Introduce the market risk and the firm-specific risk
Portfolio Theory:
– Diversification in the case of two risky assets without or with the risk-free asset
– Extension to the multiple risky-asset case
Introduce the single-factor model
– It is a statistical model
– To identify the components of firm-specific and market risks
– The Treynor-Black model to construct portfolios with higher Sharpe ratios
– To examine the CAPM (discussed in Ch7)
6-2

6.1 DIVERSIFICATION AND
PORTFOLIO RISK
6-3

Firm-specific risk ( 公司特定風險 )
– Risk factors that affect an individual firm without noticeably affecting other firms, like the results of
R&D, the management style, etc.
– Due to the possible offset of the firm-specific risks from different firms in a portfolio, this risk can be eliminated through diversification
– Also called unique, idiosyncratic, diversifiable, or nonsystematic risk
Market risk ( 市場風險 )
– Risk factors common to the whole economy, possibly from business cycles, inflation rates, interest rates, exchange rates, etc.
– The risk cannot be eliminated through diversification
– Also called nondiversifiable or systematic risk
6-4

Firm-specific risk
Market risk
※ For one single stock, the average total risk can be measured as the standard deviation of 50%. The market and firm-specific risks represent about 40% and
60% of the total risk, respectively
※ The extremely diversified portfolio will be the whole market portfolio traded on
NYSE
※ International diversification may further reduce the portfolio risk, but global risk factors affecting all countries will limit the extent of risk reduction
6-5

6.2 ASSET ALLOCATION WITH
TWO RISKY ASSETS
6-6

The portfolio theory was first introduced by
Harry Markowitz in 1952, who is a Nobel
Prize laureate in 1990
Sections 6.2 to 6.4 will introduce the results of Markowitz’s work of showing how to make the most of the power of diversification
In this section, the effect of diversification is illustrated by a case of two risky assets
6-7

Here we assumed that the risky portfolio comprised a stock and a bond fund, and investors need to decide the weight of each fund in their portfolios r p
 w r
B B
 w r
S S
(portfolio return) w
B
: weight of the bond fund r
B
: return of the bond fund w
S
: weight of the stock fund r
S
: return of the stock fund
6-8

Mean (Expected Return):
E r i
 k s 

1 p k r k i
Variance: i S B r i
  i
2  k s 

1
( )
 i
( )

E r i

2 i S B
※ A property for variance var( w r i i
)
 k s 

1
( )
 i i
( )
 i
( ) i

2
 w i
2 k s 

1
( )
 i
( )

( ) i

2  w
2 r i var( ) i
6-9

Covariance between r
S and r
B
: a measure of co-varying behavior of returns on two assets r r
S B
)
 k s 

1
( )

S
( )

( )
S

B
( )

( )
B

※ Three properties for covariance r r
S B
 r r
B S r r
S S
 k s 

1

S

E r
S

2  r
S cov( w r w r
S S
,
B B
)
 k s 

1
( )

S S
( )

S
( )
S

B B
( )

B
( )
B

 w w
S B k s 

1
( )

S
( )

( )
S

B
( )

( )
B

 w w
S B r r
S B
)
6-10

※ Another expression for covariance r r
S B
 k s 

1
( )

S
( )

( )
S

B
( )

( )
B

 k s 

1
( ) ( ) ( )
S B
 k s 

1
( ) ( ) ( )
S B


 k s 

1 p k r k E r
B S
 k s 

1
( ) ( ) ( )
S B
E r r
S B
)

E r E r
S B

E r E r
B S

E r E r
S B
E r r
S B
)

E r E r
S B
6-11

Correlation Coefficient: standardize the covariance with two standard deviations

SB
 r r
S B
)
 
S B
 r r
S B
)
   
