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Session 10: Estimating Hurdle Rates - Bottom up Betas - YouTube
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- In this session 10,
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of a 36 session corporate finance class,
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I'd like to offer you a better
way of estimating betas.
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I call this a bottom-up
beta, but I'm going to start
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with the business or
businesses a company is in,
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and build up to a beta
that is more robust,
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and better estimate of
what's coming in the future.
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The last session, we laid the foundations
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of thinking about betas.
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We argued that while you might
get a beta from a regression,
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your betas of company is
determined by choices you make
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about what business to be in,
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what your fixed cost structure looks like,
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and how much you borrow.
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In this session, I'd like
to take a pragmatic step
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towards estimating
betas, which in a sense,
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will allow you to bypass
the regression beta process.
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To understand this, let's go back again.
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We're trying to estimate the hurdle rate
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for a company, right?
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We talked about risk-free rates,
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we talked about equity risk premiums,
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we're now in the process
of talking about betas.
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We've laid the foundations for betas,
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by both looking at regression betas,
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and the determinants of betas.
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Now let's talk about a
process for estimating betas
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that's better than a
regression beta process.
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To understand what we're going to do,
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let me lay out a property that betas have,
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that make them incredibly
easy to work with.
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Betas are always weighted averages.
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You're saying, what are you talking about?
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What does that even mean?
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Let's take an example.
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Let's assume you have a mutual
fund, Fidelity Magellan,
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huge mutual fund, hundreds
of stocks in your portfolio,
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and you ask me what the
beta of your portfolio is.
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It's very simple.
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It's a weighted average of the betas
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of the individual stocks
in your portfolio,
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in proportion to how much money
you've invested in each one.
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That's true whether you
have five stocks, 50 stocks,
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or 500 stocks in your portfolio.
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The beta for a mutual
fund is a weighted average
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of the beta of the
investments in that fund.
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But here's the property we're going to use
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in corporate finance.
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Remember we talked about GE,
25 to 30 different businesses?
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The beta for GE as a company
is a weighted average
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of the betas of each of those businesses.
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If we could somehow get betas by business,
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and attach weights to the business,
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we can get a beta for a company
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by weighting the betas of
the individual businesses.
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I call this a bottom-up beta,
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but don't get too caught
up in terminology.
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You can call it whatever you want,
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but here's how the process works.
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Let's assume you have a company.
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Let's assume a company
is in two businesses,
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steel and chemicals.
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Here's the way I'm
going to estimate betas.
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I'm going to go find as many
publicly traded steel companies
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as I can, and as many publicly
traded chemical companies
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as I can.
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Why publicly traded?
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Because I can get regression
betas for each of them.
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Then I'm going to average the betas
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across all steel companies,
and average the betas
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across all chemical companies.
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So I get an average regression
beta for steel companies,
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and average regression beta
for chemical companies, right?
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That regression beta is a levered beta.
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So I'm going to take an
average debt to equity ratio
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for steel companies, and unlever the beta
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for steel companies, an
average debt to equity ratio
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for chemical companies,
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unlever the beta for chemical companies.
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I'm going to get an unlevered beta
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for being in the steel business,
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an unlevered beta for being
in the chemical business.
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Some people like to call
these pure play betas,
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to reflect the betas for the businesses.
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Then I come back to you.
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So you told me you're in two businesses,
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tell me how much value you
get from each business.
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You might say 70% from
steel, 30% chemicals.
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In fact, you might say you
don't know what the value is,
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that all you can give me are revenues.
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I'm okay with that.
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Let me start with that.
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So 70% steel, 30% chemicals,
I get a weighted average.
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That's the unlevered beta of your company.
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Final step, I ask you what
your debt to equity ratio is.
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You can give me your
actual debt to equity,
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a target debt to equity, or
some other number in the middle.
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I'll tell you what the beta
for your stock should be.
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Now why am I doing this?
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Because I don't like
regression betas, right?
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But where did I get the betas
for the steel companies?
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From running regressions.
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So you're saying what's the gain?
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What's the advantage of using an average
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of 100 regression betas,
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rather than using one regression beta?
