Fundamentals of Corporate Finance
Fourth Edition, Global Edition
Chapter 11
Risk and Return
in Capital Markets
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Chapter Outline
11.1 A First Look at Risk and Return
11.2 Historical Risks and Returns of Stocks
11.3 The Historical Tradeoff Between Risk and Return
11.4 Common Versus Independent Risk
11.5 Diversification in Stock Portfolios
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Learning Objectives
• Identify which types of securities have historically had the
highest returns and which have been the most volatile
• Compute the average return and volatility of returns from a set
of historical asset prices
• Understand the tradeoff between risk and return for large
portfolios versus individual stocks
• Describe the difference between common and independent risk
• Explain how diversified portfolios remove independent risk,
leaving common risk as the only risk requiring a risk premium
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11.1 A First Look at Risk and Return
• Consider how an investment would have grown if it
were invested in each of the following from the end of
1925 until the end of 2015:
–
–
–
–
–
Standard & Poor’s 500 (S&P 500)
Small Stocks
World Portfolio
Corporate Bonds
Treasury Bills
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Figure 11.1 Value of $100 Invested at the End of
1925 in U.S. Large Stocks (S&P 500), Small
Stocks, World Stocks, Corporate Bonds, and
Treasury Bills
Source: Global Financial Data and CRSP.
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Realized Returns, in Percent (%) for Small
Stocks, the S&P 500, Corporate Bonds, and
Treasury Bills, Year−End 1925–1935
Table 11.1 Realized Returns, in Percent (%) for Small Stocks, the S&P 500, Corporate
Bonds, and Treasury Bills, Year−End 1925–1935
Year
Small Stocks
S&P 500
Corp Bonds
Treasury Bills
1926
−7.20
11.14
6.29
3.19
1927
25.75
37.13
6.55
3.12
1928
46.87
43.31
3.38
3.82
1929
−50.47
−8.91
4.32
4.74
1930
−45.58
−25.26
6.34
2.35
1931
−50.22
−43.86
−2.38
1.02
1932
8.70
−8.86
12.20
0.81
1933
187.20
52.89
5.26
0.29
1934
25.21
−2.34
9.73
0.15
1935
64.74
47.21
6.86
0.17
Source: Global Financial Data.
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11.2 Historical Risks and Returns of
Stocks (1 of 5)
• Computing Historical Returns
– Realized Returns
– Individual Investment Realized Returns
 The realized return from your investment in the stock
from t to t + 1 is:
Div t 1  Pt 1  Pt Div t 1 Pt 1  Pt
Rt 1 


Pt
Pt
Pt
 Dividend Yield  Capital Gain Yield
(Eq. 11.1)
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Example 11.1 Realized Return (1 of 4)
Problem:
• Microsoft paid a one-time special dividend of $3.08 on
November 15, 2004. Suppose you bought Microsoft stock
for $28.08 on November 1, 2004, and sold it immediately
after the dividend was paid for $27.39.
• What was your realized return from holding the stock?
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Example 11.1 Realized Return (2 of 4)
Solution:
Plan:
• We can use Equation 11.1 to calculate the realized return.
We need the purchase price ($28.08), the selling price
($27.39), and the dividend ($3.08) and we are ready to
proceed.
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Example 11.1 Realized Return (3 of 4)
Execute:
• Using Equation 11.1, the return from Nov 1, 2004, until
Nov 15, 2004, is equal to:
Rt 1 
Div t 1  Pt 1  Pt 3.08  27.39  28.08

 0.0851,or 8.51%
Pt
28.08
• This 8.51% can be broken down into the dividend yield and
the capital gain yield:
Dividend Yield 
Div t 1
3.08