SB S B
Range of possible values for ρ ij
–1.0 < ρ ij
< 1.0
※ ρ ij
= 1 (perfectly positively correlated): the strongest tendency for two returns to vary in the same direction
※ ρ ij
※ ρ ij
= 0: the returns on two assets are unrelated to each other
= –1 (perfectly negatively correlated): the strongest tendency for two returns to vary inversely
6-12

For two series of returns in the same period
(with n pairs of observations), the covariance is computed as follows r r
1 2
 n
1

1
 t
 r
1, t
 r
1
 r
2, t
 r
2

, where r i

1 n
 t r
※ To correct the underestimation of the covariance when using sample average to estimate the true mean, the sum of the product of deviations are divided with ( n –1) instead of n
※ Note that the function “COVAR” in Excel is not appropriate to compute the covariance between two series of returns, because the sum of the product of deviations is divided by n
※ In Excel 2010, the function “COVARIANCE.S” takes the ( n –1) adjustment into account and thus can be applied to computing the covariance given historical series of observations of two returns
6-13

Expected Return (%):
Variance (% 2 ) and Standard Deviation (%):
6-14

Covariance (% 2 ) and Correlation:
Effect of Diversification (reducing risk (s.d.)):
※ Portfolio risk heavily depends on the covariance or correlation between the returns of the assets in the portfolio
6-15

Summary:
Stock fund (i = S)
Bond fund (i = B)
Combined Portfolio (i = P)
Expected
Return (E(r i
))
10%
5%
7%
Standard
Deviation ( σ i
)
18.63%
8.27%
6.65%
Sharpe Ratio
(r f
= 1%)
0.483
0.484
0.902
※ Since E ( r
P
) > E ( r
B
) and σ
P
< σ
B
, the combined portfolio is strictly better than the bond fund according to the mean-variance analysis
※ Because the stock fund is with higher return and higher standard deviation than those of the portfolio, it is difficult to choose between the stock fund and the combined portfolio
※ With the help of the Sharpe ratio, we can identify that the combined portfolio is the best investment target
6-16

Rate of return on the portfolio: r
P
 w r
B B
 w r
S S
Expected rate of return on the portfolio:
( )
 w E r
 w E r
Variance of the rate of return on the portfolio (proved on Slide 5-43):

P
2
= w
2

2
B B

2 w w
  
B S SB B S
 w
2

2
S S
6-17

※ Alternative way to derive the formula of the portfolio variance:
Calculating the sum of the covariances of different combinations of the terms in r
P
(In this two-asset case, the four combinations of w
B r
B and w
S r
S are considered)

2
P

 portfolio variance
 r
P
 var( w r
B B
 w r
S S
) cov( w r w r
B B B B
 w r w r
B B S S
 w r w r
S S B B
 w r w r
S S S S
)

 w
2
B r
B
 w w
B S r r
S B
)
 w
2
S r
S w
2

2
B B

2 w w
  
B S SB B S
 w
2

2
S S
6-18

※ Verify the above formula numerically by the above two-asset example

P
2 2 2    2
= (0.4) (18.63) 2(0.4)(0.6)( 74.8) (0.6) (8.27)
2
2 2    2
= (0.4) (18.63) 2(0.4)(0.6)( 0.49)(18.63)(8.27) (0.6) (8.27)
2
= 43.93

P
= 6.65
※ With the above formula, it is not necessary to calculate r p different scenarios. We can derive the portfolio variance in directly if we know the weights for each asset, the variances of the return of each asset, and the covariance between the returns of these two assets
6-19

n
Given r
P
 w r
1 1
 w r
2 2
  w r n n
( )
P

1
( )
1

2
( )
2
  w E r n
( ) n
 i n 

1 w E r i
( ) i

P
2  portfolio variance
 sum of n
2
pair-wise cov( w r w r i i j j
)
 w w
1 1 r r
1 1
 w w
1 2
+ w w
2 1 cov( , )
2 1
 cov( w w
2 2
, )
1 2
  r r
2 2 w w
1 n cov( , )
1 n
  w w
2 n cov( , )
2 n
+ w w n 1 cov( , ) n 1
 w w n 2 cov( , ) n 2
  w w n n
 n n  i

