Tactical Asset Allocation
session 5
Andrei Simonov
Tactical Asset Allocation
1
3/21/2016
Agenda



What is tactical asset allocation?
Mean-variance perspective on TAA and SAA
Predictability
–
–
–
–
–
January dummy
Business cycle variables
Explaining risk premia: US, World, Sweden.
Currency risk premia
Caveats: data snooping, statistical issues.
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What is TAA?




Exists since early-to-mid- 80-ies.
By now $100-200 bln are under management by TAA
managers
A TAA managers’s investment objective is to obtain
better-than-expected return with (possibly) lower-thanbenchmark volatility by forecasting the returns of two or
more asset classes and varying asset class exposure in
systematic manner (Phillips, Rogers & Capaldi, 1996)
Can TAA funds be interpreted as stand-alone asset
class?
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Conditioning Information and Portfolio
Analysis
Er
Add conditioning
information and weights
change through time.
Frontier shifts.
Vol
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Optimal portfolio for risk-averse investor
max w E (R ) 
T

2
w T Vw
s. t. w T 1  1
  11 ..  1N 


T
T
Here w  ( w1 , w2 ,...),1  (1,1,1,...,1), V   .. .. .. 


..

NN 
 N1
min L  w T E (R ) 



w T Vw   1  w T 1
2
 E (R )  Vw  1  0
V 1 E (R )  1
w
. Summing up :

T
1 w 1  0


1T V 1 E (R )

  T 1  T 1 
1 V 1
1 V 1
V 11
V 1 
1T V 1 E (R ) 
*
 E (R )  1 T 1 
w  T 1 
1 
V
1
 
1 V 1 

Global min var
portfolio
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Equilibrium and TAA
Let us assume that there exists long-term
expected returns vector e. However, due to
predictability of asset returns, eE(R)





V 11
V 1 e1T  1eT V 1
V 1 1T  1T V 1
w  T 1 
1
1
T
1
T
1
1 
V
1
 1 V 1  
 1
V 1



*
Global min var
portfolio
StrategicBet
TacticalBet
0
E rn   en  ( E r1   e1 )


1T  1T   E r1   e1  ( E rj   e j ) 0

 E r1   e1  ( E rn   en )

0


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How to do it?
We need a model that explains the
connection between today’s variables and
tomorrow returns.
 Candidates: economic business cycle
variables and Jan. Effect.

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Example: Incredible January Effect
Excess returns associated with small firms
w.r.t. Large-cap stocks
 Ritter: Tax effect. Is it so?
 Incredibly Shrinking January Effect
(William J. Bernstein ).

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Example: dividend yield
Fama-French (1988). 1927-1986
Holding
Coeff.
period
M
0.21
Q
1.07
1
2.47
2
7.38
3
9.94
4
12.86
t(coeff)
1.40
2.10
1.27
2.04
2.21
2.43
R2
0.00
0.01
0.01
0.09
0.13
0.19
• May not be sustained out of sample
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Risk and return over the business cycle
m,t  Et  Rm   rt   m vart  Rm 
????
G-7 output, 1973Q2 to 1996Q2
output level
potential line
Average
returns
Return
volatility
end. recess beg. expan end. expan beg. recess
15.23%
10.36%
6.96%
2.86%
Tactical Asset Allocation
12.59%
10.63%
16.85%
26.98%
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Evaluation of Recent
Recession




