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Session 4: Defining and Measuring Risk - YouTube
Channel: unknown
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- We're now in session four of
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a 36-session corporate finance class.
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And we're starting in
the meat-and-potatoes
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part of corporate finance.
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In this session, I want to define risk:
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A central measure in
any financial decision.
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I want to look at conventional
definitions of risk,
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and how financial theory measures risk.
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In the process, we will set up
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the estimation questions that will be
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coming up in the next few sessions.
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In this session, we're going to
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start a discussion of hurdle rates.
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To put it in perspective,
remember we talked
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about the objective in corporate finance,
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that's maximize firm value.
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If you accept that
objective, the next question
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becomes picking the right projects.
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And to pick projects right, you need
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a hurdle rate for projects.
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So what we're effectively talking about
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is that sub box in the big picture.
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The hurdle rate should be
higher for riskier investments,
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and lower for safer investments.
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And to put that hurdle rate in place,
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we need to measure risk and
convert it into a number.
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12%, 15%, 18%.
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So let me start off by giving you
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the building blocks for a hurdle rate.
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I call it a benchmark, and
what I mean by a benchmark,
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is when you make an
investment you need to tell me
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what you need to make on that
investment to break even.
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Not even to be happy, but to break even.
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And that benchmark
becomes the hurdle rate.
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So let's not make this more
complicated than it has to be.
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If you ask me what the hurdle rate for an
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investment should be,
there are two pieces to it.
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The first is what I would make on
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an investment with absolutely no risk.
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Let's call that the riskless
rate for the moment.
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And then I'm going to
add a risk premium on top
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to reflect the fact that some investments
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are riskier than others.
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And the riskier the investment,
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the larger that risk premium.
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So fundamentally, there are two questions
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I need to answer to
put this into practice.
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The first, I need to measure risk.
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What do you mean by risk?
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Second, I need to be able to convert that
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risk measure into a risk premium.
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So let's start with the
first of those two questions:
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What is risk?
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I know if you've taken a finance class,
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you've been programmed already.
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You tend to think of risk in
terms of statistical terms.
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Variants, volatility, or maybe
even Greek alphabets, betas.
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But my favorite definition of risk
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is several thousand years old.
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It's a Chinese symbol
for crisis, or big risk.
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The Chinese symbol for crisis or big risk
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is actually a combination of two symbols:
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Danger and opportunity.
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Risk is equal to danger plus opportunity.
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That's the perfect way
to think about risk.
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Risk is neither good nor bad.
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It's a combination of
danger and opportunity.
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And the reason I like that linkage,
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is we all like opportunity, right?
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We all want to make 70% returns.
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And this definition cautions us.
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It says look, if you
want to make 70% returns,
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be willing to live with a lot of danger.
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You don't like danger? Then
don't ask for 70% returns.
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Think of how many mistakes in investing
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and corporate finance will be avoided
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if we remember that linkage.
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We tend to fall for fads
and scams when we think
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we can be exposed to opportunity
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without being exposed to danger.
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One way to think about what
we're going to try to do
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in corporate finance, is
we're measuring the danger
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in an investment and
asking, how much opportunity
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do I need to compensate
me for taking that danger?
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We're not bungee jumpers
in corporate finance.
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We don't take on risk for
the sake of taking on risk.
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We take on risk because we expect
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to get rewarded for taking that risk on.
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So here's what I'm going to try to do:
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I'm going to take everything I know about
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risk and return models in finance,
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and fit it into a single page.
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Might take some doing,
but I don't want to spend
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sessions after sessions after sessions
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talking about theory of risk and return.
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So here's how I think about
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risk and return models in finance.
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In fact, the reason I'm able to
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compress them into one
page, is the first two steps
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in the derivation are exactly the same
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for every risk and
return model in finance.
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In the first step, we define risk
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as the deviation of actual returns
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around an expected return.
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What am I talking about? I'll
give you a couple of examples.
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Let's assume you have a
one-year time horizon,
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and you buy a one-year U.S. Treasury bond.
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Let's for the moment assume that
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the U.S. Treasury has no default risk.