1 j

1 w w i j r r i j r r n n
6-20

Returns
Bond fund E ( r
B
) = 5% Stock fund E ( r
S
) = 10%
Standard deviations
Bond fund σ
B
Weights
= 8% Stock fund σ
S
= 19%
Bond fund W
B
= 0.6
Stock fund W
S
= 0.4
Correlation coefficient between returns of the bond fund and stock fund = 0.2
6-21

Portfolio return
0.6(5%) + 0.4(10%) = 7%
Portfolio standard deviation
[(0.6) 2 (8%) 2 + (0.4) 2 (19%) 2 + 2(0.6)(0.4)
(0.2)(8%)(19%)]
½
= 9.76%
6-22

Different values of the correlation coefficient
(given w
B
= 0.6 and w
S
= 0.4)
ρ
SB
E ( r
P
)
σ
P
–1 –0.5
0 0.5
1
7% 7% 7% 7% 7%
2.80% 6.66% 8.99% 10.83% 12.40%
※ If the correlation between the component securities (or portfolios) is small or negative, there is a greater tendency for the variability in the returns on the two assets to offset each other, and thus the portfolio is with a smaller σ
P
6-23

Different weights on the bond and stock funds minimumvariance portfolio
6-24

The weights for the minimum-variance portfolio
Find w
S
to minimize

2
P
= w
2

2
B B

2(1

 w
S
 w
S
)

2
B
 w

2
S S

2
B
   
B S SB

2
B
 
2
S

2
  
B S SB
 w
2

2
S S

2 w w
  
B S SB B S
= (1
 w
S
)
2

2
B
 w
2

2
S S

2(1
First order condition (FOC)

0 with respect to w
S
 w w
S
  
S SB B S
4 w
S
)
  
SB B S

0

(0.08) 2 
(0.08)(0.19)(0.2)
(0.08)
2 
(0.19)
2 
2(0.08)(0.19)(0.2)

0.0932

minimal


P
P
2 
0.006090242

0.07804

7.804%
6-25

※ By varying different weights on the bond and stock funds, we can construct the investment opportunity set , which is a set of all available portfolio risk-return combinations
※ The blue curve is the investment opportunity set for these two risky portfolios with the correlation coefficient to be 0.2
6-26

Investors prefer portfolios with higher expected return and lower volatility
Portfolio A is said to dominate ( 宰制 ) Portfolio B if E ( r
A
)

E ( r
B
) and σ
A
 σ
B
(Thus, the stock fund dominates the portfolio Z in Figure 6.3)
An important feature of the portfolios in the investment opportunity set:
– Under the same expected return, the portfolio in the investment opportunity set dominates all portfolios to its right due to the smaller s.d.
– This feature will be employed to define the investment opportunity set in n risky-asset case in
Section 6.4
6-27

Efficient vs. Inefficient Portfolios ( 效率與非效
率投資組合 )
– Any portfolios that lies below the minimumvariance portfolio (MVP) can therefore be viewed as inefficient portfolios because it must be dominated by a counterpart portfolio above the
MVP with the same volatility but with higher expected return
For the investment opportunity set above the
MVP, because higher expected return is accompanied with greater risk, the best choice depends on the investor’s willingness to trade off risk against expected return
6-28

Investment Opportunity Sets for the Stock and
Bond Funds with Various Correlations
※ For more negative ρ
SB
, it tends to generate investment opportunity sets with smaller s.d. (Amount of risk reduction depends critically on correlation or covariance)
※ For ρ
SB
= – 1, since the movements of r
S and r
B are always in different directions, it is possible to construct a portfolio with a positive return and a zero s.d.
6-29

