Morningstar - Q4 2020 - 64

Strategies

U ( E,V ) = u (1 + E ) +

1
u"(1 + E )V
2
O -1

(1 + E ) O , O ≠ 1
-1
u (1 + E ) =
ln(1 + E ) ,
O =1
Using the Levy-Markowitz
utility
function, we can
-1
(1 +the
u"
E ) =MVO problem1 +asO follows:
write
(1 + E ) O

Solving the Asset-Location Problem, Part II
How to expand mean-variance
optimization to build a tax-aware model.

m

max
x , x , ... , x
1

2

m

∑

U

xi i ,

i =1

m

m

∑∑ x x

i j i j

ij

i =1 j=1

m

∑ x = 1,

s.t.

i

xi ≥ 0

i =1

QUANT U

Paul D. Kaplan
In the previous issue of Quant U, I introduced the
problem of simultaneously finding an optimal
asset allocation and the optimal location of asset
classes in taxable and tax-advantaged accounts.
I presented a method for calculating pretax
and aftertax expected returns that implies a set
of effective tax rates, which applies to
both expected returns and standard deviations.
In this issue, I review the basics of mean-variance
optimization, or MVO, and show how to extend it
using effective tax rates to simultaneously solve for
optimal asset allocation and location. I then show
how to further extend the model to incorporate an
investor's assets and liabilities that should
be taken into account when deciding on the asset
allocation and location of the financial assets.
These are the very assets and liabilities I discussed
in the Q2 2020 issue of Quant U ("A Better
Retirement Spending Rule for Everyone"), although
here they apply at all stages of the lifecycle.
The Mean-Variance Criterion
In an earlier Quant U ("Clearing Up the Great
Confusion," October/November 2014), I explained
the logic that Harry Markowitz uses to justify MVO.
Given a utility function in one plus return, u(.), the
expected utility of a random return can be
approximated by the expected return ( E ) and the
variance of return (V ) as follows:

1
U ( E,VU) (=x 'u (,1x+' Ex) )+s.t. xu"' (1=+1,E )Vx ≥ 0
max
2
This can be written more succinctly using matrix
O -1
notation. Let:
(1 + E ) O , O ≠ 1
-
1
u (1=+(E1)- = )
i
Ai
i
ln(1of+ pretax
E ) , expected
O = 1returns
	 =	 the vector
=
(
1
- 
)
i
Ai
i
	 =	 covariance-matrix
of pretax asset class
1
u"(1 + E ) =
1+ O
		 returns. The
ij-element
is
(1 + E ) O
( Tvector
)' allocation
max
+ Bof
, ( T A + Bto) 'the( Tasset
+ B)
	 =	 Uthe
A
A
m
m m
,
A B
		classes
s.t.U A' = xi , i ,B' = 1 -xi x,j iA j≥ ij0, B ≥ 0
	max=	 a vector
i =1 of m ones
i =1 j=1
x , x , ... , x

Because this approximation for expected utility
was first introduced by Haim Levy and
Markowitz, I refer to it as the Levy-Markowitz
utility function.1

(

I assume the constant relative risk aversion, or
1
( E,V,) utility:
CRRA
U
= u (1 + E ) + u"(1 + E )V
2
u (1 + E ) =

(1 + E )
-1
ln(1 + E ) ,

1

2

i =1

-1
u"(1 +UE( )x =' , x ' x ) s.t.1 +x O' = 1, x ≥ 0
max
(1 + E ) O
m

∑∑

xi xj i j ij
= (that
1 - Uu"
) i + Exi) isi , negative
i (1
Ai
Note
so that variance
i =1
i =1 j=1
max
x , x , ... , x
is
"bad."
=
(
1
- 
)
m
i
Ai
i
s.t. xi = 1, xi ≥ 0
i =1 Optimization Without Taxes
Mean-Variance
max U ( T A + B )' , ( T A + B ) ' ( T A + B )
To
describe
MVO
) s.t. x 'taxes,
, U ( x ' , x ' xwithout
≥ 0the following
max
= 1, I xuse
A B
s.t. A' = , B' = 1 - , A ≥ 0, B ≥ 0
notation:
1

2

m

∑

(

m

m

i =1

	

O =1

∑

∑

2

∑

, O ≠1

∑∑

m

1

∑∑

0 written this way:
1, now
xi ≥ be
s.t. xi =can
The MVO problem

m

m

∑

O -1
O

-1
u"(1 + E ) =
1+ O
where is the level
O investor's risk tolerance,
(1 + Eof) the
1 0 and 1.
which
is
usually
between
U ( E,V ) = u (1 +m E ) + m u"m(1 + E )V
2
U
xi i ,
xx
O i- 1j i j ij
The
second derivative
i =1
i =1 the
max
O , Outility
(1of
+j=1
E ) CRRA
≠1
x , x , ... , x
= m -1
(1 + E ) is:
ufunction
s.t. lnx(i 1=+1,E ) ,xi ≥ 0 O = 1

∑

)