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If the one-year Treasury bond right now
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has a yield of half a percent,
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if at the end of the
year of holding period,
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I came and knocked on your door and asked,
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"How much do you make on your investment?"
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The answer is always going
to be half a percent.
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That is a truly riskless investment,
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where the actual return is always
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equal to the expected return.
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Let's move one step up the ladder.
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Let's assume you buy a
ten-year U.S. Treasury bond,
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and you still have a
one-year time horizon.
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At the end of the year, if I
come and knock on your door,
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and asked you what return
you made on your bond,
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part of it is going to be certain.
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The coupon is going to be guaranteed,
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but the price could have changed.
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Because every time interest
rates change, prices change.
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So a ten-year T-bond is not risk-free
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if you're looking at a
one-year time horizon.
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Now if you bought a
stock, let's say Google,
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and I came to you at the end of the year
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and asked you, "What did
you make on the stock?"
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You can be almost guaranteed that
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you will never make what you expected
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to make at the start of the period.
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So if you expected to make a 15% return,
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and you got lucky, you might have made
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an 80, or 100, or 150% return.
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If you weren't, you
might have dropped 30%.
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That's a very risky investment.
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Notice that in all three cases,
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I defined risk by looking at
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what happens over the next year.
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And that's a point I want to emphasize:
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There is no risk in the past.
All risk is in the future.
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Unfortunately, all our risk measures
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come from looking backwards.
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But risk is always in the future.
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So the first step in every
risk-and-return model in finance,
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is we define risk as the deviation of
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actual returns around an expected return,
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and the greater the deviation,
the riskier the investment.
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So far, so good, right?
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It's the second step that
gives people trouble.
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The second step, I say
that, when you're exposed
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to risk in investment, don't expect
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to get rewarded for all that risk.
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You think, "Why not?"
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Let's take an example. Let's
assume I invested in Disney.
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Think of all the risks you're exposed to.
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You're exposed to the risk that
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the next movie they come up with
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might not make as much money
as they thought it would.
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It might even lose them money.
Remember The Lone Ranger.
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The other example is maybe that
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California Adventure theme park
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they've invested a billion dollars in over
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the last few years might not pay off.
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Those are risks that affect
Disney and just Disney, right?
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Disney is also part of
the broadcasting business.
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Any loss that affects
the broadcasting business
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will affect Disney and
maybe five, ten, fifteen
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other companies in that business.
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That's risk that affects a few companies.
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But let's assume interest rates
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go up a lot in the next few months.
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That's risk that affects not just Disney,
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but almost every company.
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The reason I'm separating
risk that affects
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a few companies from risk
that affects most companies
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is here's where we make a distinction:
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If you're an investor who's diversified,
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and in corporate finance,
all risk-and-return models
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make the assumption that
the marginal investor
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looking at a company, and we'll come back
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and define marginal investor in a moment,
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is a diversified investor.
You're saying, "So what?"
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If you're diversified, you own stock
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in Disney, and maybe 50 other companies.
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Risk that affects one or a few firms,
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will tend to average out.
Average out in what sense?
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For every company, when something
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better than expected
happens, in another company,
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something worse than expected will happen.
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Things will average out
across your portfolio.
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That's the law of large numbers in action.
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However, risk that affects
most or all companies
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you cannot diversify away.
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So in finance, we assume that
because the marginal investor
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is diversified, he or she does not care
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about risk that affects a few companies.
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That's called firm-specific risk.
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But does care about risk that
affects most or all companies.
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And that's called market
risk, it's macro risk.
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And what we try to measure
and bring into a hurdle rate
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is only the latter, market
risk or macro-economic risk.
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And that's where the different
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risk-and-return models
in finance part ways.
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The oldest risk-and-return
model in finance
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it's called the capital asset
pricing model, or the CAPM,
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makes the assumption that there
are no transactions costs,
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and that nobody knows which
stocks are cheap or expensive.
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If you make those assumptions,
you will diversify
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and keep diversifying
until you have every single
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traded asset in the
world in your portfolio.
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That's called the market portfolio.
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And since all of us hold that portfolio,
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the risk of an investment becomes a risk
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added to that market portfolio.
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That's captured with a single
number. It's called the beta.
