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.