This is Part II of my interview with Tobias Carlisle of the Acquirer’s funds, which just released its first ETF, the Acquirer’s ETF (ZIG) earlier this year. In Part I, we talked about some of the arguments against value investing and how they can be refuted. You can read that here.
In Part II, we talk more about the intricacies of building a value portfolio. As a reminder, this interview was taken from a phone call transcript, so please forgive any grammatical errors.
Jack: I wanted to ask you about sector constraints because you take a nonstandard
view among quant investors by allowing your portfolio to take overweight
positions in certain sectors when the fundamentals dictate it. Many quants tend
to view sector overweights as uncompensated risk, but you argue that there is a
benefit in moving a greater percentage of your portfolio into areas of the
market that are cheap. I wonder how you look at that balance between seeking
greater returns by concentrating in specific sectors vs. the tracking error it
can generate?
Toby: I think it’s a matter of taste how you construct a portfolio. I think
that if your objective is to not have a great deal of tracking error, which is
better for raising assets and better for short term performance, then you
should constrain by sector or industry. I think if your objective is long-term,
outperformance, then you need to be prepared to accept a lot of tracking error.
I’m not trying to build in tracking error. That’s a side effect of what I’m
trying to do, which is I’m simply trying to buy what I think are the cheapest
companies in the market at any time. And what tends to happen is industries get
cheap together because there are things that are external to the industry that
treat all of the companies inside it as being no longer worth what they were
worth a few years before. Financials are a good example of that. I think that
the 2007-2009 crash is vivid in many investor’s minds. And so financials at the
moment are the cheapest sector and they look to be unusually cheap relative to
history. I think that means that you should take a bigger bet on financials than
you would otherwise. And if you’re sector constrained, you’re not going to be
able to take full advantage of that, whereas I am. But if I do that by not
being sector constrained, it means that my performance in the short term is
going to be tied quite closely to the fortunes of that industry. But I hope
that over the longer term, over a three to five-year period, that will turn and
I’ll be there to capture more of it than anybody else. So in short I think it’s
a matter of taste for the construction of the portfolio. But mine is long-term
outperformance and I don’t mind tracking error in the short term.
Jack: One of the more interesting debates going on in value investing right
now is the one regarding the Price/Book ratio. It is an interesting case study
in the balance between long-term data and the application of logic and human
decision making in an investment process. On one hand, the academic research
supporting Price/Book goes back to 1926 and it has produced significant excess
return. On the other, in a world where intangible assets dominate tangible
ones, it is easy to argue that Price/Book is no longer applicable. I know you
don’t use Price/Book, but I was wondering what your thoughts are on how applicable
it is today and how to you look at that balance between long-term data and the
fact that things change that make the long-term data less applicable?
Toby: I gotta say, I don’t think that the
reason that price-to-book doesn’t work anymore is because there’s so many
intangible assets or there’s more intangible assets in the world. I think that
the reason that price-to-book underperforms is because the change in accounting
rules means that we haven’t been able to deal with companies like McDonald’s, which
have brought back so much stock that they now have negative equity and
price-to-book doesn’t properly classify that as being undervalued because it
doesn’t have a place for negative equity other than more debt than assets. So I
think we’ve got this kind of metaphysical problem and I think that to the
extent that price-to-book was ever a good way of measuring it, it is not as
good as it was in the past.
And I think a better way of measuring value is if you use flow measures, and
the criticism of flow measures, like income or cash flow or whatever it might
be, versus something like an asset measure like Price/Book is that they are
much more volatile. So book is pretty stable from quarter to quarter and year
to year. And so when the price in the market dips below book that might
indicate that you have a value stock. And when it’s at a premium to that, it
might indicate that you don’t. The flow measures I think will give a much
noisier answer, but I think that they are still iterating towards a more
correct answer. So I have a preference for flow measures and I think that where
a company is generating a lot of cash flow and it’s cheap relative to that
price, what that indicates is that it’s doing pretty well on a competitive
basis and it’s still probably a pretty good bet regardless of where it is
relative to its book value.
Jack: That makes sense. One of the interesting things I found when I
interviewed Jim O’Shaughnessy, when he had gone back for one of the editions of
his book and looked at Price/Book, he found that in the Great Depression it was
basically a proxy for bankruptcy risk. So stocks with low Price/Books had very
high bankruptcy risk. And so it didn’t work at all in that period either.
