MALE SPEAKER: Welcome, everyone.
We have a very special guest here today
with us, Toby Carlisle.
Welcome to our Talk at Google, at Author series.
He's going to be talking about his new book, "Deep Value: Why
Activist Investors and Other Contrarians
Battle for the Control of Losing Corporations".
Toby has a unique perspective on value investing,
much like Google's.
His approach to value investing is
data driven, just like Google's approach to engineering.
In his book he uses statistical analysis and in-depth research
to give evidence for a simple, yet counter-intuitive idea.
Losing stocks with failing businesses
and uncertain futures can sometimes
offer unusually attractive investment potential.
His work in this area is very much like Ben Graham's
classic work in his 1934 "Security Analysis" book.
The funny thing about classics is everyone's heard about them,
very few people have actually read the classics.
Toby is one of those rare, few individuals-- investors,
I would say-- who has been living and breathing
Ben Graham's deep value investing philosophy.
So please help me welcome Toby Carlisle.
[APPLAUSE]
TOBIAS CARLISLE: Thank you.
Hi folks, thank you very much for having me here today.
I'm very much looking forward to speaking to Google.
Just a little bit about me, my name's Tobias Carlisle,
I'm the managing director of Eyquem Investment Management,
and I manage the Fund and some separately managed accounts.
My most recent book is "Deep Value",
and before that I published "Quantitative Value",
co-authored with a gentleman who did his qualitative research
through Booth.
I blog at Greenbackd and I'm just
launching a new site called the Acquirer's Multiple
that is yet to go live.
The talk today proceeds in four parts.
The first part we're going to discuss
is the philosophy of deep value.
What is is, its genesis.
Then we're going to look at some of the behavioral reasons
that stocks become undervalued in Contrarians at the Gate.
Third, we're going to examine some ways
that we can avoid making those behavioral errors that
create undervalued stocks.
And finally, we're going to examine the simple metric
for generating outstanding returns in the market.
In 1927, a 33-year-old Benjamin Graham
started teaching a night class at Columbia
that he called Security Analysis.
He offered practical counsel for the students who
took the class, but the thrust of what he taught
was that a security had an intrinsic value,
and it was distinct from its market price, which
you could observe on the stock market in any given day.
And he argued that when there was a sufficient mis-pricing,
either the price was at a discount
to the intrinsic value, or, in some instances
where the price was at a premium to the intrinsic value,
you could trade long or short and rely on the fact
that there would be some mean reversion
to bring the price back to intrinsic value.
In 1929, the stock market had its worst crash,
it continues to be the worst crash to date.
From its peak it to its trough it fell almost 90%.
In 1932, fully three years after the bottom of that crash,
the stock market still hadn't recovered.
Graham authored a series of articles for Forbes Magazine
where he pointed out some research that he had undertaken
that said that of the 600 issues on the stock market
at that time, 200 of them traded for less than their liquidation
value.
So they traded for less than what the shareholders
in those companies could get out of them
if they wanted to wind them up.
And then a sizable portion of those traded at a discount
to their net cash backing, which is
after paying out all of the liabilities of the company,
there would still be cash lift in a bank account
and you could buy the entire company for less
than that amount.
And he said-- he likened it to corporate gold dollars being
traded for $0.50 or less with strings attached.
That sort of enduring iconic image continues to be the way
that we think about value investing,
that it is an attempt to buy a $0.50 dollar.
Some years later, John Burr Williams
added some additional color to the way
that we go about valuing a company,
and that is to look at all of the cash inflows and outflows,
discount them back to the present day,
and that gives you an intrinsic value.
So as you hunt in the market for these particular stocks,
you can find those at the greatest
discount to intrinsic value will tend to underperform
and those at the greatest premium to intrinsic value
will tend to-- sorry, will tend to underperform.
So this chart shows, if we rank every stock in the universe--
and this is a global universe, so 22,000 positions-- if we
give them all a ranking, price to cash flow, price to book,
price to earnings, we take the average of those
and then we rank them into five groups.
The glamour quintile, which is the most expensive,
tends to underperform on average,
and the value quintile, which is the cheapest,
tends to outperform.
So when we're seeking to invest in these companies,
one thing that we can observe is that price
tends to fluctuate more than intrinsic value, which
only moves much more slowly.
And so when you find these occasional moments when there's
a large discount or a large premium,
you can invest to capture that discount.
The question that I'm often asked
is what is it that causes the discounted price
to return to its intrinsic value?
And it's a question that Benjamin Graham got
when he was appearing before a Senate committee in 1955,
and the chairman said, how do you close that gap?
Is it by advertising, or what happens?
And Graham gave one of his great answers
where he said it's a mystery, and it's
as much of a mystery to him as it is to everybody else.
But we understand it to mean mean reversion,
and so what this talk is about is
what are the actual mechanics of mean reversion.
What are the things that make mean reversion occur.
This is actually Graham's list.
He talks about, in one, that's just
general improvement in the industry.
And this is a very common occurrence
that the industry is in a trough,
the business is in a trough, it's
got a period of bad luck, perhaps some bad management,
and either the incumbent managers
turn the company around or external managers
are brought in.
Less commonly, a sale or a merger,
or some sort of liquidation.
Some time later, behavioral researchers DeBondt and Thaler
examined securities on the basis of a variety
of different metrics, but they're looking, particularly
in this case, for undervaluation and overvaluation measured
by price to book value.
