forefathers of quantitative analysis, however their methods were never
subjected to independent and thorough testing, and most of their rules arose from a mysterious
combination of human pattern recognition and reasonable sounding rules of thumb, raising
questions about their efficacy. I made a video around a year ago on testing
the trading adage “sell in may and go away” which I will link to at the end of this video
So, Modern quantitative trading really appeared in the United States in the mid 1960’s when
mathematicians and statisticians with access to computers and market data began analyzing
markets. Edward Thorp was probably the first modern
mathematician to use quantitative strategies to invest sizeable sums of money beginning
in 1964. Thorp was an academic who had worked with
Claude Shannon, the father of information theory. After reading books on technical analysis
as well as Security Analysis by Graham and Dodd Thorp writes in his autobiography that
he was surprised and encouraged by how little was known by so many. Another professor around the same time at
Berkeley named Victor Niederhoffer began writing a number of papers on anomalies in stock market
behavior. His 1966 paper Market Making and Reversal
on the Stock Exchange is considered the first paper on statistical arbitrage and market
microstructure. Niederhoffer used innovative methods to search
for opportunities in stock markets, such as his paper ‘The Analysis of World Events
and Stock Prices’, which used the font size of news print to determine the relative importance
of news events and measure how they affected the stock market. He left academia in 1972 to launch a quantitative
hedge fund. These quant trading pioneers had strong backgrounds
in statistics along with access to computers and price data. They got their start around the same time
that the efficient markets hypothesis was becoming popular, but instead of accepting
it as gospel, they did their own analysis. I’d strongly recommend reading both of their
autobiographies, which not only demonstrate their way of thinking, but are also really
entertaining reads. Through the 1970’s these quant trading pioneers
were amongst the top returning traders, but they still went largely unnoticed by Wall
Street. They made outsize returns at a time when there
were very few quant traders. Throughout the 1980’s mathematicians and
physicists were recruited to work on Wall Street and in The City of London, but they
were mostly tasked with building derivatives pricing models. They were nicknamed rocket scientists by the
traders who at the time thought rocketry was the most advanced branch of science. Emanuel Derman, who wrote the excellent autobiography
‘My Life as a Quant’ describes arriving at Goldman Sachs in 1985 and instantly noticing
the shame associated with being numerate – how things have changed… In the early 1980’s a London based sugar
broker charged his son, Michael Adam with updating the commodities charts for the firm
– Michael automated the process using computers, and then began searching for trading indicators. He hired in an Oxford classmate (and computer
programmer) Martin Leuck to assist. They then recruited Cambridge graduate David
Harding, to the team. Michaels father fired the team after a while(well
he fired two of them and kept his son), viewing their work as time wasting. They went out on their own and launched a
fund, named AHL for the first letter in each of their last names. They built quantitative models that traded
trends in commodities, going on to be amongst the most profitable traders at the time. After being bought out by MAN group the three
split up Harding went on to launch Winton Capital and Leuck launched Aspect Capital
– Michael Adam changed his name to Mike Marlin and became a musician. Today AHL, Winton and Aspect are amongst the
largest quant funds in the world, and you can find Mike Marlins music on Spotify. In the mid 1980’s the investment banks dipped
their toes into the world of quant trading. Morgan Stanleys Automated Proprietary Trading
group was started in 1985 by Gerry Bamberger a computer scientist who noticed that traders
executing big blocks of shares were temporarily moving the market. He built a database tracking the prices of
various paired stocks - Stocks like Home Depot and Lowes or Coke and Pepsi, that might be
expected to move somewhat in line with each other. If a big block trade moved one of the stocks
but not the other, he could make money betting on these price spreads returning to their
historic price levels after the block trade was done. The APT group at Morgan Stanley started the
careers of people like David Shaw of DE Shaw fame and Robert Frey who went on to develop
the pairs trading approach at Renaissance technologies. Morgan Stanley shut down the group in the
late 1980’s after only a few years, unhappy with how highly paid the traders were and
nervous about the risk. They squandered some of the most lucrative
trading strategies in the history of finance by doing this. In 1988 code breaker and mathematician James
Simons launched Medallion Fund, which went on to be the highest returning hedge fund
in history, I’ll put a link in the description below to my video on James Simons and to the
excellent biography by Gregory Zuckerman that I based it on. The same year, David Shaw launched DE Shaw. The world has changed significantly over the
last twenty or so years, quants are no longer a rarity in the world of finance, many tasks
that used to be done manually or over the phone are now done electronically. Today a cell phone has significantly more
processing power than the computers that the pioneers of quantitative trading used in the
1960’s. In addition, data is much easier to come by
today. Forbes Magazine estimates that 90% of the
data in the world today was generated in the last two years. It is a lot easier to do quantitative research
today, but equally there is a lot more competition. A lot of people think of quants as human computers,
but hopefully some of these examples show you the importance of idea generation and
creativity. If you want to discover good trading signals,
the only thing limiting you in this day and age is your own creativity. Today, just like throughout history, a quant
trader needs to come up with smart and interesting ideas that they can test using data. They need to be driven by curiosity to learn
new things, and they need to persevere and keep finding new trades that work, as over
time every new idea becomes old, outdated, and often stops working. If you found this interesting you will probably
enjoy the video I made on James Simons and should also check out some of the book suggestions
in the description below. If you watched the whole video and didn’t
like it, make sure to hit the dislike button and the unsubscribe button – how else will
I learn.... See you later, bye.