6.3 THE OPTIMAL RISKY PORTFOLIO
WITH A RISK-FREE ASSET
6-30

Combinations of any risky portfolio P and the risk-free asset are in a linear relation on the
E ( r )σ plane
P
 w f r f
 r f

0) w
P
in ( var( )
P r
P
 
2
P
)
(
P

)
 w r f f

P
( )
P
 
)
P f

P
( )
P
  f P
[ ( )
P
 r f
]

2
P

 w
2
 f r
2 f
 w
2

2
P P

2 w w f P r r f P
)

 
P

 w f
2
0
2  w
2
P

2
P

2 w w f P
0
 w
2

2
P P w

P P
(suppose w
P
is positive)
 w
P
  
P

/
P
Replacing w
P
E r
P

)

(
P

) r

P



E r
 r f

P


( (
P

) and

P

are in a linear relation)
6-31

Combinations with different weights form a capital allocation line (CAL)
– According the derivation on the previous slide, the reward-to-volatility ratio of any combined portfolio is the slope of the CAL, i.e., slope of CAL
P

( )
P
 r f

P
– Note that the geometric representation of the above result is illustrated on Slide 5-46
6-32

A
Consider the combination of any efficient portfolio above the MVP and r f
= 3%
– It is obvious that the reward-to-volatility ratio of portfolio A is higher than that of MVP
– Mean-variance criterion also suggests that portfolios on CAL
A is more preferred than those on CAL
MIN
6-33

We can continue to choose the CAL upward until it reaches the ultimate point of tangency with the investment opportunity set, i.e., finding the tangent portfolio O as follows
6-34

CAL
O dominates other CALs and all portfolios in the investment opportunity set: it has the best risk/return or the largest slope
( )
O

O
 r f 
( )
P

P
 r f
– Note that the mean-variance criterion also suggests the same conclusion
The tangent portfolio O is the optimal risky portfolio associated with the risk-free asset
– Given a different risk-free rate, we can find a different portfolio O such that the combinations of the risk-free asset and the portfolio O are the most efficient portfolios
6-35

Since portfolios on the CAL
O are with the same reward-to-volatility ratio, investors will choose their preferred complete portfolios along the CAL
O
– Among different combinations of the portfolio O and the risk-free asset, more risk-averse (risktolerant) investors prefer low-risk, lower-return
(higher-risk, higher-return) portfolios near r f r
O or to the right of r
O
)
(near
– Recall that on Slide 5-48, the optimal weight on
Portfolio O can be derived as y

Price of risk of the portfolio O
Invester's coefficient of risk aversion
6-36

※ For point O , the investor allocates 100% of asset in portfolio O
(with expected return of 7.16% and the s.d. of 10.15%)
※ For point C , the investor allocates 55% of his asset in portfolio
O and 45% of his asset in the risk-free asset
E r
C
    
5.29%

C

0.55 10.15%

5.58%
6-37

In this two-asset case, the weights in the stock and bond funds of the optimal tangent portfolio
O can be derived through Eq. (6.10) w
*
B

E r
B
 r f
]

2
S

E r
S
 r f
]
  
B S BS
E r
B
 r f
]

2
S

E r
S
 r f
]

2
B

E r
B
  f
( )
S
 r f
]
  
B S BS w
*
S w
*
B
The expected return and the standard deviation of the portfolio O thus can be derived through
( )
O
 * w E r
B
( )
B
 * w E r
S
( )
S

O
2
= ( w
*
B
)
2

2
B

( w
S
)

2
S

2 w w
*
  
B S SB B S
6-38

6.4 EFFICIENT DIVERSIFICATION WITH
MANY RISKY ASSETS
6-39

For n -risky assets, the investment opportunity set consists of portfolios with optimal weights
(could be negative) on assets to minimize variance given the expected portfolio return, i.e., min w i

P
2  n n  i

1 j

1 w w i j r r i j
 n n  i

1 j

1 w w
   i j , i j s.t. w E r
1 1

2
( )
2
  w E r n n

E r p w
1
 w
2
  w n

1
In other words, for given expected portfolio returns E ( r p
), the portfolios on the investment opportunity set dominate other portfolios, i.e., with less risk given the same expected return
6-40