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Remember the law of large
numbers in statistics?
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What I read when I see
the law of large numbers,
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is the average of 100 bad numbers
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can be a really good number.
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An average of 100 bad regression betas
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can actually give you a
good beta for a business.
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So one reason I prefer bottom-up betas
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is they're more precise.
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The second reason is I
get to set the weights.
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Put differently, if you entered
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the chemical business yesterday,
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there is zero chance
that a regression beta
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for your company would reflect it,
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but I set the weights.
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In fact, I can be proactive.
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If you tell me you're going to be
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in the technology business tomorrow,
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I can bring in the weight
for the technology business,
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and a beta for a technology business.
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With bottom-up betas, I
regained control of the process.
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I'm no longer at the mercy
of a single slice of history,
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and a single regression.
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So let's try this for Disney.
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One of the advantages of
applying this approach to Disney,
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is Disney gives you a
fair amount of detail
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of the businesses they're in,
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and Disney breaks themselves
down into five businesses,
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and these are the five businesses
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they describe themselves as being in.
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The first is broadcasting.
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That includes Disney
Cable, it includes ABC,
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and of course it includes ESPN.
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Then the second business
is a theme park business,
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Disney World, Disney Land,
Euro Disney, Tokyo Disney,
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and the very beginnings of
Hong Kong Disney in 2013.
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The third business is the movie business,
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which for Disney, stretches
across multiple entities.
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It includes Disney Animated
Studios, the old part of Disney.
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It also includes Pixar, Marvel,
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and LucasArts, very different entities,
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but they're all under the movie business.
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The fourth business that Disney is in
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is the consumer product business.
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The licensing revenues they get
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when they license out characters,
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for people to make toys and
other characters out of,
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and maybe even games or software.
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So that's in the consumer
product business.
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And finally, there's interactive gaming,
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which is Disney's newest business,
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its attempt to come up with apps,
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and other games for computers,
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smartphones, and other devices.
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Five different businesses,
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and Disney actually gives
me a fair amount of detail.
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It gives me revenues and income,
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depreciation, capital expenditures,
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even identifiable assets by business,
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and I'm going to use this information
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to try to come up with a
bottom-up beta for Disney.
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So here's the first step.
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I need to come up with betas
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for each of the five businesses, right?
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So I'm going to take an example.
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In fact, I'm going to take the business
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where I had the easiest time
coming up with the beta,
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and then talk about how
I came up with the betas
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for the others later.
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Let me take the movie business,
that's third on the list.
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I wanted a beta for
Disney's movie business.
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So I went looking for publicly
traded movie companies.
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So I'm going to go to the next page,
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but I'll return back to this page.
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But here's the movie business in the US.
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I found 10 publicly traded US companies,
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which are movie companies.
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You can see that they're very different
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market capitalizations.
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They range from very small,
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Odyssey for instance, is a tiny company,
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to Twenty-First Century
Fox, which is huge.
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But in a sense, the market
caps don't bother me.
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These are all publicly traded,
so I have regression betas
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for each of the companies.
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I can also get their market value,
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in addition to market cap,
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I can get the debt and cash,
which allows me to compute
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both the debt to equity
ratio for each company,
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and I need market values for those,
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and how much cash each company has
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as a percentage of firm value.
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Firm value is market
value of equity plus debt.
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The enterprise value
column that you see there
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is market value of equity
plus debt, minus cash.
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I'm going to keep track
of both these numbers.
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Here's how I'm going to
come up with the beta
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for the movie business.
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I take the average regression beta
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across these movie companies.
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The number I get is about 1.24.
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Actually, let me take that back.
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The average numbers that you
get across these companies
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is skewed by outliers.
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There are a few extreme
numbers, not in the beta column,
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but in the debt to equity.
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So I'm going to use the median numbers.
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It allows me to kind of
get rid of the outliers.
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The median regression beta
across these companies is 1.24.
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First step is unlever the beta.
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The median gross debt to equity,
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total debt over equity for
these companies is about 27%.
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I unlever the beta and I
come up with about 1.066
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as my unlevered beta.
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I'm almost home, but here's what I notice.
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These companies have cash of about 3%
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of their overall value.