)

f

max
U ( x ' , x ' x ) s.t. x ' = 1, x ≥ 0
R˜W = F R˜F + H R˜H - L R˜L
W
W
W

P

= ( 1 -  ) i subject to the non-negativity and
Ai meansi that
This
budget
constraints,
the optimizer finds the
= ( 1 - 
) i
i
Ai
allocations
to
the
different
) that
T A +classes
max U EW ( T A + B ) , VW (asset
B
maximize
expected
utility.
,
A B U (T
max
s.t. A' A =+ ,B )'B' , =( T1 -A +, BA )≥' 0,( T BA ≥+ 0 B )
,
A B
In the last
, A ≥ 0,pretax
' = 1U,-I derived
≥0
s.t. issue
' = of,Quant
B
B
A
expected returns on a set of asset classes using
the reverse optimization technique. In reverse
optimization, we assume that a given portfolio-
the reference portfolio1-that
1 is often a market
c OL-1 frontier
portfolio-is
O H=the1R˜ efficient
u(c)
˜R +on
˜R == F{ ln(c),
-
R˜ , O ≠ 1 and,
W
F
H 1- 1 L
W optimal
W for
WO
therefore,
investors
who have a certain
level of risk tolerance.
To
find
what
level of risk
~ - O1
c
~
tolerance
makes
the
reference
portfolio
optimal,
1
t
t+1
= Qt+1
+
c
1
I first traced
t out the entire efficient frontier
(max
EXHIBUIT E
1W
). (Then,
T A + forB )various
, VW ( T values
+ B )of , I search
A
, efficient
c~At+1
O
~t for- the
the
optimal portfolio.
1 frontier
B
=s.t. ' =O ,Qt+1' = 1 - , ≥ 0, ≥ 0
B
ct the
+A ) of B that resultsA in an optimal
(1value
I take
portfolio closest to the reference portfolio as the
~t - O for which the reference
level
Qt+1
~ of risk tolerance
-turns
1 out to be 22.7%.
ROt+1 = is optimal.
portfolio
O
~t 1 -This
Et Qt+1

(
(

(

)

)

)

	 ==	( 1 - 
thei )pretax
expected return on
Ai
i
i
		
asset
class
i
= ( 1 -  i ) i
Ai
	i =	 the pretax standard deviation
		
+ Hclass
R˜ -i L R˜L
R˜W = FonR˜Fasset
( Tcorrelation
( TWreturns
)
max
+W B )H' , of
' ( T A + asset
+ B )between
Uthe
A
A
B
	ij =	W
,
A B
		classes
s.t. A' =i and
, Bj' = 1 - , A ≥ 0, B ≥ 0
1
xi	 =	 the allocation to asset class i
To illustrate maximizing
expected
utility in the
1
~Ot =11- O c1- O -1 , O ≠ 1
U ( E,V ) = u (1 + E ) + u"(1 + E )V
~
=
{
ln(c),
u(c)
E
Qt+1framework,1 I ~include an
ct+1
2
m	 =	 the number of asset classes
mean-variance
t
1-1 +O R
=
Ot+1
max U EW ( T A + B ) , VW ( T A + B )
ct n = (1qT+ ) O
O -1
(1 + E ) O , O ≠ 1
,
A B
1
-
1
~ -O
u (1 + E ) =
s.t. A' = , B' = 1 - , A ≥ 0, B ≥ 0
~t
qT3, PP.
1 f =c , n-
1 Levy, H., & Markowitz,
H.M.
Expected Utility by
Issue
308-17.
= Qt+1
ln(1 +
E ) ,1979. "Approximating
O =1
F R˜ of+ Mean
H R˜ and- Variance."
L R˜ The American Economic Review, Vol. 69t+1
˜R a =Function
+
c
1
W
n
t
W F W H W L
-n
-j
c
y
y f
-1
+ 1+ q
P= q
1+ q
u"(1 + E ) =
~
1+ O
ct+1
1 j =1 ~t - O
64 Morningstar (Q4
1 +2020
E) O
=
Q
ct
(1 + ) O t+1 n
- qT
f- j
m
m m

(

(

)

)

n

( )

P
n

f
D
P

PP

c

Dq

=P

Pcc

( )
( )

D

( )

=
D

(

∑

( )
c

)

(

) (

y

)

y

q

Pc

D

D

∆
P
D



Morningstar - Q4 2020

Table of Contents for the Digital Edition of Morningstar - Q4 2020

Contents
Morningstar - Q4 2020 - CT1
Morningstar - Q4 2020 - CT2
Morningstar - Q4 2020 - Cover1
Morningstar - Q4 2020 - Cover2
Morningstar - Q4 2020 - 1
Morningstar - Q4 2020 - 2
Morningstar - Q4 2020 - Contents
Morningstar - Q4 2020 - 4
Morningstar - Q4 2020 - 5
Morningstar - Q4 2020 - 6
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Morningstar - Q4 2020 - Cover3
Morningstar - Q4 2020 - Cover4
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2021q2
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2021q1
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2020q4
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2020q3
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2020q2
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2020q1
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2019winter
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2019fall
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2019summer
https://www.nxtbook.com/nxtbooks/morningstar/magazine_2019spring
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20191201
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20181011
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20180809
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20180607
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20180405
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20180203
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20181201
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20171011
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20170809
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20170607
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20170405
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20170203
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20171201
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20161011
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20160809
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20160607
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20160405
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20160203
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20161201
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20151011
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20150809
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20150607
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20150405
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20150203
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20151201
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20141011
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20140809
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20140607
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20140405
https://www.nxtbook.com/nxtbooks/morningstar/magazine_20140203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20141201
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20131011
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20130809
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20130607
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20130405
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20130203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20131201
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20121011
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20120809
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20120607
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20120405
https://www.nxtbook.com/nxtbooks/morningstar/investorconference2012
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20120203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20121201
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20111011
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20110809
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20110607
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20110405
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20110203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20111201
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20101011
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100809_lincoln
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100809
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100607_lincoln
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100607
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100405_lincoln
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100405
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20100203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20101201
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20091011
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20090809
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20090607
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20090405
https://www.nxtbook.com/nxtbooks/morningstar/advisor_20090203
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2008fall
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2008summer
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2007spring
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2007fall
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2007summer
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2008spring
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2008catalog
https://www.nxtbook.com/nxtbooks/morningstar/advisor_2008winter
https://www.nxtbookmedia.com