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.
Market Anomalies: Small-Firm and Value Effects
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Del curso dictado por University of Illinois at Urbana-Champaign
Investments I: Fundamentals of Performance Evaluation
399 calificaciones
University of Illinois at Urbana-Champaign
399 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 the capital asset-pricing model doesn't perform as well
as we would like, although it does seem to perform pretty well
on the days when macro economic news is released.
But there's another problem if we want to kind
of honestly evaluate the performance
of the capital asset-pricing model.
It not only has a prediction as to what should effect returns,
data should predict returns in the capital asset-pricing model,
but it also has predictions
about what should not predict returns,
and that's basically nothing else should predict returns
except for beta.
So for example, no firm characteristic other then beta
should be able to predict stock returns.
Looking at the data, some return anomalies have popped up.
Now, if these are reflecting some type of risk,
some type of multifaceted risk beyond the systematic risk
that the CAPM emphasizes,
then we probably shouldn't be calling these anomalies.
These are higher returns because they represent risk.
But for now, let's just kind
of call these market return anomalies.
One of these is small firm effect, stocks of small firms
and by small firm, we need a market value equity
of the stock has a low ranking.
Small firms do better than large firms historically,
higher returns particularly in the month of January.
Book-to-market effect or value effect.
Stocks with high book-to-market ratios--
remember this is a book value of equity that you see
in a balance sheet in the annual report of the firm divided
by the market value of the equity.
Those type of firms we call them value firms historically have
earned higher returns than growth firms,
firms that have low book-to-market ratios.
So for the growth company, think of a firm where there's
like a great idea, but there aren't many tangible assets
in place.
Maybe a computer server and that's it.
Valley stocks doing better than growth stocks,
book-to-market effect.
Momentum. This is another return anomaly
or predictable pattern in return.
Instead of calling these market anomalies,
we could also call them predictable pattern in returns
that have been found in the data, size, value and momentum.
What's a momentum effect?
Stocks that have done very well during the last year continue
to drift up over the next month.
Stocks have done very poorly over the last year continue
to drift down over the next month.
So for those of you who are taking this course
for high engagement experience, you want to get credit
through the University of Illinois for the course,
you're going to have more experience with momentum.
We'll do some back testing of the momentum strategy
in the AQR momentum funds case study.
So let's talk a little bit about the small firm effect.
So this is data again from the Ken French Data Library
where were rank forms and deciles based on the size
of the market value of the equity.
So decile 10 is the largest firms.
So think like currently in 2015,
Apple would be in this group here.
Decile 1 are the smallest firms here, and then kind
of all those in between.
Just looking here at the average annual stock returns,
1927 to 2014, there's a clear pattern.
Bigger firms have smaller returns.
Difference here of almost 8 percentage points
from the smallest stocks to the largest stocks here.
Now, one explanation going back to this difference in returns,
how much of this is just due to smaller stocks being riskier
in a capital asset-pricing model sense?
Maybe they have higher sensitivity to the economy,
more likely to fail in bad times so to compensate for this kind
of kicking you while you're down effect,
they have to give a higher return on average
because investors realize if we go into a recession,
these small CAP stocks are really going
to hurt their portfolio performance.
So, you can do an analysis,
estimate what is the capital asset-pricing model beta
for each of these portfolios?
Again, using this annual data, 1927-2014,
and you do see the small stocks are riskier
in that they have higher beta.
So again, there's kind of a clear relationship.
Smaller stocks, higher beta in the portfolio.
For the smallest stocks, their beta is a little over 1.5,
so they amplify movements in the broader market.
For the biggest stocks,
there are betas actually a little less than one.
Now remember, when we look at the whole U.S. stock market,
that's value weighted so when we look
at the whole U.S. stock market, it's going to behave much more
like the firms in deciles 10 and 9 because they're bigger
so they have more weight in the overall portfolio than the firms
in deciles 1 and 2, which are smaller
so then have a much smaller weight in the market portfolio.
So let's look at the small firm effect without
and then with risk adjustment.
Without risk adjustment--
and here instead of looking at the total return, we're looking
at the return in excess of treasury bills
so a simple excess return with no risk adjustment,
again we see there's about an 8 percentage point difference.
The returns of small firms being about 8 percentage points higher
than the return of the largest firms.
But once we take into account differences and risk,
look at the alpha from the capital asset-pricing model,
we see small stocks outperform by about the CAPM benchmark
by about 2.6 percentage points per year while for large stocks,
they basically are hitting right on their cap and benchmark.
So of this roughly 8 percentage points difference in return,
only about 2.7 of that is explained by the alpha
of the small, small stocks.
So the rest of that then obviously explained
by difference in risk.
So kind of summing up, small stocks over 1927 to 2014--
small stocks, the bottom decile ranked
by size have outperformed big stocks the top decile rank
by size on the order of about 8 percentage points per year.
You might expect smaller stocks to be more sensitive
to the economy, more likely to fail in a downturn.
That shows up in the CAPM beta.