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So one way to think about this
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is the unlevered beta
for the company is 1.066,
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but 3% of these companies is cash,
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and if I want an unlevered beta
for just the movie business,
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I should be dividing that 1.066 by the 97%
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that's just movies.
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That gives me my unlevered
beta for the movie business.
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It's about 1.099.
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That's the process by which I'm
going to get unlevered betas,
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and while I will not go
through the process in detail
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for the other businesses,
I did exactly what I did
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for the movie business, for
the other businesses as well.
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But there were some
issues I had to deal with,
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in terms of sample size
with the other businesses,
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and let me talk about some of the ways
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I got around the issues
of small sample size.
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Take the broadcasting business.
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There aren't too many publicly traded,
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big broadcasting companies left in the US.
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They've either been assimilated
into other companies,
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or they don't trade on their own.
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So here's how I expanded my sample.
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I looked for other companies
that generated their value
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from the broadcasting business.
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A couple of examples, I used Nielsen,
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a company that rates TV shows
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as part of the broadcasting business,
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because it derives its revenues
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from the broadcasting business.
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I used King World, a company
that syndicates TV shows
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and sells them to broadcasting companies
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as being in the broadcasting business.
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I ended up with about 26
companies in my sample,
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and that's a pretty good sample.
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I'm okay with that, and
I used that to come up
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with the unlevered beta for
the broadcasting business.
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And the theme park business,
initially, I stayed US focused,
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and I found only two publicly
traded theme park companies,
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Six Flags and a Midwestern
company called Cedar Point.
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But basically these
are only two companies.
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I need a much larger sample.
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So to expand my sample, I went global.
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I looked at theme park
companies in Europe and Asia.
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They say you can't do that.
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Why not?
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Betas are standardized around one,
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there's no reason why I
need to stay US-centric.
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I ended up with about 20 companies,
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and an unlevered beta for being
in the theme park business.
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Consumer products and interactive gaming,
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I had a much easier time.
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At the end of the process though,
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if you look at the very last column,
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I have the unlevered betas for
each of the five businesses.
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First part of the mission is complete.
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Let's move to the second step.
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Before I go to the second
step, one more parse.
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Remember the studio business,
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and the way I came up
with an unlevered beta?
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Here's another way you can
think about what I'm doing.
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Think about a balance sheet, right?
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You've got cash and the
movie business on one side,
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you've got debt and equity on the other.
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This is the different way I have
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of working through how I ended up
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with an unlevered beta
for the movie business.
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I start with the equity beta,
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which is what a regression beta is.
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It's 1.24.
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I use the debt to equity ratio
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to come up with an unlevered
beta for the entire company.
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That's 1.066.
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Then I look at how much
cash the company has
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as a percentage of value, 3%.
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And that's how I come up
with the unlevered beta
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for the movie business.
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It's a useful exercise to
do this at least a few times
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before you get comfortable
with levered betas,
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unlevered betas for companies,
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and unlevered betas for businesses,
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'cause it's easy to get confused
among the three concepts.
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Now that we have the unlevered betas
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for the five businesses,
let's start weighting.
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To get the weights I initially
started with revenues,
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but I did not stop there.
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I could use revenue weights,
but then I'm assuming
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a dollar in revenue in each business
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is roughly of similar value.
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Instead of doing that,
I went back to those
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publicly traded companies
from which I got the betas.
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So the movie companies, for instance,
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I had their enterprise value
for each company, right?
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Remember, the market value of
equity plus debt, minus cash.
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I divided that enterprise
value by revenues
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for each movie company, and
I came up with a median value
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across movie companies.
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I applied that multiple to the revenues
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that Disney got from the movie business
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to get an estimated value
for Disney's movie business.
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See what I'm trying to do?
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I'm trying to convert revenues into value,
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and I'm using that EV to sales,
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enterprise value to sales ratio
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from comparable companies
to estimate the value.
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Once I make those estimated values,
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I use those estimated
values to get my weights,
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'cause ideally, I'd like these
weights to be value weights.
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Based on my estimates, the
biggest part of Disney right now,
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is its broadcasting business.