 0.1097,or10.97%
Pt
28.08
CapitalGain Yield 
Pt 1  Pt 27.39  28.08

 0.0246,or  2.46%
Pt
28.08
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Example 11.1 Realized Return (4 of 4)
Evaluate:
• These returns include both the capital gain (or in this case
a capital loss) and the return generated from receiving
dividends. Both dividends and capital gains contribute to
the total realized return—ignoring either one would give a
very misleading impression of Microsoft’s performance.
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Example 11.1a Realized Return (1 of 4)
Problem:
• Elite Health Management paid a one−time special dividend
of $10.00 on March 2, 2017. Suppose you bought Elite
Health Management stock for $20.33 on February 15,
2017 and sold it immediately after the dividend was paid
for $10.29.
• What was your realized return from holding the stock?
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Example 11.1a Realized Return (2 of 4)
Solution:
Plan:
• We can use Equation 11.1 to calculate the realized return.
We need the purchase price ($20.33), the selling price
($10.29), and the dividend ($10.00) and we are ready to
proceed.
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Example 11.1a Realized Return (3 of 4)
Execute:
• Using Equation 11.1, the return from February 15, 2017
until March 2, 2017 is equal to
Rt 1 
Div t 1  Pt 1  Pt 10.00  10.29  20.33

 0.002,or  0.2%
Pt
20.33
• This −0.2% can be broken down into the dividend yield
and the capital gain yield:
Dividend Yield 
Div t 1 10.00

 0.4919,or 49.19%
Pt
20.33
CapitalGain Yield 
Pt 1  Pt 10.29  20.33

 0.4939,or  49.39%
Pt
20.33
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Example 11.1a Realized Return (4 of 4)
Evaluate:
• These returns include both the capital gain (or in this case
a capital loss) and the return generated from receiving
dividends. Both dividends and capital gains contribute to
the total realized return—ignoring either one would give a
very misleading impression of Elite Health Management’s
performance.
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11.2 Historical Risks and Returns of
Stocks (2 of 5)
• Computing Historical Returns
– Individual Investment Realized Returns
 For quarterly returns (or any four compounding
periods that make up an entire year) the annual
realized return, Rannual, is found by compounding:
1  Rannual  (1  R1 )(1  R2 )(1  R3 )(1  R4 )
(Eq. 11.2)
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Example 11.2 Compounding Realized
Returns (1 of 7)
Problem:
• Suppose you purchased Microsoft stock (MSFT) on Nov 1,
2004, and held it for one year, selling on Oct 31, 2005.
• What was your annual realized return?
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Example 11.2 Compounding Realized
Returns (2 of 7)
Solution:
Plan:
• We need to analyze the cash flows from holding MSFT
stock for each quarter. In order to obtain the cash flows,
we must look up MSFT’s stock price data at the purchase
date and selling date, as well as at any dividend dates (see
Chapter 7 and the book’s Web site for online sources of
stock price and dividend data).
• From the data, we can construct the following table to fill
out our cash flow timeline:
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Example 11.2 Compounding Realized
Returns (3 of 7)
Plan:
Date
Nov 01 04
Nov 15 04
Feb 15 05
May 16 05
Aug 15 05
Oct 31 05
Price
28.08
27.39
25.93
25.49
27.13
25.70
Dividend
Blank
3.08
0.08
0.08
0.08
Blank
• Next, compute the realized return between each set of
dates using Equation 11.1. Then determine the annual
realized return similarly to Equation 11.2 by compounding
the returns for all of the periods in the year.
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Example 11.2 Compounding Realized
Returns (4 of 7)
Execute:
• In Example 11.1, we already computed the realized return
for November 1, 2004, to November 15, 2004, as 8.51%.
We continue as in that example, using Equation 11.1 for
each period until we have a series of realized returns. For
example, from November 15, 2004, to February 15, 2005,
the realized return is:
Rt 1 
Div t 1  Pt 1  Pt 0.08  25.93  27.39

