In this course, we will discuss fundamental principles of trading off risk and return, portfolio optimization, and security pricing. We will study and use risk-return models such as the Capital Asset Pricing Model (CAPM) and multi-factor models to evaluate the performance of various securities and portfolios. Specifically, we will learn how to interpret and estimate regressions that provide us with both a benchmark to use for a security given its risk (determined by its beta), as well as a risk-adjusted measure of the security’s performance (measured by its alpha). Building upon this framework, market efficiency and its implications for patterns in stock returns and the asset-management industry will be discussed. Finally, the course will conclude by connecting investment finance with corporate finance by examining firm valuation techniques such as the use of market multiples and discounted cash flow analysis. The course emphasizes real-world examples and applications in Excel throughout. This course is the first of two on Investments that I am offering online (“Investments II: Lessons and Applications for Investors” is the second course). The over-arching goals of this course are to build an understanding of the fundamentals of investment finance and provide an ability to implement key asset-pricing models and firm-valuation techniques in real-world situations. Specifically, upon successful completion of this course, you will be able to: • Explain the tradeoffs between risk and return • Form a portfolio of securities and calculate the expected return and standard deviation of that portfolio • Understand the real-world implications of the Separation Theorem of investments • Use the Capital Asset Pricing Model (CAPM) and 3-Factor Model to evaluate the performance of an asset (like stocks) through regression analysis • Estimate and interpret the ALPHA (α) and BETA (β) of a security, two statistics commonly reported on financial websites • Describe what is meant by market efficiency and what it implies for patterns in stock returns and for the asset-management industry • Understand market multiples and income approaches to valuing a firm and its stock, as well as the sensitivity of each approach to assumptions made • Conduct specific examples of a market multiples valuation and a discounted cash flow valuation This course was previously entitled “Financial Evaluation and Strategy: Investments” and was part of a previous specialization entitled "Improving Business and Finances Operations", which is now closed to new learner enrollment. “Financial Evaluation and Strategy: Investments” received an average rating of 4.8 out of 5 based on 199 reviews over the period August 2015 through August 2016. You can view a detailed summary of the ratings and reviews for this course in the Course Overview section. This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.
Defending the CAPM
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Del curso dictado por University of Illinois at Urbana-Champaign
Investments I: Fundamentals of Performance Evaluation
272 calificaciones
University of Illinois at Urbana-Champaign
272 calificaciones
Curso 3 de 7 en SpecializationFinancial Management
De la lección
Module 3: Testing the CAPM, Multifactor Models, & Market Efficiency
In Module 3, we will discuss different asset-pricing models, the pros and cons of each, and market efficiency. In particular, we will test the effectiveness of the Capital Asset Pricing Model (CAPM) and examine survey data concerning its use by chief financial officers (CFOs) of firms. Predictable patterns in stock returns, such as the size and value effects, will also be examined and the Fama-French 3-Factor Model will be introduced. Market efficiency will be discussed in this module, as well as its implications for the asset-management industry and observed patterns in stock returns.
Conoce a los instructores
Scott WeisbennerWilliam G. Karnes Professor of Finance
Department of Finance, College of Business
[ Music ]
>> All right, so in this video, we're going to look
at what are some of the defenses for the kind
of lackluster performance, if you will,
at [inaudible] pricing model and predicted returns,
at least over the, you know, kind of the long history
from the 1920s till, you know,
kind of the most recent century here.
When you looked at performance from 1931 to 1965,
capital asset pricing model was performing extremely well,
predicting returns in a way that, you know,
the -- it should, okay?
If you look at, you know, kind of the, you know,
more long time series,
beta seems to only weakly predict stock returns
and less so, you know, kind of since the mid1960s.
So what's going on here?
You know, advocates of the capital asset pricing model,
I think their main defense would go something like this;
the model maybe isn't perform as well --
performing as well as we would
like because the market is mismeasured, okay?
Theoretically when we talk about this, you know, kind of market,
we are -- it should include all assets, not just the S&P 500,
real estate should be in there, like a key asset for young folks
who would be their human capital.
We just simply proxy for the market portfolio.
We aren't putting in all these, you know, kind of assets
that really are in, you know,
kind of a broad portfolio that people have.
We just proxy with the, you know, stock market
because we have, you know, good data on that.
we have daily on the stock market but by using a proxy
for the real market, this is inducing measurement error
into the regression, and could buy us against, you know,
kind of the CAPM performing as well as we would,
you know, kind of expect it to.