So they found that by selecting the decile, which
is 1/10 of stocks that were most undervalued,
and the decile of stocks that were most overvalued,
and then examining the trend in earnings per share,
you find this unusual phenomenon where the earnings for the most
undervalued stocks have been falling for the years
previous to the portfolio's being formed,
and for the most overvalued stocks,
they've been increasing.
And that's the reason that one is overvalued
and the other is undervalued.
But then this very unusual thing happens
once the portfolios are formed.
The overvalued stocks still continue
to grow but they slow a great deal,
and the earnings per share of the undervalued stocks
begin to improve at this stage.
And four years hence, on average,
the undervalued stocks which have been selected simply
based on their undervaluation have actually earned more
and are earning more than the overvalued stocks which
had the highest rate of growth.
It's a phenomenon that we see over and over again
in the research.
So this is another interesting study
subsequent to the Debondt and Thaler
research by Lakonishok, Shleifer, and Vishny.
They said, well, if it is in fact the case
that there is this main reversion in the fundamentals
of the business, if there's mean reversion
in the earnings, if the undervalued stocks do start
creating earnings growth and the overvalued stocks' earnings
slow, then what if we went and selected portfolios
specifically on-- we could find an undervalued
portfolio with very slow growth and an overvalued portfolio
with very high growth, and an undervalued portfolio with very
high growth, which would be the better portfolio?
So they sorted the universe into three portfolios
on the basis of valuation, from most overvalued
to the median average, and to the most undervalued.
And then they divided each of those portfolios
into the highest rate of growth, the mid rate of growth,
and the slowest rate of growth.
So there were nine portfolios formed.
I've selected three of them here to show you the phenomenon.
So the glamour portfolio has the highest rate of growth,
and you can see an earnings, cash flow, book value,
operating earnings, all of those are growing higher
than the other two value portfolios.
But you can also see that you have
to pay a higher multiple to acquire these stocks, so
at 19.6 times 10.8 times cash flow, they're expensive stocks.
On the other end of the scale, these
are two of the three value portfolios.
The high growth value portfolio is still
growing at quite a high rate of growth,
but it's available for a much cheaper multiple,
and the contrarian value portfolio,
which is the very low growth portfolio,
is growing at a low rate and available on a low multiple.
So as you would expect, the value portfolios
outperformed the glamour portfolios.
But this is the really interesting thing.
The low growth undervalued portfolio
outperforms the high growth undervalued portfolio,
and this is something really unusual,
and I think it's something that is a little bit
counter-intuitive because we sort of like
to think that high growth undervalued stocks
are bargains, gems in the rough.
And it turns out that mean reversion
acts on those portfolios as well to pull the high growth back
and to improve the low growth.
So this is just that previous slide with the glamour
portfolio removed, just so we can see specifically.
The thing that I want to draw your attention to here
is that the contrarian value portfolio,
in many of these categories, is actually a little bit more
expensive than the high growth portfolio.
The reason possibly is that earnings are little
bit more anemic, cash flow is a little bit more anemic.
So you can see on the book value basis,
the high growth value portfolio is slightly more expensive.
But it's very definitely growing much faster than the contrarian
value portfolio.
So the real driver of returns here
was the undervaluation and not the rate of growth.
And then there was some mean reversion as well
which pushed down the high rate of growth
and made the high growth value less
attractive over a period of time compared
to the contrarian value.
This was an earlier examination conducted by Michelle Clayman.
There was a book written by Tom Peters that came out
in the early '80s called "In Search of Excellence",
and Peter said that he'd identified the characteristics
that created excellent companies.
He looked at a variety of these quantitative financial type
elements.
The book has been described as the book of the century,
possibly by Tom Peters himself.
I've never read it, but I've read the research,
and the research is compelling.
These are his criteria for an excellent company,
so they have very high rates of asset growth, very high rates
of equity growth, they're expensive,
and they have excellent returns on capital, assets, and equity.
So Clayman said, well, let's create the opposite portfolio.
Let's call them unexcellent companies,
and we're going to find those with the worst asset
growth, equity growth, low valuation, anemic returns
on capital, equity, and assets, and then
we're going to track their performance
over a period of time.
The thing that I'll draw your attention to
is just the valuation alone.
You can see here, this is some research
that appears in the book.
It was conducted by Barry Bannister at Stifel Financial.
The unexcellent portfolios have vastly outpaced
the excellent portfolios.
For an early period of time, and it's
difficult to see on this chart, the excellent portfolios
did actually outperform the market.
But not since you get a better return investing in the S&P 500
than you do investing in these very high quality, high growth
companies, these excellent companies.
And you get a much better return investing
in the ugliest of the ugly.
The question that naturally people
ask when they see that data is well,
is there some advantage to investing
in the excellent portfolios because they
offer some sort of risk protection?
Is it a downside protection phenomenon,
is it that when the world goes into a recession,
you want to be in these things that
generate higher rates of growth?
And the answer is no.
Bannister looked at periods where the global growth
was below average and above average.
The shaded area is where global economic growth
is below average, and the lighter areas
are where global and economic growth exceeds the average.
And you can see quite clearly, there's
no real method to when unexcellent
outperforms or excellent outperforms.
It does outperform occasionally, but it doesn't necessarily
need a bad state of the world to do so.
I always like to use this motif to describe the nature of what
occurs in these undervalued companies.
Most companies are going to go through this cycle.
There are a very small number of companies
that can avoid this cycle, but a very small number of them.
This is from a woodcut from Albrecht Durer in 1494
from a book called "The Ship of Fools",
which is about some fools on a ship to a fool's paradise.
And they go through, they have a number of follies.
One of them is hubris about where they are and disregarding
the role of luck in their own lives, good and bad.