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So next time you use beta,
remember all the baggage
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you're carrying with
you when you use a beta.
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But in the CAPM, the risk that cannot be
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diversified away, is
captured in a single beta.
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For a few years, perhaps
as long as 14 or 15 years,
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that was the only model in town.
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In 1978, Steve Ross at Yale said,
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"This is crazy. Why are
we trying to measure
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"all of that market risk in one beta?
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"Why don't we allow for
multiple sources of market risk,
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"and measure the beta
against each one separately?"
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Nice idea, right?
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When he first came up
with it, people said,
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"Well how will we know how many measures
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"of market risk there are,
and what those betas are?"
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He said, "No problem. Give me some
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"stock price data and I'll tell you."
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Ane he did. He took a long
period of stock price history,
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and he let the computer
go on a search mission
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looking for common patterns or factors.
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The computer came back and said
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we found five factors,
and here are the betas
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for each company against each factor.
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That's called an arbitrage pricing model.
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We have multiple unnamed economic factors
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and betas against each one.
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You can come up with a
hurdle rate using that model.
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But unfortunately, it doesn't have
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much intuitive feel to it,
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because those factors remain unspecified.
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Now in the decade following
the arbitrage pricing model,
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people tried to attach
macroeconomic names.
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interest rates, term structure,
inflation, GDP growth
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to those factors. And when you do that,
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you've gone from an
arbitrage pricing model
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to a multi-factor model.
That's much better, right?
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Multi-factor models
actually do much better
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in explaining the past, but the
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factors themselves are very unstable.
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What I mean by that, is
the macroeconomic factors
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that drove stock prices in the last decade
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might not be the factors that
drive them in the next decade.
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As a consequence, neither
the arbitrage pricing model
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nor multi-factor models caught on.
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Now to complete the
story, in the early 1990s,
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Eugene Fama and Ken French, two professors
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at the University of Chicago, decided that
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rather than try to
measure risk, they'd let
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the market tell us what risk was.
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What I mean by that, is they went back
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and looked at a long period
of stock price history
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to see what kinds of companies
had earned high returns.
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They found, for instance,
that small companies
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had earned higher returns than
large market cap companies.
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Small price-to-book companies had earned
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much higher returns than
high price-to-book companies.
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See then, what does that mean?
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They made a leap of faith.
They said that must mean
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that small market cap
companies are riskier
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than large market cap companies.
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Low price-to-book companies are riskier
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than high price-to-book companies.
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They didn't ask why and they did not care.
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Those models are called proxy models,
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and effectively you've
given up on measuring risk,
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you let something else stand in.
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Every risk-and-return model
you will see out there
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is either going to be one of these models
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or a combination of these models.
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Keep in mind, though, that they're all
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built into presumption that the
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marginal investor is a
diversified investor.
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Now given the fact that
many of these models
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post-date the CAPM. They've come
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in the last 10, 20, 30 years.
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You'd think that the CAPM would be
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a dead model, but you'd be wrong.
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A lot of people still use
the CAPM, including me.
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And when I use the CAPM,
there are a lot of people
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who pick apart that assumption.
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They say, "Why do you
continue to use a model
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"that is the oldest model out there,
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"and might, in fact, be a flawed model."
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In fact, there are three critiques I hear
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about the CAPM, and I'll list all three.
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The first is that it makes
unrealistic assumptions.
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Does it? Absolutely. But
that doesn't bother me.
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Because I used to be an econ
major in a previous lifetime,
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and I remember those models that made
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realistic assumptions
that nobody could use.
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I'd much rather have a model
with unrealistic assumptions
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that I can use rather than one that makes
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realistic assumptions that nobody can use.
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The second critique I hear is
my parameters might be wrong,
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my data might be wrong, my
risk rate might be wrong,
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my risk premium might be
wrong. Absolutely true.
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But anytime you're estimating the future,
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you are going to be wrong.
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If you let that be the dividing line,
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you will never use a model.
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The third critique
though cuts to the bone,
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and that is that the model
doesn't work very well.
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It is true that if the
CAPM with the right model,
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beta should explain
most of the differences
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in returns across stocks
over very long time periods.
