How about if instead of using the stock market return maybe you don't think that's
the best measure to get a sense of the customer's buying power in all these
different countries like South Korea for
Pepsi, Portugal for Coach, using my example.
What if instead, you look at GDP growth?
So gross domestic product, that's reported on a quarterly basis.
So instead of looking at the stock market return each month, every quarter we could
look at the GDP growth of the different countries where a firm has sales.
And then rank firms by their weighted average of what's the GDP growth of
the regions where they make sales.
And it's simply weighted by how many sales do you have in that region?
So if you do a lot of sales in one region,
we're going to give a high weight to that region's GDP growth.
So then, we do this four times a year, as opposed to 12 times a year.
Ranking firms by their GDP growth over the past quarter,
the regions where they do sales.
Does that predict future returns?
Okay, The answer is yes, it still does.
So these coefficient estimates here are again focusing on the difference
between the top quintile and bottom quintile.
Top quintile firms, those are US firms that make
sales to regions that had the highest GDP growth over the last quarter.
The bottom group are those US firms that make sales to
regions that had the smallest GDP growth over the last quarter.
What do we see going forward on a monthly basis?
We're getting excess, we're getting risk adjusted returns,
0.6 to 0.9% on this measure here.
So these returns are lower than what we had
when we do the monthly adjustment with stock returns.
When we form portfolios on a monthly basis with stock returns.
Here we just formed portfolios four times per year
once we had the different GDP growth.
These returns still are almost 10 percentage points on an annual basis.
They're lower, but we're making less frequent adjustments to the portfolio, so
lower transactions cost.
Okay?
So kind of the bottom line, whether you rank
Firms by the stock market performance in the regions where they do sales or
the economic growth, the GDP growth in the regions you have sales.
You still have predictability, the market doesn't seem to fully incorporate
immediately good news in overseas markets and what it means for US firms.
So another example of the valuation of complicated firms,
kind of very similar in spirit.
Can one use a performance of easy-to-analyze firms to
predict the performance of their more complicated peers?
This is research by Lauren Cohen and Dong Lou.
They're basically documenting that you can predict the return of conglomerate firms.
Firms that have multiple divisions,
by paying attention to the performance of stand-alone firms.
Okay?
So let's look at their results here.
They're kind of using a similar methodology.
Let's look at a firm, and they have four divisions.
So, last month, let's see how the stand-alone
firms did in all four of those divisions.
So if your division and
let's make it simpler since I don't want to do four examples.
Let's just say they have two examples.
I have a railroad and I sell soft drinks okay.
So let last month, let's look at the return of the railroad industry,
let's look at the return of soft drink firms.
And let's see if these returns of your stand-alone competitors
predict your return next month.
Okay?
So under efficient markets, if good news happens to soft drinks, that should be
good news for me as well because part of my business is soft drinks.
There shouldn't be any predictability.
The fact soft drink industry did well last month
shouldn't predict me doing well this month.
I should've done well last month as well.
But, if it takes a little while for people to process the news, hey, good news for
soft drinks isn't only good news for Coke and Pepsi, it's good news for me as well.
It's maybe 40% of my business, but that's still good news for
me, if it takes a little while for people to figure that out,
maybe there can be some predictability, right.
Good news for Coke and Pepsi last month
predicts kind of good things happening to my stock price this month.
And this is what Cohen and Lou look at.
So let's look at the kind of results here.
So first, let's weight firms each month, let's categorized firms,
each month, by what was the performance of their stand-alone competitors.
So group one, these are firms that their stand-alone competitors did very well.
That predicts me doing very well next month.
If I look at a CapM model, I outperform my benchmark by 0.7%.
Next month in a 3-factor model, I outperformed by 0.5% next month,
with T statistics here in parentheses.
So statistically significant results.
So the fact my stand-alone competitors did well last month predicts that I do well,
in terms of higher stock returns, next month.
How about those groups, group 1,
where my stand-alone competitors did poorly last month?
Well that predicts me under-performing my benchmark next month.
In the CapM model I under-perform by 0.5%.
In the 3-factor model I outperform by 0.7%.
So the fact Coke and Pepsi did poorly last month,
that predicts my stock return doesn't go down last month.
Or doesn't fully go down last month, it goes down the next month because it takes
a little while for the infer, for people to process hey, bad news about soft drink.
Also affects Scott's firm because he's 40% in the soft drink industry.
It takes a little while for the market to process that.
And then the key is a final row, which is long short.
Where we're long, we're investing in the stocks
whose stand-alone competitors did well last month.
We're shorting the stocks where the stand-alone competitors did poor
last month.
We see here, these risk adjusted returns,
kind of similar to the does the water stop, does the,
kind of news stop at the water's edge, paper the geographic momentum.
Paper having 1.2% monthly returns to the strategy.
About 15% on an annual basis.
So quite impressive here.
So conglomerates,
firms with you know sales overseas both seem to be complicated firms where
the information isn't you know kind of fully efficiently processed in real time.
And then here is just looking at these returns to the strategy
whether you look at the blue line where we're equal weight firms or
you look at the red line where we're value weighting firms.
Firms.
Okay, and we're simply reporting the returns to this hedge fund strategy of
buy the stocks whose stand-alone competitors did well last month,
short the stocks where the stand-alone competitors do poorly.
You see there's this big 1% return to that strategy in the first month, and then, if
anything, the return to the strategy goes up a little bit, but it doesn't reverse.
It doesn't reverse over time here.
Final example of this drift strategy here,
using information that's publicly available to predict future returns.
