- [Instructor] When it comes to algorithmic trading,
there are a number of strategies that are used.
The simplest trading strategies
are based on a concept called mean reversion.
The idea is that basically a relationship that exists,
any time there's a deviation from that relationship,
eventually things will kind of revert to the mean.
One example of this will be in pairs trades.
So we might look at, for instance,
the Ford Motor Company versus General Motors.
Well, the factors that affect Ford
are likely also to affect GM.
If it's a good economy,
probably both stocks and companies are doing well.
If it's a bad economy, probably they're not doing well.
We might think about Lowe's and Home Depot,
Walgreens and CVS, Pepsi and Coke, things like that.
We're looking for stocks
where there seems to be a stable relationship over time.
So in this chart,
what we see is Ford
and their price change over time in yellow
and then GM in blue.
And what you see in general is that the two stocks
that track pretty closely to one another for a while.
But then during the later part of this sample period,
they start to really widen out,
where GM considerably outperforms Ford.
So we might take a pairs trade type approach
and bet on that relationship reasserting itself;
GM underperforming and Ford outperforming.
So that would be one example
of kind of a simple strategy in algorithmic trading.
Another approach should be trend following.
Now, trend following basically just means,
as the name implies,
trying to find or establish a trend
or pattern in the markets
and then capitalize on this.
Trend following tends to work very well
for algorithmic trading.
And in fact,
it's the basis for trading
at a lot of different hedge funds.
It relies solely on data.
We can adjust in or out of the trade
based on that data over time.
And it's a short-term type strategy to use.
The alternative and perhaps the opposite
in some ways to trend following
is what's called a four-factor model.
Now, the four factor model is an algorithm model
for which stocks should outperform
versus underperform over time.
It was developed by a pair of researchers
named Eugene Fama and Ken French.
Fama is at Chicago, French is at Dartmouth.
And basically,
the idea behind the model is to identify characteristics
of firms that do well on average over time.
More precisely, we're trying to identify risk factors.
And then those risks correlate to returns
that is taking on these specific risks,
leads to greater returns over time.
But it's important to understand the model
only works well with portfolios of companies.
I've said previously that in algorithmic trading,
you're not going to find a rule that gives you 100% accuracy
or anything of the sort.
If you find a rule that gives you 60% accuracy,
that's great.
So how do you overcome the possibility of 40%
that an individual stock it's going to violate that rule?
Well, the answer is we build portfolios of stocks.
If we take 100 stocks that all meet a certain criteria,
and we think that 60 of these 100 will outperform,
and 40 will underperform,
and we just don't know which 40 it is, well,
then we should be in good shape here over time.
So we use portfolios of companies
that have these characteristics
in order to avoid noise in individual stocks.
And the idea behind this is just that small cap companies
grow faster than large cap companies
just because it gets harder to grow
as you get bigger and bigger.
The second factor is the book-to-market value factor.
This is a proxy form firm valuation.
And basically,
the idea is that firms that have a higher
book to market ratio have higher returns.
Now, book value is an accounting metric.
So it is historical, but of course,
market value is real-time in nature.
So we can kind of capture this measure regardless of the fact
that we're using a historical factor that is book value
because we're incorporating current data into the model.
Now, book-to-market,
it's not 100% clear why this is a useful risk factor,
but it appears to be an indicator for financial distress.
That is companies that are kind of
in a turnaround type situation.
And then investing in those generates greater risk,
but also long-term returns
if the turnaround is successful in particular.
So these distressed firms
bounce back strongly in a lot of cases.
Now, our third factor is beta.
This measures the volatility of the stock.
We'd previously looked an example of Microsoft.
And we looked
and saw how Microsoft's beta among other factors,
among other numbers or figures changed on a daily basis.
Well, beta measures the volatility of the stock.
And basically,
what that means is if we have a beta of one,
there's a correlation of 1.0 between the stock market
and the firm in question.
So if Microsoft had a beta of one,
what that would mean is that on average over time
when the stock market went up 1%,
Microsoft would also go up 1%.
When the stock market went down 1%,
Microsoft would also go down 1%.
That's the idea.
So if we had a beta of two,
then a market rise of 1% means that that stock
should on average over time rise 2%.
And of course, fall 2% if the market falls 2%.
So beta, as we might expect then,
is a measure of volatility and hence risk.
And it's a good factor
for predicting long-term returns in a stock.
Then fourth and finally, we have momentum.
Now, technically,
momentum wasn't one of the original three factors
proposed by Fama and French,
but it quickly got added
by other researchers kind of building on this work.
And basically,
the idea is that momentum is related to this concept
in sports that we'd call kind of a hot hand, right?
In other words,
companies that have done well in the recent past
continue to do well in the future.
That's the idea.
And so momentum could be measured by earnings, stock price,
perhaps by revenues, a variety of different metrics.
And if we can get these numbers on a real-time basis,
perhaps pulling in as an example
data from satellite imagery, or social media,
or things like that as a proxy for sales, wonderful.
And if we can't, well, we can certainly use historical data.
But momentum is basically just the idea
that companies that have been doing well,
that have been outperforming
will probably continue to perform well.
And companies that are struggling a little bit,
maybe they're products
aren't necessarily appreciated by the market
or they're out of step
with what the customer base is looking for,
it's probably going to take them a while to readjust.
So at this point,
you should have a good handle
on some of the basic strategies
that can be used for algorithmic trading.