 0.0504,or  5.04%
Pt
27.39
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Example 11.2 Compounding Realized
Returns (5 of 7)
Execute:
• The following table includes the realized return in each
period
Date
Nov 01 04
Nov 15 04
Feb 15 05
May 16 05
Aug 15 05
Oct 31 05
Price
28.08
27.39
25.93
25.49
27.13
25.70
Dividend
Blank
3.08
0.08
0.08
0.08
Blank
Return
Blank
8.51%
−5.04%
−1.39%
6.75%
−5.27%
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Example 11.2 Compounding Realized
Returns (6 of 7)
Execute:
• We then determine the one-year return by compounding.
• Note that to use the method in Equation 11.2, we must have an
investment to compound, so just as in Chapter 3, when we
compounded interest, we add 1 as if we are computing the outcome
of investing $1. The first return is 8.51%, giving us 1 + .0851 or
1.0851. The same is true when the return is negative: The second
return is −5.04%, giving us 1 + 1 − .05042 or 0.9496. To compute the
annual realized return, we simply subtract the initial $1, leaving only
the return:
1  Rannual  (1  R1 )(1  R2 ) 1  R3  (1  R4 )(1  R5 )
1  Rannual  11.08512 10.94962 10.98612 11.06752 10.94732 = 1.0275
Rannual  1.0275 - 1 = 0.0275 or 2.75%
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Example 11.2 Compounding Realized
Returns (7 of 7)
Evaluate:
• By repeating these steps, we have successfully computed
the realized returns for an investor holding MSFT stock
over this one-year period. From this exercise we can see
that returns are risky. MSFT fluctuated up and down over
the year and ended only slightly up (2.75%) at the end.
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Example 11.2a Compounding Realized
Returns (1 of 7)
Problem:
• Suppose you purchased Elite Health Management’s stock
on March 16, 2016 and held it for one year, selling on
March 15, 2017.
• What was your realized return?
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Example 11.2a Compounding Realized
Returns (2 of 7)
Solution:
Plan:
• We need to analyze the cash flows from holding HMA
stock for each period. In order to get the cash flows, we
must look up Elite Health Management stock price data at
the start and end of both years, as well as at any dividend
dates.
• From the data we can construct the following table to fill
out our cash flow timeline:
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Example 11.2a Compounding Realized
Returns (3 of 7)
Plan:
Date
16−Mar−16
10−May−16
9−Aug−16
8−Nov−16
15−Feb−16
2−Mar−17
15−Mar−17
Price
21.15
20.70
20.62
19.39
20.33
10.29
11.07
Dividend
Blank
0.06
0.06
0.06
Blank
10.00
Blank
• Next, compute the realized return between each set of
dates using Equation 11.1. Then determine the annual
realized return similarly to Equation 11.2 by compounding
the returns for all of the periods in the year.
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Example 11.2a Compounding Realized
Returns (4 of 7)
Execute:
• In Example 11.1a, we already computed the realized return
for February 15, 2017 to March 2, 2017 as −.2%. We
continue as in that example, using Equation 11.1 for each
period until we have a series of realized returns. For
example, from August 9, 2016 to November 8, 2016, the
realized return is
Rt 1 
Div t 1  Pt 1  Pt 0.06  19.39  20.62