So it's the wheel of fortune, that's Fortuna's hand puppeting
the wheel in the top left hand corner.
The ass at the top is regno, "I reign".
And you can see that he's at the very pinnacle,
and he's about to descend on the other side,
but instead he reaches out for the sun.
And this is what, in the academic financial literature,
they describe as naive extrapolation.
You imagine that your current state
will continue on when, in actual fact,
mean reversion is a much more likely outcome.
So the next, regnavi, "I reigned".
He's slowly turning back into a man
as he descends down the other side.
And this is, perhaps, where deep value investors
want to stake their claim.
Sum sine regno, "I am without a kingdom",
he's not even visible on the wood cut.
And then back up the other side, regnabo, "I will reign",
maybe that's another good time to invest, too.
If what we're looking for is fundamental weakness, declining
earnings, and undervaluation, the thing
that makes them difficult to buy for other investors
makes it equally difficult for us to buy.
So how can we avoid making the behavioral errors
that other investors are making?
How can we not be the naive extrapolation investor,
and become a contrarian value investor that
captures that upside performance from buying companies
with ugly fundamentals?
There's been a great deal of research
into this area of the role of statistical prediction models
and experts.
It's a counter-intuitive area of study for this reason.
So a professor went and studied 1,000 admissions
to hospital of depression or psychosis,
and apparently, on initial presentation,
they can appear very similar.
Then there's a subsequent diagnosis several weeks
or months later where they're able to determine properly
whether the patient who presented
was in fact psychotic or depressed.
So he created, by examining the admission
records and the subsequent diagnoses,
he created a simple questionnaire
that could be administered by psychologists when
these people presented to the hospital
and they could make a determination.
Without the benefit of the model,
the worst psychologists got it right about 55% of the time,
on average about 62%, and the very best psychologists
got it right 67% of the time.
The simple model in back test got it right 70% of the time.
The professor distributed the questionnaire
to a variety of different psychologists in the hospital.
There were two basically inexperienced
who were students, and then there
were clinical psychologists who were actually
treating patients, and they found
that without the benefit of the model,
the inexperienced students got it
right about 59% of the time, the experienced clinical
psychologists got it right about 64% of the time.
The inexperienced psychologist, with the benefit
of the model but the ability to override the model's output got
it right about 2/3 of the time, and the experience
psychologists, with the benefit of the model,
got it right 75% of the time.
But this is the really incredible thing.
The simple model by itself outperformed all of them.
The reason is that we make errors
when we attempt to apply a model and we don't follow the model's
output, and that leads us to underperform
what the simple model does by itself.
The reason that people-- when people observe this research,
they often say, if you have a case that's
so different from the base, right,
from the statistical research that you've conducted,
doesn't it make sense then to be able to override the model?
And the example they always give,
you have some sort of algorithm that predicts whether John will
go to the theater on Friday, and now you
know that he has a broken leg, shouldn't you
be able to factor that into your model
to determine whether he will, in fact, go to the theater
this Friday.
And the answer is no.
And the reason is that we tend to find more broken legs
than there really are.
And particularly in this type of deep value investing,
all of the stocks have what appear to be broken legs.
You might be familiar with "The Little Book
That Beats the Market", it's a book by Joel Greenblatt.
He took Warren Buffett's exhortation
to buy wonderful companies at fair prices,
and he translated that into a quantitative model
that we'll examine in detail in a moment.
But for the moment, you just need
to know that it meant high quality, however
that's defined, and good value.
He had an experiment in his own firm
where he handed out lists of the stocks selected by the formula,
and he allowed people to, in their own separately managed
accounts, to either choose the stocks that they wanted,
or to have him apply the formula automatically.
He found that the automatically applied formula did, in fact,
outperform the market over two years,
and by quite a substantial margin.
He also found that when people were allowed
to manage the portfolio themselves,
they tended to underperform, and the reason
is that they cherry-picked out the very best stocks,
and they were the stocks that were the ugliest.
You might say well, they were not experts,
they were people who were relying on his expertise.
But then Greenblatt says, we attempted to do the same thing,
and he found he had the same outcome.
He underperformed his own model.
So that brings us to the golden rule of statistical prediction
rules, and that is that simple models outperform experts.
That simple models continue to outperform experts even
when they have the benefit of the model.
When we're thinking about designing
a quantitative system, there are several things
we need to be aware of.
One of them is that the rules must be simple
and they must be concrete.
They must be simple so that they're able to be followed,
and concrete so that they're able to be understood.
This is a picture of a pipe, this is not, in fact, a pipe.
This is a picture of a picture of a pipe.
And when we're valuing companies, in many ways,
this is sort of what we're doing.
We're using some proxy.
And there are a variety of different models or proxies
that we can select.
We can choose the liquidation value,
we can look at the franchise value, the growth value,
earnings power value, the acquirers multiple.
We can use any number of these simple multiples,
but we're not really getting the truth of the company.
And so it's important that we recognize, first,
that there are limitations to the model.
Recognizing that the model is somewhat imperfect,
we can recognize then that we do have
some tool for making a decision.
So the 80/20 rule does apply to information in investing, too.
You get 80% of the way there with a simple model,
and the urge of most investors is
to continue to find that final bit of information that
will remove the uncertainty that they have.
But often by the time that occurs,
the uncertainty has been removed for everybody
else as well, and so those low prices
that you were attracted to are gone.
The simplest rule, I think, and one of the most effective,
is the net current asset value rule.
Graham wrote about this in 1934.
He described it as a rough measure of liquidating value.