For the small stocks, the beta is 1.54.
For the largest stocks, it's 0.93.
So there is a pretty big difference in risk.
So you put all this information together
when we examine the capital asset-pricing model and we look
at performance above and beyond the benchmark,
the difference is only 2.7 percentage point once we adjust
for risk.
So of that difference of about 8 percentage points
and the performance of small stocks relative to large stocks,
about 2/3 of that is accounted for the difference
in the underlying risk of the two types of firms,
smaller firms riskier on average
than higher firms so higher returns.
But there is some of that that remains,
1/3 that just represents kind of the alpha or the outperformance,
the performance above and beyond the benchmark of the small firms
over this long time period, 1927-2014.
Nothing stays a secret for long.
Such a difference, 8 percentage points per year.
People are going to talk about it and write about it,
and there were academic articles documenting the size effect back
in 1981.
So I know there's a bunch of insert joke kind
of moments here coming up in the video.
What if we break the sample into two parts kind
of pre the 1981 article and then post the 1981 article
on the size effects?
Does the size effect persist?
Did the academic papers kind of contribute to diminishing
of the size effect, post 1981?
So, size doesn't matter as much as it used to.
I make it easy for you guys who are kind
of joking along at home here.
So let's look at decile 1 versus decile 10.
So this is looking
at the smallest firms performance minus the
performance of the largest firms when they focus in terms
of decile or we can look at the small minus big factor SMB.
This is basically looking
at the firms whose size is below median versus those above.
So two differentials reflecting size.
Look at the dark bar here on the left represents the difference
across all the years between small and big firms
and again we have these two measures of small and big.
The middle bar here kind
of medium tone is the result [inaudible] years before 1981
and the final bar here is actually representing then the
performance of small relative to big stocks
after 1981 going to 2014.
And I forgot to mention these returns we're looking
at here are already risk adjusted.
These are alphas from the capital asset-pricing model.
So if you look at-- let's just focus here
on the left side decile 1 through 10.
The smallest stocks minus the biggest stocks.
We have this alpha.
Remember, we documented this before of 2.7 percent.
This was over the full sample period.
Go to prior 1981, 1927 to 1980,
this difference was 5.3 percentage points per year.
Since then, it's gone away to zero.
So size doesn't matter like it used to.
If you use this alternative size breakdown, so here it's not
as disparate the firms, really focusing
on small being below median, big being above median.
Again, you still have the same pattern that most
of this differential in the performance of small
versus big firms occurs during the first part
of the sample before 1981
after the academic papers are published documenting,
hey small firms are doing historically very well,
the small firm effect goes away, which is consistent with a bunch
of people going out buying the stocks of small firms.
That drives up their price, but that means
and subsequently lower returns in the future
and this picture here definitely is consistent with that story.
Okay, so now we talked about the small firm effect.
Let's talk about the famous value premium.
If you're talking about the value premium,
you certainly have to bring up Warren Buffett here,
kind of very famous value investor.
So, a good place to look for this kind of documentation
of the value effect is work by Eugene Fama and Ken French.
So remember that chart I had
where they did an updated analysis of the performance
of the capital asset-pricing model?
Well, this chart is a little different.
Here, they're looking at the performance of ten portfolios
and within the portfolio they kind of value weight
by the market capitalization of the stocks,
but the portfolios are formed based on book to market ratio.
So ten portfolios.
Portfolio 10 has the firms with the highest book-to-market ratio
so these are the cement companies, the companies
that have a lot of tangible assets, the ratio book value
to market value is high.
They're right up here.
Portfolio one are the growth companies,
the lowest book-to-market ratios.
So these are the companies that have a great idea
so that's why you have a market value.
People are willing to pay something to own their stock,
but there currently aren't hardly any tangible assets
in place that show up on the balance sheet like cash,
property and equipment.
Those would be in portfolio one.
Now, what was kind of key observation from this analysis
for each of these portfolios Fama and French had a prediction
from the capital asset-pricing model
about what the return should be, and they found
that when you look at these portfolios,
the value portfolios, those that have the biggest value tilt
like portfolio 10, portfolio 9, portfolio 8,
they outperform their CAPM benchmark
by the greatest amount.
For those that are more growth like for example, portfolio one,
it underperforms its capital asset-pricing benchmark.
It has a negative alpha.
Therefore, value stocks are outperforming growth stocks.
Those with the highest book
to market ratio have the biggest alphas here relative
to the CAPM benchmark 10, 9, 8 and 7.
They're all earning returns historically much higher
than would be predicted by the CAPM, which is given
by this line here while portfolio one,
those of the growth companies,
it's actually underperforming its capital asset-pricing value.
So value strategies go by several different names here.
If you want to invest in value stocks,
high book-to-market firms, low price-to-earnings firms,
each of these are value stocks.
Firms are paying high dividends would also be a value strategy.
Just to throw some names out there, Warren Buffett,
before him, Ben Graham, to throw out a University
of Illinois connection, [inaudible] all
of these are well known value investors.
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