 0.0567,or  5.67%
Pt
20.62
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Example 11.2a Compounding Realized
Returns (5 of 7)
Execute:
• The table below includes the realized return at each period.
Date
Price
Dividend
Return
16−Mar−16
21.15
Blank
Blank
10−May−16
20.70
0.06
−1.84%
9−Aug−16
20.62
0.06
−0.10%
8−Nov−16
19.39
0.06
−5.67%
15−Feb−17
20.33
Blank
4.85%
2−Mar−17
10.29
10.00
−0.20%
15−Mar−17
11.07
Blank
7.58%
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Example 11.2a Compounding Realized
Returns (6 of 7)
Execute:
• We then determine the one−year return by compounding.
1  Rannual  (1  R1 )(1  R2 ) 1  R3  (1  R4 )(1  R5 )(1  R6 )
1  Rannual  (0.982)(0.999)  0.943  (1.048)(0.998)(1.076)
Rannual  1.0411  1  .0411or 4.11%
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Example 11.2a Compounding Realized
Returns (7 of 7)
Evaluate:
• By repeating these steps, we have successfully computed
the realized annual returns for an investor holding Elite
Health Management stock over this one−year period. From
this exercise we can see that returns are risky. Elite Health
Management fluctuated up and down over the year and
yielded a return of only 4.11% at the end.
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11.2 Historical Risks and Returns of
Stocks (3 of 5)
• Average Annual Returns
– Average Annual Return of a Security
1
R  (R1  R2  ...  RT )
(Eq. 11.3)
T
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Figure 11.2 The Distribution of Annual Returns
for U.S. Large Company Stocks (S&P 500), Small
Stocks, Corporate Bonds, and Treasury Bills,
1926–2015
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Figure 11.3 Average Annual Returns in the U.S.
for Small Stocks, Large Stocks (S&P 500),
Corporate Bonds, and Treasury Bills, 1926–2015
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11.2 Historical Risks and Returns of
Stocks (4 of 5)
• The Variance and Volatility of Returns:
– Variance

1
Var  R  
(R1  R )2  (R2  R )2  ...  (RT  R )2
T 1

(Eq. 11.4)
‒ Standard Deviation
SD(R )  Var  R 
(Eq. 11.5)
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Example 11.3 Computing Historical
Volatility (1 of 6)
Problem:
• Using the following data, what is the standard deviation of
the S&P 500’s returns for the years 2005–2009?
2005
15.8%
2006
5.5%
2007
−37.0%
2008
10.75%
2009
26.5%
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Example 11.3 Computing Historical
Volatility (2 of 6)
Solution:
Plan:
• With the five returns, compute the average return using
Equation 11.3 because it is an input to the variance
equation. Next, compute the variance using Equation 11.4
and then take its square root to determine the standard
deviation, as shown in Equation 11.5.
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Example 11.3 Computing Historical
Volatility (3 of 6)
Execute:
• In the previous section we computed the average annual
return of the S&P 500 during this period as 3.1%, so we
have all of the necessary inputs for the variance
calculation:
• Applying Equation 11.4, we have:
Var (R ) 

1
(R1  R )2  (R2  R )2  ...  (RT  R )2 
T 1
(0.049  0.031)2  (0.158  0.031)2  (0.055  0.031)2   0.370  0.0312   0.265  0.0312 

5 1
1
 0.058
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Example 11.3 Computing Historical
Volatility (4 of 6)
Execute:
• Alternatively, we can break out the calculation of this
equation as follows:
Blank
2005
2006
2007
2008
2009
Return
0.049
0.158
0.055
−0.370
0.265
Average
0.031
0.031
0.031
0.031
0.031
Difference
0.018
0.127
0.024
−0.401
0.235
Squared
0.000
0.016
0.001
0.161
0.055
• Summing the squared differences in the last row, we get
0.233.
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Example 11.3 Computing Historical
Volatility (5 of 6)
Execute:
• Finally, dividing by (5 − 1 = 4) gives us
0.233
 0.058
4
• The standard deviation is therefore:
SD(R)  Var (R)  0.058  0.241,or 24.1%
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Example 11.3 Computing Historical
Volatility (6 of 6)
Evaluate:
• Our best estimate of the expected return for the S&P 500
is its average return, 3.1%, but it is risky, with a standard
deviation of 24.1%.
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Example 11.3a Computing Historical
Volatility (1 of 6)
Problem:
• Using the data below, what is the standard deviation of
10−year Treasury bond’s returns for the years 2011−2015?
Year
10−Yr T−Bond’s Return
2011
2012
16.04%
2.97%
2013
−9.10%
2014
10.75%
2015
1.28%
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Example 11.3a Computing Historical
Volatility (2 of 6)
Solution:
Plan:
Year
10−Yr T−Bond’s Return
2011
16.04%
2012
2.97%
2013
2014
−9.10%
10.75%
2015
1.28%
• With the five returns, compute the average return using
Equation 11.3 because it is an input to the variance
equation. Next, compute the variance using Equation 11.4
and then take its square root to determine the standard
deviation, as shown in Equation 11.5.
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Example 11.3a Computing Historical
Volatility (3 of 6)
Execute:
• Using Equation 11.3, the average annual return for 10−year Treasury
bonds during this period is:
R  (0.1604  0.0297  0.0910  0.1075+0.128)
1
 0.04388
5
• We now have all of the necessary inputs for the variance calculation:
• Applying Equation. 11.4, we have:
Var (R ) 