So you can see, you look simply at the balance sheet,
you ignore the earnings, the cash flow statement--
it didn't exist at the time that Graham put this together,
but you can ignore it for the purpose of this analysis--
and then you treat only the current assets as having value.
So cash is worth cash, receivables, there's
some discount applied to them, inventories
would vary depending on whether it's food that can spoil,
high fashion that will be less valuable in 12 months time,
or something that's going to continue to be valuable well
into the future, and then you can look at the fixed assets
and determine whether they have a value.
The research shows quite comprehensively
that net current asset value massively outperforms.
And it's an extraordinary thing that is such a simple analysis.
So the market in these instances is always
a small capitalization, micro-capitalization
equivalent to what we're looking at for the net current asset
value stocks.
You can see in the US, that's a study from 1970 to 1983,
the net current asset value portfolio
did 29% versus 11.5% for the market.
This is a study that I conducted in the US,
'83 to 2010, just continuing on the second study.
So it was a second 27 years and a similar sort
of outperformance, maybe more so.
Which is surprising, because you would
think that with all of the access to information,
a lot of these positions would have been arbitraged away,
but they haven't been.
They continue to exist in the UK and they exist globally
as well.
But there are some unusual things
that you find when you conduct this sort of research.
The first one is that the individual net current asset
value stock is more likely to go to 0
than the rest of the market, and the rate is about 6%
for a net current asset value stock
versus 2.5% for the average stock in the market.
But as a portfolio, there are fewer
down years than the market.
And this is perhaps the most controversial finding, the most
counterintuitive, and that's that loss-making net-nets
actually outperform net-nets that are profitable.
Within the profitable net-nets, those that don't pay a dividend
outperform the ones that do pay a dividend.
So your instinct might be to find a net-net
that pays a dividend with positive earnings,
and that would lead you to underperform what
you can do just by net-nets alone.
Many of you will know that Warren Buffett started out
as a student of Benjamin Graham's, then he
worked with him in his Graham-Newman Corporation,
and then he went out and he set up his own partnership.
In the early days of his partnership,
he was very much a Graham-type investor,
looking at liquidation value.
One of the early positions that he put into the fund
was Sanborn Map.
You had a portfolio of securities
and it was trading at $0.65 on the dollar of that portfolio
of securities, with no value given at all
to the business, which was making about $100,000 a year.
He got on the board, he got control,
he liquidated the security portfolio, paid it out
in a tax-effective manner, and so he got a 50% return just
on the portfolio, and then the business remained.
And the business continues to exist
to this day as a geographic information system
business in the US.
He had this evolution after meeting Charlie Munger,
and in 1963, he found American Express mired in a scandal.
American Express is a financial company,
and it had provided some warehouse receipts
to Anthony "Tino" De Angeles, who
was a commodities broker and trader.
He had figured out that he could bring soybean oil to the port,
show the inspectors that his tanks contained soybean oil,
and then through pipes and valves
he could fill up with sea water what had been soybean oil.
Then they would check a new tank and it
would contain the old soybean oil.
At one stage, he controlled almost 10 times more soybean
oil than there was in existence.
And that was the secret to his low, low prices.
Eventually he went bankrupt.
American Express had said that the warehouses
did in fact contain the salad oil.
His broker went bankrupt when the people who
had lent against those warehouse receipts
came looking for some deep pockets,
they found American Express.
And American Express owed something
in the order of $175 million, which
was 10 times its average annual earnings of the last few years.
So there was a real risk that American Express
would go bankrupt at that stage.
Buffet put 40% of his portfolio into the stock, it recovered.
He bought $13 million worth of that stock.
That position today, if he had held it--
which he didn't all the way through because he's changed
investment entities-- that position today
is worth something in the order of $14 billion,
which is an enormous return.
He learned from that that it wasn't
necessary for these things to have a liquidating value.
He could, in fact, buy them on the basis of a franchise.
He made sure that people were still
continuing to use American Express cards,
and so it taught him that there was
a different method of investing.
In 1972, he found See's Candies.
It was earning something like $2 million on $8 million
of invested capital.
They paid $27 million dollars for it.
He assessed the value in the order of $45 million,
so he got a fairly substantial discount.
And he said at the time when someone asked him about it,
are you still a Grahamite-type investor.
He said I'm 85% Graham, 15% Phil Fisher, who recommended
the scuttlebutt method of investing,
which is you go and find as much information
as you can about the quality of the business
and its ability to grow.
So See's Candies, between 1972 to 2011, returned $1.35 billion
to Berkshire Hathaway, which they've continued to invest.
And it's required only something like $70 million
reinvested in the business to generate those earnings.
So that's what's known as a franchise.
The lesson that he took from investing in See's Candies
was that you're much better off with these businesses that
are able to grow over a long period of time.
He said, though the cigar butt might have a single puff left
in it, and that puff is pure profit, after you've
smoke that puff, there's nothing left.
So he stopped being a cigar butt investor
and became an investor in wonderful companies
at fair prices.
Looks at Buffett's investment methodology
and says that's been very lucrative
for a very long period of time.
Assuming we don't have Buffet's great mind,
are we just able to create a quantitative version
of the methodology that Buffet describes in his letters,
and he wrote a book about that process.
So he decided that good quality, as defined by Buffett,
means a high return on invested capital.
So invested capital is the money that you
need invested in the business to run
the business, the assets of the business that are actually
used to produce income.
The higher the return on invested capital,
the better the business, the faster it's able to grow.