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The next biggest is the
theme park business.
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The movie business is only about 13%,
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and the consumer products is about 7%,
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and everything else is 1%.
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This company is made
up of five businesses,
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but some businesses
are bigger than others.
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I take a weighted average
of those unlevered betas,
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and the weighted average I get of 0.9239
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is my unlevered beta for
Disney's operating assets.
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Now Disney does have some
cash, about 3.9 billion.
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That cash has a beta of zero.
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I could compute a beta
for Disney as a company,
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an unlevered beta for Disney as a company,
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and that's what the beta
0.8978 at the bottom tells you.
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That's the beta for Disney as a company.
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But Disney as a company
also incorporates cash.
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I'm actually going to use the
operating asset beta, 0.2939,
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more that I use the unlevered
beta for Disney as a company.
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But I thought it would be
useful to compute both numbers.
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Final step in the process
is I need a levered beta,
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and here I ran into a bit of an issue.
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I need a debt to equity ratio by business,
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and Disney does not borrow by business,
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it borrows as a company.
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In fact, it has about 15.96
billion dollars worth of debt.
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Now I could assume that every business
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has the same debt to equity
ratio, but rather than do that,
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I decided to try to allocate
the debt that Disney has
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across the businesses, based
not on their market values,
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but based upon their identifiable assets.
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Why that?
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I thought that made more
sense than using market cap,
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or cash flows, or earnings.
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I mean, you might choose
a different proxy,
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but I allocated the debt
across the divisions.
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That gives me different
debt to equity ratios
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for each business, and
those debt to equity ratios
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are what I use to come
up with a levered beta
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for each of the five businesses,
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and you can see how different they are.
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The levered beta for Disney
as a company is close to one,
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but they're much higher
for some businesses,
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and much lower for others.
[893]
The theme park business
has a much lower beta
[896]
than the broadcasting business does.
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The costs of equity that come out of this
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are very different across
the businesses as well.
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The cost of equity for Disney as a company
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is only about 8.5%.
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For the broadcasting
business, it's much higher,
[909]
for the theme park
business, it's much lower.
[911]
Now here's why it matters.
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Disney is a multi-business company.
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When it looks at an investment,
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that investment doesn't
cut across businesses.
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It's often in a single business.
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So let's say that you're
looking at a movie project.
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I don't care, you can name the movie.
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Let's assume this movie
project is expected to deliver
[928]
a return on equity of 9.5%.
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See why I picked the 9.5%?
[933]
It's higher than the cost of
equity for Disney as a company,
[936]
but lower than the cost of equity
[938]
for the broadcasting business.
[939]
The question I would
like you to think about,
[942]
is would you accept this investment?
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Put differently, what should you compare
[946]
this cost of equity, or
this return on equity to?
[949]
Should we compare to the cost of equity
[950]
for the company taking on the project,
[952]
because after all, it
comes up with the money?
[954]
Or should we compare to the cost of equity
[955]
of the business the project is in?
[959]
Logically, I really think it
should really be the business,
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and here's why.
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If you use the company's cost
of equity across the board,
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here's what you're going to end up doing.
[966]
You're going to end up subsidizing
your riskier businesses
[969]
with your safer businesses, and over time,
[971]
your safer businesses are going to shrink,
[973]
and you're going to
become a riskier company.
[975]
The right thing to do at a company,
[977]
if you're a multi-business company,
[978]
is to come up with different hurdle rates
[980]
for different businesses.
[982]
I'll also have to tell you
[982]
that most companies don't do this.
[984]
Part of the reason for
that, is if your only way
[987]
of getting betas is a regression beta,
[989]
you get only one beta for a company,
[991]
even one as large as GE.
[992]
You have no way of
estimating betas by business.
[995]
This is one of the bonuses of using
[997]
a bottom-up beta approach,
is you can estimate
[999]
a cost of equity by business,
[1001]
and use that cost of
equity to make a judgment
[1004]
on whether that business
is a good business or not.
[1008]
That's pretty much what I want to say
[1010]
about bottom-up betas, but we'll come back
[1012]
to this in the next session.
[1013]
Thank you very much for listening.
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