The curve for the investment opportunity set is also called the portfolio frontier ( 投資組合
前緣 )
The upper half of the portfolio frontier is called the efficient frontier ( 效率前緣 ), which represents a set of efficient portfolios that offer higher expected return at each level of portfolio risk (comparing to the lower half of the portfolio frontier) (see the next slide)
6-41

※ For rational investors, they prefer the portfolio of risky assets on the efficient frontier due to the mean-variance criterion
6-42

Using the current risk-free rate, we can search for the CAL with the highest reward-to-volatility ratio and thus find the optimal risky portfolio O (see
Slides 6-33 and 6-34)
Finally, investors choose the appropriate mix between the optimal risky portfolio O and the riskfree asset (see Slide 6-37)
The separation property: the process to choose portfolio combinations can be separated into two independent tasks
1. Determination of the optimal risky portfolio O , which is a purely technical problem
2. The personal choice of the mix of the risky portfolio and the risk-free asset depending on his preference
6-43

An illustrative example: Construct a global portfolio using six stock market indices
※ To take the forbiddance of short sales into account, one need an additional constraint that specified on Slide 6-40 𝑤
1
, 𝑤
2
,…, 𝑤 𝑛
≥ 0 in the minimization problem
6-44

6.5 SINGLE-INDEX MODEL
6-45

The index model ( 指數模型 ) is a statistical model to measure or identify the components of firmspecific and systematic risks for a particular security or portfolio
William Sharpe (1963), who is a Nobel Prize laureate in 1990, introduced the single-index model ( 單一指數模型 ) to explain the benefits of diversification
– To separate the systematic and nonsystematic risks in the single-index model, it is intuitive to use the rate of return on a broad portfolio of securities, such as the
S&P 500 index, as a proxy for the common macro factor (market risk factor)
– The single-index model is also called the market model
6-46

Excess return of security i can be stated as:
R i
  i
R i M
 e i
– R i
– R
M
– e i
(= r i
(= r
–
M r f
) denotes the excess return on security
– r f i
) denotes the excess return on the market index denotes the unexpected risk relevant only to this security and E ( e i
) = 0, var( e i
) = σ 2 ( e i
), cov( R
M
, e i
) = 0, and cov( e i
, e j
)
= 0
– α i
– β i is the security’s expected excess return if R
M is zero measures the sensitivity of the excess return R i with respect to the market excess return R
M
– This model specifies two sources of risks for securities:
1.
2.
Common macro factor ( R
M
) (or the market risk factor): represented by the fluctuation of the market index return
Firm-specific components ( e i
): representing the part of uncertainty specific to individual firms but independent of the market risk factor
6-47

點散圖
R i
R
M
※ The linear relation is drawn so as to minimize the sum of all the squared errors (which is measured as the vertical distance between each node and the examined straight line) around it. Hence, we say the regression line “best fits” the data in the scatter diagram
※ The line is called the security characteristic line (SCL)
6-48

點散圖
R i
R
M
Geometric interpretation of α i and β i
– The regression intercept is α i
, which is measured from the origin to the intersection of the regression line with the vertical axis (see the previous slide)
– The regression coefficient β i is measured as the slope of the regression line
The larger the beta of a security
 the greater sensitivity of the security price in response to the market index
 the higher the security’s systematic risk
6-49