And for valuation, he uses an endings yield,
earnings before interest and taxes,
because it's agnostic to the capital structure
where interest payments on debt affect the tax that you pay.
If you back out interest and tax then
you get this idea of the operating earnings
that are coming into the business.
We tested this qualitative value,
and we found that the magic formula does,
in fact, beat the market, a comparable market of stocks.
And quite comprehensively, it's beaten it by 3.5% each year
from 1974 to 2011.
What is really shocking is that the earnings yield alone,
what I describe as the acquirer's multiple,
beat the magic formula itself, and the quality measure
actually underperformed the market.
The quality measure actually led the magic formula
to underperform the earnings yield alone.
And it's not just a performance, it's
not just a raw return story.
It's, in fact, a risk-adjusted return story as well.
The earnings yield alone generates a better Sharpe ratio
and a better Sortino ratio, which is basically
the amount of growth relative to the amount of variability
in the returns.
So you get better returns and you
get better risk adjusted returns just using the earnings yield.
And the reason is that there is this mean reversion
in return on invested capital.
Michael Mauboussin has tested this.
He's examined a number of the thousand largest listed
stocks in the US, ranked them in order of return
on invested capital, and then divided them into five groups.
So the very highest, and then he's
ranked them into very highest and the very lowest.
And then he's examined those same companies 10 years hence.
And what he finds is that the highest return on invested
capital mean reverts towards the mean return,
and the lowest return on invested capital
also slightly improves.
So I think if the highest return on invested capital
as being regno, at the very peak of the wheel,
and sum sine regno at the very bottom of the wheel.
What is the acquirer's multiple.
Well, it's the enterprise multiple.
The reason it's called the acquirer's multiple
is it's the metric used by leveraged buyout firms,
private equity firms, activists, to look through
into the hidden value of the business
and, in terms of the mechanics of it,
it's market capitalization plus debt.
Because the business has to fund the debt,
you're able to use the cash to pay off the debt.
You're also liable for the preferred stock,
you're also liable for any minority interests,
and underfunded pensions, off balance sheet liabilities.
So it's the real cost to buy the business.
And then you get access to EBITDA or EBIT,
which is the cash flow coming in.
It turns out that it doesn't really
make much difference which you choose.
But when we tested them using data from 1964 to 2011--
so we tested a variety of different possible metrics.
Earnings yield, which is the inverse of the price earnings
metric, just to highlight the two.
The acquirer's multiple using EBIT,
the acquirer's multiple using EBITDA.
Free cash flow on enterprise value, gross profits
yield, so that's just revenues minus cost
of goods sold to give you the third line on the income
statement, and book to market, which
is the inverse of price to books so that they're
arranged in the same way.
We found that the acquirer's multiple outperformed.
And again, not just on a raw return basis.
It outperforms on a risk-adjusted return basis,
too, and comprehensively.
The four things I want to take away from today
are that the very deepest undervalued stocks
outperform the highest quality and the highest growth,
even in the undervalued portfolio.
So undervalued portfolios divided into high growth
and low growth, the low growth undervalued portfolios
will outperform.
So it's better to be assuming that there's
going to be some mean reversion, positive and negative,
rather than to naively extrapolate out
the growth in earnings.
Simple models, applying these ideas
will always outperform expert discretion.
So at the beginning of your process,
you decide what is important in the assessment of value,
and then you rigorously apply that without fear of failure.
And the acquirer's multiple is the best multiple.
If you're looking for a very simple application, a very
simple rule, this is a very good one.
This is a plug for the book, so I
go into these studies in a great deal more depth in the book.
Each one of them-- there are several different versions
of this study, so you can see as we go through the entire book.
The site Acquirer's Multiple has a place
to capture your email and your details
if you'd like to learn more when it's up and going.
It will basically provide a free screen
of acquirer's multiple companies,
and some commentary and some research as it goes along.
So if you have any questions, I'd be happy to hear them.
AUDIENCE: This question is about the mean reversion.
Amongst the universe of companies,
do you think there are some particular companies
or industries that are more flexible towards mean reversion
and others that are not, or did you think
it's the entire universe that this mean reverts over there.
TOBIAS CARLISLE: There are certainly
some companies do demonstrate persistence in their ability
to maintain a high return on invested capital.
And the question is that if you look at a large enough universe
of stocks, would you just expect that there
would be no persistence at all, would you find some just
by random chance?
So it's not entirely clear whether there's
a reason for their persistence, or whether it's just
the luck of looking at a 10 year period.
[? Maberson ?] has looked at that specifically,
and he found that pharmaceuticals
and biotechnology and another group
did demonstrate some persistence,
so they kept the high returns on invested capital.
But he wasn't able to determine the factors that-- so
prospectively, if you look at a data set without knowing
the outcome, can you determine which ones are going to persist
and which ones are going to mean revert,
and he hasn't been able to do that yet.
AUDIENCE: You mentioned the EBIT divided by EV
is actually better than the magical formula.
Is Joel Greenblatt aware of that?
I cannot imagine that he doesn't know that, right?
It doesn't make a lot of sense to me.
TOBIAS CARLISLE: He's almost certainly aware of that.
AUDIENCE: But you think he knows that,
but he still put [? RYC ?] into the book.
TOBIAS CARLISLE: Well, the magic formula does outperform.
The magic formula does beat the market.
AUDIENCE: Yeah, but I'm sure that he also
has done the study on the separate metrics, right?
Do you think he hasn't done the--
TOBIAS CARLISLE: Well, I'm sure that he has.
AUDIENCE: But, OK, OK.
