Hello and welcome back to Patrick Boyle
on Finance - where we learn all about
quantitative finance. Today we'll be
discussing what correlation is and why
it matters to traders and investors.
We'll learn why correlation is so high
right now and why investors might care.
Keep watching till the end of the video
and I'll explain a trading strategy
where traders can profit from rises and
falls in stock market correlation. [Music] ok so
here's an interesting statistic for you
right now in June 2020 the S&P 500 index's three-month realized correlation has
settled around the 0.8
level its highest in about eight years
even though other gauges of market
stress such as the VIX index has
returned to normal levels. The divergence
between these indicators suggests that
investors continue to focus on just one
main driver - the global coronavirus
pandemic, making it harder for active
fund managers to beat their benchmarks.
Essentially it means that all stocks are
rising and falling together and it
doesn't really matter which ones you
pick to invest in. Correlation is a
statistical measure that indicates the
extent to which two or more variables
fluctuate together. It measures the
degree to which two phenomena are
related to one another.
A positive correlation indicates the
extent to which variables increase or
decrease in parallel. A negative
correlation indicates the extent to
which one variable increases as the
other decreases. Uncorrelated variables
have a covariance of zero. If two
variables are uncorrelated there's no
linear relationship between them.
Correlation is one of the most useful
statistics to a trader. This video by the
way is based on my newest book which is
called statistics for the trading floor
and it has been released a couple of
days ago on Amazon.com. I'll put a link
to it in the description of the video
below if you're interested. You can
also check out
my other books, one is on derivatives
The other one is on corporate finance.
The S&P 500
index is three-month realized
correlation began the year with a value
of around point to the gauge of how
closely the top stocks in the S&P 500
move in relation to one another spiked
to 0.85 in mid-march the peak of the
coronavirus sell off before steadying
down at around point 8 a maximum
possible correlation of Warren would
signify that all stocks are moving in
precise lockstep with each other during
periods of high correlation over the
last decade people have blamed the rise
of index funds or quantitative trading
algorithms that rely on historical
relationships and end up blindly
reinforcing those correlations investors
argued that something was broken in the
pricing and capital allocation functions
of the stock market and that all of the
different stocks simply rose and fell
together trading like the same stock
this time the argument is different
the current explanation for high
correlations in the stock market are
that in February there was an economy
then in March the economy shut down and
eventually there either will be an
economy or there won't if there is an
economy than different companies will
have different earnings but if there's
not then no companies will have any
earnings obviously every company will be
better off if there is an economy than
if there isn't your bet on whether there
will be an economy and when will almost
certainly determine whether you think
any particular company's stock is over
or under priced particular differences
in the outlook for specific companies
have become in this environment
essentially irrelevant this is of course
an oversimplification the S&P
correlation is 0.8 not 1.0 and certain
companies that are holding up well in
this environment are of course
performing better than the most hard-hit
sectors but overall this might be the
correct rough model if you're an active
fund manager who spent the last few
years argue
that index funds are fine in rising
markets with high correlations but in
more complicated times investors will
need the human intuition of active fund
management this outcome is far from
ideal you were hoping for the market to
go back to normal with some stocks going
up and others going down so that you
could pick the ones that go up while
index funds were forced to also hold the
ones that go down instead the bull
market broke in a way that ended up
increasing correlations in early 2020
all companies were overwhelmed by the
same novel risk they sold off together
and then they recovered together based
upon global government interventions one
thing that sometimes confuses traders
about correlation is the to us it's can
actually be very highly correlated and
stay that way yet one asset might yield
a + 50 percent return in a year while
the second asset could yield a minus 50
percent return over the same period the
reason for this confusion is that
traders often misunderstand what
correlation means and doesn't mean
correlation is best thought of as
representing the general directional
relationship between two assets if two
assets start out with a price of will
say $20 and every time the first asset
goes up by 1% the second asset goes up
by 5% and that ratio of 5 to 1 is
consistent the correlation will be 1
even though the second asset is
producing 5 times the return of the
first asset in addition to this there
can be very high correlations between
assets where one asset has a decay using
the same example as above let's say that
the first asset lost 20 cents of value
every day in addition to its
relationship with the second asset there
can still be an extremely high
correlation coefficient
despite the first asset falling to zero
while the second asset rises in price a
common real-world example of this effect
is levered ETFs I made a video about
them
blaah weeks ago the double shortly Vord
real estate ETF in 2008 was almost
perfectly negatively correlated with the
underlying real estate ETF yet ended up
losing almost all of its value if you
had just shorted the real estate ETF
rather than buying the double short ETF
you would have actually made money so
why does high correlation upset active
managers the idea is that when
correlations are high investors should
spend more time worrying about the
things that affect all stocks rather
than the things that affect the
differences between particular stocks
which is usually what active managers
focus on in particular fundamental
managers when correlations are low doing
research to find the best stocks will be
more valuable than when they are high
we're having an accurate opinion on how
quickly the economy will reopen is
significantly more useful so can traders
trade correlation yes they can actually
correlation trading is an advanced
trading strategy in which the investor
bets on the future realized correlation
amongst the member stocks of a given
stock market index the goal in trading
correlation is to predict when future
realized correlation amongst the stocks
of a particular index will be greater or
less than the implied correlation level
which can be backed out from the pricing
of options or other derivatives on the
index and the stocks that make up the
index due to the nature of
diversification the volatility of a
portfolio of securities is less than or
equal to the average volatility of the
individual securities that make up the
portfolio the lower the correlation is
between the individual securities the
greater the diversification benefits and
the lower the overall portfolio
volatility thus when expected
correlation is very low index volatility
will be a lot lower than the volatility
of the individual index members and when
expected correlation is very high index
volatility will be
to the average volatility of the
individual securities that make up the
index to sell correlation a trader can
either sell index options while buying a
basket of options on the individual
stocks that make up the index or sell a
variance swap on the index and buy
variants swaps on the individual
constituents or sell a correlation swap
to buy correlation a trader would just
do the opposite of that okay so hit the
like and subscribe buttons and if you
found this video useful check out my
book on Amazon which is linked to in the
description below bye
you