α i
β i
The slope and intercept of the best-fit regression line can be derived as follows
A
7
8
5
6
2
3
4
Week
9
10
11
12
13
14 Average:
15
16
1
30
31
32
33 Intercept
34 Market Return
35
6
7
4
5
2
3
8
9
10
B C D
Annualized Rates of Return (%)
E
ABC XYZ Mkt. Index Risk Free
65.13 -22.55 64.40 5.23
51.84
-30.82
31.44
-6.45
24.00
9.15
4.76
6.22
-15.13 -51.14
70.63 33.78
107.82
-25.16
32.95
70.19
50.48 27.63
-36.41 -48.79
-42.20 52.63
Coefficients Std. Error
4.336
1.156
16.564
0.630
-35.57
11.59
23.13
8.54
25.87
-13.15
20.21
17
18
19
20
21
22 SUMMARY OUTPUT OF EXCEL REGRESSION
23
24 Regression Statistics
25 Multiple R
26 R-square
27 Adj. R-square
Standard
28
Error
29 Observations
0.544
0.296
0.208
48.918
10.000 t-stat
0.262
1.834
3.78
4.43
3.78
3.87
4.15
3.99
4.01 p-value
0.800
0.104
F G
ABC
47.08
66.20
104.04
-29.03
H
Excess Returns (%)
XYZ
59.90 -27.78 59.17
26.68 19.24
-37.04 -12.67
-18.91 -54.92 -39.35
29.35
29.17
66.32
I
Market
2.93
7.16
19.35
4.67
46.33 23.48 21.72
-40.40 -52.78 -17.14
-46.21
15.20
48.62
7.55
16.20
9.40
COVARIANCE MATRIX
ABC
3020.933
XYZ Market
ABC
XYZ 442.114 1766.923
Market 773.306 396.789 669.010

ABC
 cov( R
ABC var( R
M
, R
)
M
)

773.31
669.01

1.156
E R
ABC
)
 
ABC

 
ABC
E R
ABC
)



ABC
E R
M
)

ABC
E R
M
)

R
ABC
 
ABC
 

R
ABC


4.33%
※ The SCL of ABC is given by
R
ABC
= 4.33% + 1.156 R
Market
※ The regression results in the left table is generated by the Data
Analysis tool in Excel
6-50

Because the firm-specific components of the firm’s return is uncorrelated with the market return, we have the following equation
Total risk
 var( R i
)
 var(
  i

R i M
 e i
 var(
 i
R
M
 e i
 var(

R i M
)
)
 e i
)
  
2
 i M
 2 e

Systematic risk + Firm-specific risk
– The systematic risk of each security depends on both the volatility in R
M
(that is, σ
M
) and the sensitivity of the security to fluctuations in R
M
(that is, β i
)
– The firm-specific risk is the variance in the part of the stock’s return that is independent of market return (that is, σ 2 ( e i
))
6-51

One method to measure the relative importance of systematic risk is to calculate the ratio of systematic variance to total variance
Systematic variance

Total variance
 
2 i M

2 i


 cov( ,
M
)

2
M


2 
2

M i
2
 cov( ,
M
)
 
2
M i
2
 
2 iM

R
2
– A larger correlation coefficient (in absolute value terms) indicates that the systematic risk represents a larger portion of the total risk and is more important
– At the extreme, when the correlation is either 1 or –1, the return of the individual stock is perfectly positive or negative correlated with the market return. Thus the security return can be fully explained by the market return and there are no firm-specific effects
※ This ratio is also called R-squared, which measures how well the market return can explain the individual return
6-52

Interpretation of Regression Lines and Scatter
Diagrams
※ For R
1 to R
6
, β > 0, and for
※ For R
1
, R
2
, and R
※ For R
2
, R
3
6
R
7 and R
8
, β < 0
, β ’s are larger because the regression lines are steeper
※
, and R
7
, the degree of deviations from the regression line are smaller and relatively stable, which implies a smaller
For R
1
, R
4
, and R
σ 2 ( e i
) or a higher R-squared value
8
, α > 0, which is a preferred feature because for stocks with the same β , a higher α means a higher expected excess return for that stock
6-53