So mismeasurement of what the market portfolio really is
by using this proxy of, you know, kind of the stock market,
using this proxy as stock market maybe could explain somewhat why
the capital asset price model doesn't perform as well
as we would like it to.
Another defense of the capital asset price model --
and just in a recently published paper in the Journal
of Financial Economics back, I think, just in 2014,
that the capital asset pricing model actually does perform
pretty well on days where important macroeconomics,
systematic news, is released, okay?
So the authors of this are Savor and Wilson, 2014,
Journal of Financial Economics piece.
They basically, you know, do an analysis in spirit of Black,
Jensen and Scholes and Fama and French that we've, you know,
kind of talked about already in this module.
They use daily data over the last 12 months
to estimate betas, and then they form beta sort of portfolios
over the next month, so they're kind
of using a little different horizon for when they, you know,
kind of form the betas, but basically the same idea.
Use passed data to estimate betas for stock,
rank stocks by their estimated beta, and then look into the --
look ahead and see do the stocks
that have the higher beta typically have higher returns.
But the rub that they're adding is they're looking, you know,
does the CAPM predict well on days
where there's a big news announcement about some macro,
you know, kind of economic factor;
that's what they're really bringing to the table, okay?
What do we mean by macroeconomic news?
Well, it could be news about inflation, employment,
Federal Reserve, federal open market committee,
interest rate decision, okay?
So they're looking at days when those announcements occur.
Now if you look at the trading days,
these macro announcements are occurring about 13% of the time,
roughly one, you know, kind of one out of eight, so you know,
once every, you know, kind of two weeks or so.
So that's 13% of the trading days, but when you look
at the market risk premium, the performance of stocks, this --
these days when the macroeconomics news is
announced, they account for about 60%
of the equity risk premium during the year.
So these are important days, 13% of the days but 60% basically
of the stock market return occurring
on these macro news announcements, okay?
So let's look here -- and this is kind of a nice analysis
to get things started here, where they look
to show how important these announcement days are.
Now remember, these announcement days here, showing, you know,
kind of the dark bar here, only are 13%
of the total trading days, but most of the term --
return of stock -- and here we have the excess return.
This is in basis point, is occurring during days
where you have this macroeconomic news.
So macroeconomic news comes out,
average excess return is a little
over 10 basis points compared to about 1 basis point
on non-announcement days.
And Savor and Wilson compared this to, you know,
kind of other turn of the month effects.
So, you know, the first day of the month versus, you know,
kind of not the first day and looking
at returns during the month of January, the dark bar
versus not the month of January, the light bar here.
So a pretty big difference in the performance of stocks,
rather there's a macroeconomic news coming out versus not,
you know, kind of the difference here, much larger than,
you know, if we're looking for example at returns in the month
of January versus non-January,
which is also a well-documented result.
So let's look at some
of the empirical results in their paper.
Let's look at macro announcement days,
which they call A days, A for announcement.
What's the average excess return for 50 -- forget looking at 10,
let's look at 50 beta sorted portfolios
over the period 1964 to 2011, okay?
So on our x axis, we have the capital asset pricing model
beta, okay?
And on our y axis, we have the average excess return
in basis points here.
And what do we see?
An interesting distinction.
The diamonds here, these are the returns of these 50 portfolios
from the highest beta
to the lowest beta here on announcement days.
Here, you have the relation --
the highest beta to the lowest beta on non-announcement days.
So on non-announcement days,
the capital asset pricing model isn't, you know,
kind of doing very well.
There isn't really, you know, any relation if anything,
is slightly negative, but you know, I won't make too much
but that's basically no relationship
between beta and the return.
But when you look at the announcement days,
when this macroeconomic news comes out, which should affect,
you know, kind of stocks with systematic risk,
you have a very clear relationship here.
Firms that have higher beta, higher exposure to macro
or systematic risks have bigger changes in their returns.
Those with lower betas have lower returns
on these announcement days, okay?
And they're kind of, you know, very, very,
very clear relationship.
So when we look at the regression here
of daily excess returns on, you know, kind of going back
to just focusing on 10 beta sorted stocks
over the 10 beta sorted portfolios over the period 1964
to 2011, let's recall the capital asset pricing model.