1
(R1  R )2  (R2  R )2  ...  (RT  R )2 
T 1
(0.1604  0.04388)2  (0.0297  0.04388)2  ( 0.0910  0.04388)2   0.1075  0.04388 2   0.0128  0.04388 2 

5 1
1
 0.00925
SD(R )
 0.00925  0.0962  9.62%
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Example 11.3a Computing Historical
Volatility (4 of 6)
Execute:
• Alternatively, we can break the calculation of this equation
out as follows:
Blank
2011
2012
2013
2014
2015
Return
0.1604
0.0297
−0.091
0.1075
0.0128
Average
0.04388
0.04388
0.04388
0.04388
0.04388
Difference
0.11652
−0.01418
−0.13488
0.06362
−0.03108
Squared
0.0136
0.0002
0.0182
0.0040
0.0010
• Summing the squared differences in the last row, we get
0.0370.
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Example 11.3a Computing Historical
Volatility (5 of 6)
Execute:
• Finally, dividing by (5 − 1 = 4) gives us
0.0370
 0.00925
4
• The standard deviation is therefore:
SD(R)  Var (R)  0.000925  0.0962,or 9.62%
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Example 11.3a Computing Historical
Volatility (6 of 6)
Evaluate:
• Our best estimate of the expected return for 10−year
Treasury Bonds is the average return, 4.39%, with a
standard deviation of 9.62%.
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Figure 11.4 Volatility (Standard Deviation) of U.S.
Small Stocks, Large Stocks (S&P 500), Corporate
Bonds, and Treasury Bills, 1926–2015
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11.2 Historical Risks and Returns of
Stocks (5 of 5)
• The Normal Distribution
– About two−thirds of all possible outcomes fall within
one standard deviation above or below the average
– 95% of all possible outcomes fall with two standard
deviations above or below the average
 95% Prediction Interval
Average  (2  standard deviation)
R  (2  SD  R )
(Eq. 11.6)
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Figure 11.5 Normal Distribution
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Example 11.4 Confidence Intervals (1 of 4)
Problem:
• In Example 11.3, we found the average return for the S&P
500 from 2005 to 2009 to be 3.1% with a standard
deviation of 24.1%. What is a 95% prediction interval for
2010’s return?
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Example 11.4 Confidence Intervals (2 of 4)
Solution:
Plan:
• We can use Equation 11.6 to compute the prediction
interval.
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Example 11.4 Confidence Intervals (3 of 4)
Execute:
• Using Equation 11.6, we have:
Average  2  standarddeviation  3.1%  (2  24.1%) to 3.1%  (2  24.1%)
 45.1% to 51.3%
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Example 11.4 Confidence Intervals (4 of 4)
Evaluate:
• Even though the average return from 2005 to 2009 was
3.1%, the S&P 500 was volatile, so if we want to be 95%
confident of 2010’s return, the best we can say is that it will
lie between −45.1% and +51.3%.
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Example 11.4a Confidence
Intervals (1 of 4)
Problem:
• The average return for 10−year Treasury bonds from
2011−2015 was 4.39% with a standard deviation of 9.62%.
What is a 95% confidence interval for 2016’s return?
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Example 11.4a Confidence
Intervals (2 of 4)
Solution:
Plan:
• We can use Equation 11.6 to calculate the confidence
interval.
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Example 11.4a Confidence
Intervals (3 of 4)
Execute:
• Using Equation 11.6, we have:
Average  2  standarddeviation  4.39%  (2  9.62%) to 4.39%  (2  9.62%)
 14.85% to 23.63%
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Example 11.4a Confidence
Intervals (4 of 4)
Evaluate:
• Even though the average return from 2011−2015 was
4.39%, 10−year T−Bonds were still volatile, so if we want
to be 95% confident of 2016’s return, the best we can say
is that it will lie between −14.85% and +23.63%.
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Summary of Tools for Working with
Historical Returns
Table 11.2 Summary of Tools for Working with Historical Returns
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11.3 Historical Tradeoff between Risk
and Return (1 of 2)
• The Returns of Large Portfolios