Diversification in a Single-Factor Security Market
The systematic component of each security return β i
R
M is perfectly correlated with the systematic part of any other security’s return
Thus there are no diversification effects on systematic risk no matter how many securities are involved
The beta of a portfolio is the weighted average of the individual security betas in that portfolio
If r
P
 i n 

1 w r i i
, then

P
 i n 

1 w
 i i
(This result will be verified numerically in
Ch. 7)
This is why the systematic risk is also called the nondiversifiable risk
6-54

Diversification in a Single-Factor Security Market
Since the firm-specific risks ( e i
) are independent of each other, their effects could be offset or almost eliminated through diversification, i.e., through investing in many securities
Consider a equally weighted portfolio
Total firm-specific risk
 var( w e
1 1
 w e
2 2
  w e n n
)
 i n n 

1 j

1 w w i j e e i j
)
 i n 

1 w i
2
 e
1
 e
2 n
2
  var( ) n
 n n

2


2 n
2 n


0
So the firm-specific risk is also called the diversifiable risk e i
6-55

To improve the Sharpe ratio of the market portfolio based on stocks with nonzero α ’s in the single-index model
– Construct a new portfolio based on the market portfolio M and these non-zero alpha stocks
– First, construct the active portfolio A by optimally combining non-zero alpha stocks with the weights w i

 i
  i
/ 2 ( ) i
  i
/
2
( ) i
 
A
  i w i i A
  i w
  i i
,
2
( e
A
)
  i w i
2

2 e
Positive (negative) α i
 positive (negative) w i purchase (short) the stock i
 w i
α i
 always has positive contribution to α
A
Smaller σ 2 ( e i
)

Higher R 2 based on the market model
 higher confidence level of the accuracy of α i
 higher weight w i on the stock i
6-56

– Second, construct the investment opportunity set using the portfolios M and A , and find the optimal tangent portfolio O given the risk-free rate r f r
A r f

A
 
( r
A M
 r f
)
 e
A


E r
A
 
A r f

A

2
A
  
2
A M
 
2
E r
M
)
 r f
]
( e
A
)
E r
A

2
A
E r
M
),

2
M
), and

MA
  
A M
/

A
into the formulae on Slide
6.38, and the optimal weights on the p ortfolios
A and M can be derived as follows.
w
*
A
 w
0
A
1
 w
0
A
(1
 
A
) where w 0
A

and w
*
M
 
A
/ 2 ( e
A
)
R
M
/

2
M
  w
*
A
,
( R
M

(
M
)
 r f
)
– The improvement of the Sharpe ratio
SR
2
O

SR
2
M
   
A e

2 
A
2 
0
6-57

w
Google
and w
Dell
in the active portfolio

A

A

2
( e
A
) w
0
A
(derived based on
 
A
,
2
( e
A
), R
M
, and

2
M
) w
*
M
and w
*
A
※ Note that the performance of the Treynor-Black model critically depends on the accuracy of the prediction of α i
‘s and β i
‘s in a future period of time
6-58

6.6 RISK OF LONG-TERM INVESTMENTS
6-59

Vast majority of financial advisers believe that stocks are less risky if held for the long run
– Risk premium for the Tyear investment is RT (= R +
R +…+ R )
– Variance for the T -year investment is σ 2 T (Under the assumption that excess returns are serially uncorrelated, var( R + R +…+ R ) = var( R ) + var( R )
+…+ var( R ) = T var( R ) = σ 2 T
– Standard deviation for the T -year investment is σ 𝑇
– As a result, the Sharpe ratio becomes RT /
σ 𝑇 =
R 𝑇 / σ
When T is large, R 𝑇 / σ is higher than R / σ , the 1-year
Sharpe ratio
6-60

Time diversification effect:
– The overperforming and underperforming effects could offset for each other and therefore reduce the variance of the T -year investment
However, the time diversification effect is widely rejected in practice primarily due to
– The serial correlation for successive returns cannot be ignored, so var( R + R +…+ R ) ≠ var( R ) + var( R )
+…+ var( R )
6-61