So here, Savor and Wilson are basically doing an analysis
pretty much, you know, similar to those done, you know,
kind of more recently by Fama, Fama and French
where we have 10 beta sorted portfolios, and we're just going
to see if portfolio 10, which has the highest beta, has the --
has higher subsequent returns on average relative to portfolio 1,
for example, which has the lowest betas.
So remember the capital asset pricing model here, okay,
predictions for the regression coefficients,
the intercept term should be zero, okay?
There's no intercept term predicted.
There's no constant as part of this equation,
so that's another way of saying when you do a regression,
the prediction is the intercept term should be zero.
The coefficient on beta, when we're regressing the return
of the beta sorted portfolio on its average beta,
the average beta, the stocks in the portfolio,
the prediction is the coefficient
on this regression should be just whatever the average excess
return of the market was during that period,
which in this case is 1964 to 2011, okay?
And it's useful in terms of, you know,
kind of daily average equity market premium,
10.5 basis points.
So there's a clear prediction when we run this regression
of the excess return of the beta sorted portfolio on its beta.
This -- the coefficient on the beta should be 10.5 basis points
if returns are being predicted
by the capital asset pricing model
in the way the capital asset pricing model says they should.
Okay, let's get to the results.
First, let's put up our regression equation
for the beta sorted portfolios,
we're regressing their excess return, okay, we're doing this,
you know, on a daily rate,
on the average beta of that portfolio.
Prediction is higher beta,
higher should be the return, okay?
Intercept of this regression should be zero,
there's no constant term here.
Coefficient on beta should be this average excess market
return during the period.
They're using daily data
so the average excess market return during this period was
10.5 basis points.
What's the prediction -- or what are the results -- excuse me --
when we have -- just focus on announcement days
so these are days where a macro announcement is occurring.
There's, you know, federal --
U.S. federal government is announcing employment numbers,
is announcing what inflation was, the Federal Reserve,
federal open market committee is announcing a change
in interest rates, so just focus in these announcement dates.
Remember they're about 13% of all trading days.
The intercept in this regression, 1.3 basis points,
but not statistically significant different
from zero, okay?
What's the beta, what's the coefficient on beta?
Usually we talk about [inaudible] when we say beta,
we mean the coefficient, but in this case,
what's the coefficient on beta?
It's 9.2 basis point, 9.2, statistically significant result
so it means a higher beta, higher returns earned
by the portfolio, and the key is this 9.2 estimate is not
statistically different from the capital asset pricing model
prediction at 10.5.
So in other words, on these macroeconomic news days,
you can't reject the hypothesis
that the capital asset pricing model predicts returns
as it says it should, okay?
So capital asset pricing model works, you know,
basically very well in predicting which stocks go
up the most on news when macroeconomic --
news concerning macroeconomic conditions.
Now when we go to the non-announcement days,
the N days, then the capital asset pricing model does not
work well.
And in fact, there's no relationship between the beta
of the portfolio and the return of the portfolio on these non --
no announcement days, these N days.
Coefficient here is, you know,
statistically different from zero.
So another way to think about this,
if you're like trading stocks,
if you think you have some insight about what's going
to happen to, you know, kind of the macro economy,
what the news is going to be, if you think the news is going
to be very good about future economic development,
you want to invest in stocks that have the highest beta
because these regression results suggest the stocks
with the highest beta are going to have the biggest, you know,
kind of movement in returns
when this macroeconomics news is released.
So if you think good news is going to come
out about the future economy, you want to be holding
that day stocks that have the highest beta
because they will have the biggest jump in price according
to this regression analysis.
So in conclusion, from A Tale of Two Days,
days when macroeconomic news is released versus days
where it's not, beta is strongly related to average returns
as a CAPM would predict on the days
when macroeconomic news is released.
Kind of makes sense, right?
Beta is a measure of the systematic risk
or the macroeconomic risk of the stock.
So on the days when this macro news comes,
that's when the CAPM performs pretty well.
But it is a puzzle.
Why no relation between the stock beta and returns
on non-announcement days, okay?
So the authors of this study, A Tale of Two Days,
speculate maybe the announcement days provide a clearer signal
of, you know, systematic risks
and expected future stock market returns to investors, therefore,
you know, by getting this clear signal,
they kind of trade according.
And then that's, you know, kind reflected in the prices.
So bottom line, there is kind of a tale of two days here,
when macroeconomic news is being released,
capital asset pricing model's pretty good
at predicting returns on days outside of that, doesn't seem
to work very well at all.
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