I guess--
TOBIAS CARLISLE: So why do it that way?
Well, you know there are--
AUDIENCE: You understand my question, yeah.
TOBIAS CARLISLE: I do.
There are elements to the business.
It's a marketing business as much as it's a return business.
AUDIENCE: But I have the impression
that Joel Greenblatt doesn't really--
I mean, he has enough money and he doesn't really
care about making more money.
I mean, I could be wrong.
TOBIAS CARLISLE: In different states of the world,
the magic formula will outperform the earnings yield.
So when the markets are racing up, in a bull market,
it's about even.
But there are some periods of time--
the late 1990s for example, which
was an unusual period in the markets-- but the magic formula
did outperform the pure earnings yield.
But the earnings yield has outperformed the magic formula
over the last 15 years.
So it may be a job security type Idea
that if you apply the magic formula rather
than the earnings yield, you'll have
fewer years of underperformance.
When I test that, that's not what I find.
Basically, the earnings yield outperforms the magic formula
pretty consistently, and pretty consistently
over five and 10 year rolling periods.
I think it's the better metric for the reason
that I've outlined here, that return on invested capital,
mean reversion, is a very real phenomenon.
And it's an easily explained phenomenon.
When you find companies that are very profitable,
it invites competitors to go into those industries.
In industries that low return, people leave the industry.
That happens all the time.
It's happening right now in the oil and gas industry
with low rates of return, because the oil price is
so low, they'll stop drilling holes.
So that has a flow on effect.
AUDIENCE: Joel Greenblatt talks about the fact
that a lot of these economical companies are too cheap
and don't have enough liquidity for the fund managers to own,
and also that a lot of the fund managers
just want saleable product.
That's the popular names, and so on.
So could you comment on the liquidity issue
and moving in and out of some of these smaller issues?
TOBIAS CARLISLE: The book, "The Little Book
That Beats the Market", shows a return
for the magic formula that's much higher than you
can achieve if you adjust for liquidity and for size,
but you can still get outperformance
applying it in a very large capitalization universe.
And market capitalization weighted,
which means you've sized the positions in the portfolio
relative to the market capitalization of the company
that you're buying rather than equally weighting them,
so that means that you're putting more money
into the bigger companies.
That is testing for precisely that problem.
So the results that I was showing in here
were actually a market capitalization weighted.
So you get slightly better results, again,
if you equal weight, for the simple reason
that you're putting more money into smaller positions.
So market capitalization weighting
sort of adjusts for that.
The problem with something like the net current asset value
is that it's really not an investable strategy
for anybody other than an individual.
They're just not around often enough,
and they're not big enough or liquid enough to invest in.
If you have a million dollars of investable capital,
it's probably too small for you.
But the magic formula and the acquirer's multiple scale
beautifully.
You can take the acquirer's multiple in an S&P 500
universe, select the 5% or the 10%,
so the 25 or 50 stocks in that universe, roll it once a year,
and you'll find pretty consistently
that you get quite a lot of outperformance doing that.
As a broad problem, liquidity and size
are issues that reduce returns, but these metrics still
do work in large universes.
You get the incredible outperformance
if you're able to invest in the little stuff, which
small managers and individuals can.
Does that answer the question?
AUDIENCE: Yeah, that's a very good answer.
AUDIENCE: So I think your last statements have answered
a lot of my questions I was going to ask.
But for the magic formula backtest,
I would like to know more details.
So one is, you said the rebalance is once per year.
TOBIAS CARLISLE: Yes.
AUDIENCE: OK.
So how does the backtest deal with the survival buyers?
TOBIAS CARLISLE: That's a good question.
So the database that we use, Compustat,
keeps companies that have failed in the data.
So if it failed in 1975, the backtester would have bought it
and could have bought it in 1974,
because the data remains in the database.
So we do a number of things.
In quantitative value, we outline in some detail
the process we go through to backtest.
Basically we do a number of things.
We lag the data, so one of the problems you have
is that you have this look-ahead bias, which
is the possibility for trading on information
that you don't already have.
And in addition to that, you have this January effect,
which is quite pronounced.
So there's some tax loss selling at the end of the year,
and then if you invest assuming that you
can buy your entire position on the very first day of January,
you capture quite a bump in performance.
So what we did is we rebalance the portfolios on June 30,
and we used data from the preceding December.
So we use the K data and we rebalanced in June.
So we're avoiding that January effect,
and we're avoiding the look-ahead bias,
and then we use a very good database.
AUDIENCE: So the basket is about 30 stocks, in the basket?
TOBIAS CARLISLE: It varies depending
on the size of your universe.
The universe, and depending on the size of how much you're
investing and how much time you want to spend doing it,
the fewest stocks that you could possibly do it with
might be 20.
And the most stocks, you really don't get much benefit
beyond about 30.
So somewhere between 20 and 30 is
the appropriate size for portfolio, equally weighted.
That also means that you can buy them on a quarterly basis.
So you might buy-- if you're buying 20,
you buy five on a quarterly basis,
and then you rebalance those five 12 months and one day
to capture the tax effect.
Does that make sense?
AUDIENCE: OK.
Yeah.
Thank you.
AUDIENCE: So if more people use the same strategy,
wouldn't your strategy just be equal to the market?
TOBIAS CARLISLE: Possibly.
But Joel Greenblatt wrote that book in 2006.
The magic formula doubled the return of the stock market
last year.
So the magic formula has continued to work.
And the reason that it continues to work
is that there aren't that many value investors out there.
It's a new strategy, and within that new strategy,
the deep value investment is a new strategy.