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Beta doesn't. In fact, that was the basis
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for the Fama-French proxy model critique,
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where they said, "If beta
doesn't work very well,
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"let's look for something
that does better."
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That again is something
you've got to factor in.
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It's true that betas don't
explain past returns very well.
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Part of the reason for
that, I think is people are
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not very good about estimating betas.
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And we can improve the proportion of
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return variants that can be explained
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by betas if we do a better job.
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I still believe, though, that even if
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you do a better job, the bulk of
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the variation in returns
will never be explained.
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There's too much
irrationality, too much noise,
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so to speak, in markets, for
us ever to build a great model.
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The CAPM is a flawed
model, but it's going to
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hang around for a very simple reason:
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The alternatives are just as flawed.
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In fact, if you think
about replacing the CAPM
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with an arbitrage pricing
model, or a multi-factor model,
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or even a proxy model, here's what
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I'd like you to think about:
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These models might do better
at explaining the past,
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and they do. But they're
not that much better
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at explaining the future. In other words,
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if your hurdle rate is coming from
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an arbitrage pricing model
or a multi-factor model,
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I'm not sure it's any more precise than
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the expected return and hurdle rate
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that I'm getting from the CAPM.
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And given a choice
between getting a flawed
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hurdle rate with one
beta, or one parameter
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of five betas, I'm going to take
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the one beta every single time.
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So it is true that CAPM is flawed,
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but all of the models I'm
picking among are flawed.
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Here's how I'll end this session, though:
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To me the CAPM is a tool. For the moment,
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it does a at least as good a job
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as any other model out there.
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But if tomorrow, you are able
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to come up with a more
pragmatic or better model
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for measuring risk and
coming up with hurdle rates,
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I'd drop the CAPM in a minute.
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I'm not stuck on betas. I don't want to
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carry the baggage of
modern portfolio theory
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with me every time I do corporate finance.
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To me, the CAPM is a means to an end,
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a way to get to a hurdle rate. And if you
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don't like the CAPM, come
up with a different way
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of estimating hurdle rates, but don't
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throw the baby out with the bath water.
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What I mean by that, is don't abandon
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estimating hurdle rates just because
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you don't like betas and the CAPM.
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A final point: Before you
use any of these models,
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remember what you have to determine.
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You have to determine
whether the marginal investor
[905]
in your company is
well-diversified, right?
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Here's a very simple test. I went back to
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the list of top 17 stockholders in Disney,
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and what did I see? I see a lot of
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institutional investors. I see Steve Jobs,
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but remember, Steve Jobs cannot
be the marginal investor,
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and here's why: For an investor
to be the marginal investor,
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he or she has to own a lot of
stock and trade that stock.
[925]
So Steve Jobs owns a lot of stock,
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but doesn't trade that stock.
[929]
Larry Ellison might own 23%
of the shares in Oracle,
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but he doesn't trade those shares.
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If you look at the kinds of investors
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who can own a lot of stock and
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trade shares in these companies,
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it's almost always going to
be institutional investors.
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And odds are, those
investors are diversified.
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In the case of Disney, I feel
I'm on pretty safe ground
[948]
assuming the marginal
investor is diversified.
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Am I always that secure? No.
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There will be companies
where you get to the stage,
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and you feel doubtful about whether
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the marginal investor is well-diversified.
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What that effectively means is none of the
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models we talked about are very effective.
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We have to come back and
deal with that later.
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But for the moment, at least with Disney,
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I feel on pretty secure ground
[970]
that the marginal investor is diversified.
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In fact, looking across all my companies,
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I look at the institutional
investments in these companies.
[977]
These are all large-market
companies around the globe
[980]
with a lot of trading
volume, and much of the
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trading is done by institutions.
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I think I'm okay. Perhaps not as secure
[987]
as I was with Disney, even
with the other companies,
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the Baidu, Tata Motors, and
Vale, and Deutsche Bank,
[992]
assuming the marginal
investor is diversified.
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And the reason, again, that matters, it
[997]
allows me to focus in only on that risk
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that cannot be diversified away,
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and bring it into my hurdle rate.
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That's the first step, and I hope we can
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continue with this discussion.
[1007]
Thank you very much for listening.
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