Toby: There’s also some research, the Loughran research on EV/EBITDA. He said
that the problem with price to book value is for one thing it tends to buy, and
you’ll notice if you look at low Price/Book companies, what you think is that you
are buying something that is cheap relative to the assets. What you’re really
buying is something with enormous amount of debt and a little residue of
assets. And then you’re buying it at a big discount to that little residue. And
so I think what happens is when that works, it works really well. That’s a
company that’s paid off its debt. And when they don’t, it’s because the debt
has overwhelmed the company. In that Loughran research they say that Price/Book
never really worked in any but the smallest 4%-6% of the market.
And then you’ve got other problems with measuring in that part of the market
because you’ve got wide bid/ask spreads. It’s not clear that you can trade at
any of those prices, so the back test may not match reality, whereas something
like EV/EBITDA, which they look at in that paper, they said that scales really
well. Even in the S&P 500, it’s more likely that the cheapest 10% will outperform
the most expensive 10%, so it scales through the whole of the universe. So
that’s why I prefer flow measures to book value measures for that reason.
Jack: That makes complete sense. Another interesting debate that many value practitioners have is the debate over which metric to use or whether to just use a bunch of them in a value composite. On one hand, if you think you have identified the best metric to use, using one can make the most sense, but on the other, if you think the best metric is difficult to identify in advance, then using a composite can be the most sensible approach. I know you have found an individual metric that you think is superior with the Acquirer’s Multiple, but others like Jim O’Shaughnessy have migrated to a composite based approach over time. I was wondering your thoughts are on using an individual value metric vs. a value composite?
Toby: Well, the way that I’m using the Acquirer’s Multiple, I use that for the industrial companies. So I don’t use that for Financials. For Financials, I use a much more traditional book value measure. And then I want them to be doing all the other things that I want other cheap stocks to be doing like buying back stock and generating free cash flow. Negative financing income basically: paying down debt or buying back stock or paying a dividend. And I think that indicates some sort of financial health measure.
So I use a slightly different beginning
way of measuring financials versus industrials. But then I think that the next
step is the same for both and you want to make sure that the cash flows do
match the reported income. So that’s a second measure that I’m looking at to
make sure that the cash flows match the accounting measures of income. And I
think Jim’s point is a good one and Jim’s experience is one that I have learned
from because the first edition of What Works on Wall Street had price to sales,
and I think that the argument there was compelling because it was undiluted by
management’s changes to the income statement, and the bottom line is a very
noisy, messy figure because it’s been manipulated. The top line is harder to
manipulate.
But it also misses the fact that some companies have a higher gross profit
margin than others. And companies with higher margins should be better targets
then companies that have a lower gross margin, even if they are equivalent on a
price to sales. So I think that it’s necessary to have other measures in there
that confirm what the main measure is saying. And so that’s what I have done. I
want to make sure the cash flows match. I want to make sure they’re doing
things that a good management of a cheap company would do, which is buying back
stock and paying down debt. And I think that when those things come together, I
think that basically what I’ve built is a composite, even though the composite
that I have built is the way I think of value investor would go through an evaluation.
Jack: So for non-financials, you are using the Acquirer’s Multiple as a
starting point and then you are going through and doing a variety of checks to
make sure the other data backs up that initial opinion?
Toby: Yes. Which goes to answer one of
your other questions. I’ve been running this model for about a decade. And over
the course of that decade, I’ve had this iterative process where it spits out a
bad name, which I don’t like for whatever reason, and then I go back to my
process. And when I said I don’t like the name, I don’t like the name because I
don’t think it’s undervalued, not because I care about how it’s gonna perform.
So what I have been trying to build is a model that acts like a value investor
rather than a model that acts like a Chicago School of Business quant investor.
So the model that I have built over 10 years is sort of going back and forth,
looking at the outcome and going back and adding another part of the process.
So now I’m comfortable that basically the model behaves the way that if I was discretionary,
I would still be doing the same thing.
Jack: Yeah. You know that’s interesting. That’s kind of the same way we tried to do it when we built Validea. We wanted to use quant models to analyze stocks as closely as we could to the way a human would look at it, but without the human emotion and biases.
Toby: I think that’s the best approach. It makes it easier for the manager to stick with the process as well because the outputs you’re getting match the way that you think about investing. The first time you run a quant model, the output is madness. There’s some stuff in there that you just wouldn’t buy. It takes a long time to get from that point to where you’re comfortable with the output of the model.
Jack: Thank you again for taking the time to talk to us today. If investors want to find out more about you and Acquirer’s Funds, where are the best places to go?
Toby: The Acquirers Fund (ZIG:NYSE) website is https://acquirersfund.com and my firm is https://acquirersfunds.com
My website https://acquirersmultiple.com has a free list of my favorite industrial stocks in the largest 1,000 stocks.
My email is tobias@acquirersfunds.com and I’m on Twitter @greenbackd.