Most guys who are value investors
would try to emulate Buffett, they're
franchise-type investors.
There are very few guys who are really deep value guys.
And there's also this problem that you'll find.
If you look at the data-- I'll give these positions away
for free on theacquirersmultiple.com,
and you can go ahead there and you'll be able to see,
they're quite frightening positions.
You'll be buying very cheap iron ore mining companies,
you'll be buying very cheap oil and gas companies.
A few years ago, you'd have been buying for profit education.
They're frightening to buy.
And that's the real thing that drives returns.
That's why I went through that part of the presentation that
says you have to follow the model.
That's the most important thing, without fear of failure,
you can't cherry pick.
Because what you do when you cherry pick is
you do what everybody else does, and you
avoid the things that generate the really big outperformance.
AUDIENCE: OK.
I've got a followup question.
Have you tried the same strategy in different markets?
TOBIAS CARLISLE: Yes.
Yes, so the magic formula works.
And I discuss this "Deep Value", but the magic formula
works in Japan, it works in the UK,
and it works in Europe ex the UK.
In each of those instances, the acquirer's multiple
alone outperforms.
The only place where that hasn't happened
for the period of the data that we examined,
which was about 14 years, because there just
isn't that much international data.
But Japan, the magic formula seem
to outperform the acquirer's multiple alone in that one
single region over that 14 year period that we examined.
AUDIENCE: Hey.
So I was wondering, what does your company do then?
Do you just follow the model as well?
TOBIAS CARLISLE: So basically, we
have some additional things that we do.
Quantitative value goes through, quite comprehensively,
a very big model that you could use.
And we sort of discuss.
You can do things like avoiding companies
at high risk of financial distress,
avoiding fraud, avoiding earnings manipulators,
looking for financial strength, looking
for quality of earnings.
So that's making sure that the cash flow into the business
matches the accounting earnings.
All of those things add a little bit of additional performance
at the edges.
But the big muscle movement, the big driver of performance
is the acquirer's multiple.
So yes, that's what I do.
That's what I apply.
AUDIENCE: One question.
Why won't Buffett move to liquidator to operate.
Is it because he had too much money?
And another question related to the first book
of Joel Greenblatt.
He's talking about the spin off and also
the special certification.
I think his point is that just buying those would be good,
but if you can pick your spot, yeah, that would be better.
I think his performance in test was
like 50%, that's the highest I've ever known.
Then his way is like this is the area you want to pick from,
and then you want to pick the best.
That is kind of not dissimilar from what you are saying here.
You're saying things like don't involve any human being.
Just buy everything, right.
Yeah.
Just want to know your--
TOBIAS CARLISLE: His first book, "You Can Be a Stock Market
Genius", which is a terrible name for a really good book,
the process that he describes in there
is a reasonably complex investment strategy.
It's a strategy that really only a human being could implement
at the moment, because it requires
reading unusual filings and finding spin-offs or companies
about to pay out a big dividend, special situation investing.
I don't think he could have invested
as much money in that strategy as he can in the magic formula,
and I think that that's a more difficult strategy
to implement.
You can certainly get expertise in an area
and understand better than another area.
Spin-offs, special situations are really a very broad basket
of potential things that you can do,
and you can become good in an industry
or good at a particular thing, and that
might lead to better performance.
I sort of think that the broader your strategies,
and the broader your potential universe of stocks,
the better performance you're going to have.
I think to maintain that very high rate of return,
he was paying out lot of the capital that he was--
so he'd make the profit and pay it out, and then do it again
the next year.
Whereas the magic formula's a compounding strategy.
You roll over whatever you get and you
reinvest that compounded amount, and it's
a strategy that really shows how good it is over a longer
period of time, because you get to that point
where you're investing larger sums of money
at a higher rate than a special situation one, which requires
that you keep on paying it out and you're sort of
limited a little bit in where you can apply.
AUDIENCE: [INAUDIBLE]?
TOBIAS CARLISLE: Why did he change?
AUDIENCE: Yeah, yeah, yeah.
Why'd he change?
TOBIAS CARLISLE: Possibly the challenge.
Maybe he had too much money to invest.
AUDIENCE: So one of the biggest criticisms
of any model created looking at backward
data is you are fitting data, right.
And so the strategy that is outlined feels a bit like,
well, let's ignore quality and go for the liquidation value,
the enterprise value, buying price of the company.
And that could be kind of a valid criticism.
What would be your answer to that?
TOBIAS CARLISLE: Well, we've looked
at it in different markets, too.
So I certainly didn't go into it-- it's not a great marketing
strategy to tell somebody that what we're going to do
is buy the lowest quality stocks that we can find,
or not really care about quality.
It's a much better marketing strategy to go and say,
what we do is we look for really high quality stocks that
are undervalued, and that's what we buy.
Because that sounds like a really safe strategy to me.
If you said I'm going to ignore quality,
people think you're crazy.
So I've written an entire book explaining
why I ignore quality.
We didn't set out to find that.
It was just something-- it's sort
of unavoidable in the literature.
You find this every single time you read.
I give a sample of the studies in the book,
there are a lot more studies in the book.
There's another thing that looks at admiration.
So what are your positive or negative feelings
about a company, and they rank all of the companies
on the basis of how liked or hated they are,
then they examine their performance the following year.
The most hated companies outperform the most admired
companies.
Morningstar gives rankings to companies,
this is an A company, A plus, this one's a B, this one's a D,
this one's an E. The Es comprehensively
outperform the As.
Every single time you look at research,
and it's so counter to everything
you see everywhere else, where everybody's
saying find the really high quality ones.