– Investments with higher volatility, as measured by
standard deviation, tend to have higher average
returns
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Figure 11.6 The Historical Tradeoff Between Risk
and Return in Large Portfolios, 1926–2014
Source: CRSP, Morgan Stanley Capital International.
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11.3 Historical Tradeoff between Risk
and Return (2 of 2)
• The Returns of Individual Stocks
– Larger stocks have lower volatility overall
– Even the largest stocks are typically more volatile than
a portfolio of large stocks
– The standard deviation of an individual security doesn’t
explain the size of its average return
– All individual stocks have lower returns and/or higher
risk than the portfolios in Figure 11.6
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Figure 11.7 Historical Volatility and Return for 500
Individual Stocks, Ranked Annually by Size
Source: CRSP
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11.4 Common Versus Independent
Risk
• Theft Versus Earthquake Insurance
• Types of Risk
– Common Risk
– Independent Risk
– Diversification
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Summary of Types of Risk
Table 11.3 Summary of Types of Risk
Type of Risk
Definition
Example
Risk Diversified
in Large Portfolio?
Common Risk
Linked across
outcomes
Risk of
earthquake
No
Independent Risk
Risks that bear no
relation to each
other
Risk of theft
Yes
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Example 11.5 Diversification (1 of 5)
Problem:
• You are playing a very simple gambling game with your
friend: a $1 bet based on a coin flip.
• That is, you each bet $1 and flip a coin: heads you win
your friend’s $1, tails you lose and your friend takes your
dollar.
• How is your risk different if you play this game 100 times
versus just betting $100 (instead of $1) on a single coin
flip?
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Example 11.5 Diversification (2 of 5)
Solution:
Plan:
• The risk of losing one coin flip is independent of the risk of
losing the next one: Each time you have a 50% chance of
losing, and one coin flip does not affect any other coin flip.
• We can compute the expected outcome of any flip as a
weighted average by weighting your possible winnings (
+$1) by 50% and your possible losses (−$1) by 50%. We
can then compute the probability of losing all $100 under
either scenario.
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Example 11.5 Diversification (3 of 5)
Execute:
• If you play the game 100 times, you should lose 50 times
and win 50 times, so your expected outcome is 50 × (+$1)
+ 50 × (−$1) = $0.
• You should break even. But even if you don’t win exactly
half of the time, the probability that you would lose all 100
coin flips (and thus lose $100) is exceedingly small (in fact,
it is 0.50100, which is far less than even 0.0001%).
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Example 11.5 Diversification (4 of 5)
Execute:
• If it happens, you should take a very careful look at the
coin!
• If instead you make a single $100 bet on the outcome of
one coin flip, you have a 50% chance of winning $100 and
a 50% chance of losing $100, so your expected outcome
will be the same: break-even.
• However, there is a 50% chance you will lose $100, so
your risk is far greater than it would be for 100 one-dollar
bets.
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Example 11.5 Diversification (5 of 5)
Evaluate:
• In each case, you put $100 at risk, but by spreading out
that risk across 100 different bets, you have diversified
much of your risk away, compared to placing a single $100
bet.
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Example 11.5a Diversification (1 of 5)
Problem:
• You are playing a very simple gambling game with your
friend: a $1 bet based on a roll of two six−sided dice.
• That is, you each bet $1 and roll the dice: if the outcome is
even you win your friend’s $1, if the outcome is odd you
lose and your friend takes your dollar.
• How is your risk different if you play this game 100 times in
a row versus just betting $100 (instead of $1) on a roll of
the dice?