So I didn't go looking for it.
But when you look at this sort of strategy outside
of this market, you find almost the same thing.
The one exception to that is Japan.
For whatever reason, the magic formula
over the period of the data that we examined
outperformed the earnings yield alone.
Japan's an outlier in a number of different strategies.
Momentum hasn't worked in Japan, whereas that's
worked in lots of other markets.
Even though Japan has sort of been gently falling--
this is just an interesting side comment-- even though it's
been gently falling since 1990, value investing
strategies in Japan have worked really well,
just buying the cheapest companies on the basis of price
to earnings, price to cash flow, price to book
has done something like 20% a year.
So the magic formula might have just captured a little bit more
of that performance.
But yes, there's always a risk of data
mining in this sort of stuff.
I think that the acquirer's multiple is the way
that value investors think about investing.
So if you think about the way that you're
instructed in "Security Analysis" or "The Intelligent
Investor" or any of Buffett's letters,
he says think about buying the company,
and it's think about buying the entire business.
You're not buying a share of it, you're buying the whole thing.
And that's exactly what the acquirer's multiple does.
It says, this is what you pay for the market capitalization,
but don't forget that you've got preferred stock, debt, minority
interests, off balance sheet liabilities,
underfunded pensions, other things that you have to fund.
But you get the benefit of the cash that's
sitting in the bank, all the net cash,
and then you have this discretion
to spend the operating earnings as they're coming in on capex
or paying down debt, or various other things.
So it's looking at the same thing
that big acquirers look at, and I often
find that I'm in positions that somebody else has
been buying right behind me.
It's a Carl Icahn or another activist,
or a private equity firm.
Something happens in them.
Because when they get very cheap,
there's a little bit of instability.
It's not a situation that should persist like that.
There's sort of inviting external managers
to come in and try to rectify the situation.
Data mining is always a problem, but I
think there are a variety of reasons
why this strategy should work quite well,
and it does work quite well in different time periods
and in different markets.
AUDIENCE: Buffett talks about holding stocks
for the long term, and one of the reasons
that he likes it is because of the tax consequences.
Can you talk a little bit about the difference
between trying to find companies that you
can hold for a long period of time versus rebalancing
and using the deep value strategy?
TOBIAS CARLISLE: This is one of the arguments
for-- this is what the franchise investors are trying to do.
They want to buy that company that
can sustain those very high returns on invested capital,
and ideally grow and compound away, throwing off cash
while they're doing it so that 10 years or 20 years from now,
you're getting out of it in dividends
what you invested in it, and it's
a much more valuable company.
So if you think about the process of going and finding
one of those companies, knowing that there's
that mean reversion in return on invested capital,
what would you rather buy?
Do you want to buy one that has a very high return on invested
capital that might mean revert, or are you better off finding
one that's very cheap, that might be at a trough,
that you can hold for seven or eight
or nine or ten years that reverts up.
I think you've got as much chance of selecting.
The challenge is not to buy something
that looks like it's high quality now,
the challenge is to buy something and hold
something that is high quality.
So we all want to buy the same stocks.
Maybe I haven't made that clear enough.
We all want, I want to buy, everybody
wants to buy the really high-- everybody wants
to own the really high quality stock,
the compounding machine that has the very high returns
on invested capital.
But can you use historical data to identify them?
It's not clear.
But what is clear is that buying the ones
with higher returns on invested capital
don't necessarily turn into ones that
have high returns on invested capital subsequently.
AUDIENCE: So one thing you talk about in the book is,
one of the reasons this strategy works
is because of activist investors are kind of arbitraging
into an alternate market where companies are being traded, not
because they want the stock to trade high,
but because they want to take control of the company.
And so activism is a pretty important kind
of catalyst to this strategy.
What do you think about this strategy working for companies
where the incumbent management is an owner operator,
and they have motivations that are completely
independent of the minority shareholder motivations,
and not allowing the activists to get in.
So I'm thinking like in countries in Asia,
where a lot of the companies are owner operator companies.
They really have really different motivations,
and even though it could be cheap on the acquirer's
multiple, what are your perspectives
on why would it work in those markets
when the founders have no reason to let anyone in?
TOBIAS CARLISLE: You might need to look
at what they're doing with the cash flows when they get them.
Are they paying them out, are they
using them to expand the size of their kingdom, are they
investing them in lower return businesses?
I think that it definitely does work in the US,
because you have that tension when somebody comes in.
But I think if you look in the US
at the-- and this is something that I looked at,
the number of those companies that are actually taken over
or have some sort of activist event
occur is actually still quite small.
It's only about 1/20.
And so for the other 19 it's simply that mean reversion
in the business that generates the returns.
So when you're looking at Asia, if you're looking at Japan,
for example, where it's a fairly insular society,
but there are some sort of inroads
being made by American activists there.
Even though the period of time where
there wasn't a lot of activist activity,
there still were quite good returns to those undervalued
companies, even [INAUDIBLE] across shareholdings.
And I acknowledge now that they have been very undervalued,
and maybe they're less so now over the last few weeks.
But it still has work there.
I don't think it's necessary for it to work,
but there are always metaphysical reasons
outside the realm of the backtesting
that I can't answer, and you're sort
of going on faith a little bit.
Perhaps if those markets open up, too.
They continue to open up and they adopt more external type
management, because it's a globalising world.
They have to sell outside their boundaries,
so they need different types of management.
AUDIENCE: Thank you so much for your talk.
It was great.
TOBIAS CARLISLE: Thank you.
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