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Example 11.5a Diversification (2 of 5)
Solution:
Plan:
• The risk of losing one dice roll is independent of the risk of
losing the next one: each time you have a 50% chance of
losing, and one roll of the dice does not affect any other
roll.
• We can compute the expected outcome of any roll as a
weighted average by weighting your possible winnings
(+$1) by 50% and your possible losses (−$1) by 50%. We
can then compute the probability of losing all $100 under
either scenario.
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Example 11.5a Diversification (3 of 5)
Execute:
• If you play the game 100 times, you should lose about
50% of the time and win 50% of the time.
• The expected outcome is 50  (+$1) + 50  (−$1) = $0.
You should break−even.
• Even if you don’t win exactly half of the time, the
probability that you would lose all 100 dice rolls (and thus
lose $100) is exceedingly small (in fact, it is 0.50100, which
is far less than even 0.0001%).
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Example 11.5a Diversification (4 of 5)
Execute:
• If instead, you make a single $100 bet on the outcome of
one roll of the dice, you have a 50% chance of winning
$100 and a 50% chance of losing $100, so your expected
outcome will be the same: break−even. However, there is
a 50% chance you will lose $100, so your risk is far greater
than it would be for 100 one dollar bets.
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Example 11.5a Diversification (5 of 5)
Evaluate:
• In each case, you put $100 at risk, but by spreading−out
that risk across 100 different bets, you have diversified
much of your risk away compared to placing a single $100
bet.
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11.5 Diversification in Stock
Portfolios (1 of 4)
• Unsystematic Versus Systematic Risk
– Stock prices are impacted by two types of news:
1. Company or Industry−Specific News
2. Market−Wide News
– Unsystematic Risk
– Systematic Risk
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Figure 11.7 Volatility of Portfolios of
Type S and U Stocks
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Figure 11.8 The Effect of
Diversification on Portfolio Volatility
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11.5 Diversification in Stock
Portfolios (2 of 4)
• Diversifiable Risk and the Risk Premium
– The risk premium for diversifiable risk is zero
 Investors are not compensated for holding unsystematic
risk
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Systematic Risk Versus Unsystematic
Risk
Table 11.4 Systematic Risk Versus Unsystematic Risk
Blank
Diversifiable?
Requires a Risk Premium?
Systematic Risk
No
Yes
Unsystematic Risk
Yes
No
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11.5 Diversification in Stock
Portfolios (3 of 4)
• The Importance of Systematic Risk
– The risk premium of a security is determined by its
systematic risk and does not depend on its diversifiable
risk
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11.5 Diversification in Stock
Portfolios (4 of 4)
• The Importance of Systematic Risk
– There is no relationship between volatility and average
returns for individual securities
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The Expected Return of Type S and Type U Firms,
Assuming the Risk−Free Rate Is 5%
Table 11.5 The Expected Return of Type S and Type U Firms,
Assuming the Risk−Free Rate Is 5%
Blank
S Firm
U Firm
Volatility (standard deviation)
30%
30%
Risk-Free Rate
5%
5%
Risk Premium
5%
0%
Expected Return
10%
5%
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Chapter Quiz
1. Historically, which types of investments have had the
highest average returns and which have been the most
volatile from year to year? Is there a relation?
2. For what purpose do we use the average and standard
deviation of historical stock returns?
3. What is the relation between risk and return for large
portfolios? How are individual stocks different?
4. What is the difference between common and independent
risk?
5. Does systematic or unsystematic risk require a risk
premium? Why?
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