In July 2000, the Yield Curve inverted forecasting
recession to begin in June 2001.
Official NBER Peak is March 2001 (Yield Curve within
one quarter accurate).
In March 2001, the Yield Curve returned to normal
forecasting the end of the recession in November 2001.
On July 17, 2003 the NBER announced the official end of
the recession was November 2001.
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Exhibit 1
Next couple of slides are due to Cam Harvey
Lead Lag Analysis in Months
Business Cycle
5-Year Yield Spread
NBER NBER
Length
Length of
Peak
Trough
of Cycle Inversion Lead Normal Lead
Inversion
Dec-69
Nov-70
11
Oct-68
14
Feb-70
9
16
Nov-73
Mar-75
16
Jun-73
5
Jan-75
2
19
Jan-80
Jul-80
6
Nov-78
14 May-80
2
18
Jul-81
Nov-82
16
Oct-80
9
Oct-81
13
12
Jul-90
Mar-91
8
May-89
14
Feb-90
13
9
Average last four
11
11
7
15
Recent Recession
Mar-01
Nov-01
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8
Jul-00
8
Mar-01
8
8
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Exhibit 2
Forecast evaluation
Term
Structure
Inversion
Date
Jul-2000
Average
Forecast
Actual
Lead to
Beginning Recession
Recession of Recession Begins
11
Term
Structure
Average
Normal Date Lead
Mar-2001
Tactical Asset Allocation
8
Jun-2001 Mar-2001
Forecast
End of
Recession
Error
3
Actual End Error
Nov-2001 Nov-2001
0
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Yield Curve Inverts Before Last Six Recessions
(5-year Treasury note minus 3-month Treasury bill yield-secondary)
Annual
GDP growth
or Yield Curve %
Source: Campbell R. Harvey.
% Real annual GDP growth
8
6
4
2
Yield curve
0
-2
Recession
Correct Recession
-4
Correct 2 Recessions
Correct
Recession
Correct
Recent
flattening
Yield curve accurate
in recent recession
Data though April 11, 2006
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
-6
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Yield Curve Inverts Before Last Six Recessions
(5-year Treasury note minus 3-month Treasury bill yield – constant maturity)
Annual
GDP growth
or Yield Curve %
8
% Real annual GDP growth
Source: Campbell R. Harvey.
6
4
2
Yield curve
0
-2
Recession
Correct Recession
-4
Correct 2 Recessions
Correct
Recession
Correct
Recent
flattening
Yield curve accurate
in recent recession
Data though April 11, 2006
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
-6
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Recent Annualized One-Quarter GDP Growth
(10-year and 5-year Yield Curves-secondary market)
Annualized
1-quarter
GDP growth
8
10-year
Yield curve
4
% Real annualized one-quarter GDP growth
6
3
4
2
2
1
0
0
-2
5-year
Both curves
invert 2000Q3
-1
Data though April 11, 2006
-2
M
ar
-9
Se 5
p9
M 5
ar
-9
Se 6
p9
M 6
ar
-9
Se 7
p9
M 7
ar
-9
Se 8
p9
M 8
ar
-9
Se 9
p9
M 9
ar
-0
Se 0
p0
M 0
ar
-0
Se 1
p0
M 1
ar
-0
Se 2
p0
M 2
ar
-0
Se 3
p0
M 3
ar
-0
Se 4
p0
M 4
ar
-0
Se 5
p0
M 5
ar
-0
6
-4
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Recent Annualized One-Quarter GDP Growth
(10-year and 5-year Yield Curves-constant maturity)
Annualized
1-quarter
GDP growth
8
10-year
Yield curve
4
% Real annualized one-quarter GDP growth
6
3
4
2
2
1
0
0
-2
5-year
Both curves
invert 2000Q3
-1
Data though April 2006
-2
M
ar
-9
Se 5
p9
M 5
ar
-9
Se 6
p9
M 6
ar
-9
Se 7
p9
M 7
ar
-9
Se 8
p9
M 8
ar
-9
Se 9
p9
M 9
ar
-0
Se 0
p0
M 0
ar
-0
Se 1
p0
M 1
ar
-0
Se 2
p0
M 2
ar
-0
Se 3
p0
M 3
ar
-0
Se 4
p0
M 4
ar
-0
Se 5
p0
M 5
ar
-0
6
-4
Tactical Asset Allocation
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What shall we expect now?
US yield curves, 2006
5.4
5.2
5
4.8
4.6
4.4
1/3/2006
4/3/2006
7/3/2006
8/29/2006
4.2
4
1 mo
3 mo
6 mo
Tactical Asset Allocation
1 yr
2 yr
3 yr
5 yr
7 yr
10 yr
20 yr
30 yr
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May 2007: Practically flat
5.1
5
4.9
4.8
4.7
5-5.1
4.6
4.9-5
4.5
4.8-4.9
4.4
4.7-4.8
4.3
4.6-4.7
4.5-4.6
4.2
5/1/2007
5/3/2007
4.4-4.5
4.3-4.4
5/7/2007
5/9/2007
4.2-4.3
5/11/2007
5/15/2007
5/17/2007
5/21/2007
1mo
Tactical Asset Allocation
3mo
6mo
1yr
2yr
3yr
5yr
7yr
10yr
20yr
30yr
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August 2007
5-6
4-5
6
3-4
2-3
5
1-2
0-1
4
3
2
1
0
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Current Situation: Economic growth
•The economy expanded at an annual pace of 4.1%, the most in
more than a year, according to the median estimate of 81
economists surveyed by Bloomberg News. The Commerce
Department last month calculated the growth rate at 3.4%.
• But the outlook for the second half of 2007 has soured in recent
weeks as the subprime mortgage crisis has restricted access to
credit. The Federal Reserve this month said risks to growth had
``increased appreciably'' and economists at JPMorgan and
Lehman are among those that have reduced forecasts.
•There are growing signs of a housing slowdown; new home
sales down, housing prices down, and homeowners with ARMs
facing much higher interest rates.
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Current Situation
Inflation perceptions. The long-term rate is a combination of
expected inflation, expected real interest rates and an
inflation risk factor. Long-term inflation expectations have
decreased mainly due to the glut of cheap labor resulting
from globalization.
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Current Situation
Strong buying of long-term bonds by foreigners. For the past
few years, strong buying by Asian central banks have
pushed up the Treasury bond prices. However, there is a
debate as to whether this has had a large impact on bond
prices. In addition, this buying has flattened out recently. A
recent Fed study estimated that the foreign buying pushed
yields down by 150bp. Subprime crisis does not end
buying of T-debt by foreigners. Demand for 5yr TB last
week was very high.
Tactical Asset Allocation
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Current Situation
Hedge funds. There has been a recent increase in demand for
U.S. bonds from the Caribbean area indicating hedge fund
activity. With long-rates above short rates, many managers
do “carry trades” (borrow short-term and buy long-term
bonds hoping the relation between rates remains stable).
As the term structure flattens, many of these managers
increase their leverage which means more buying pressure
on the long-term bonds.
Tactical Asset Allocation
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Current Situation
Demographic forces. As the population ages, more money is
allocated into fixed income and long-term bond yields may
decrease.
Inflation risk. The long-rate rates contain expected inflation,
expected real rates and an inflation risk factor. It is widely
perceived that inflation risk (an unexpected episode of
inflation turbulence) has decreased.
Tactical Asset Allocation
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Annual Real Economic Growth After
Yield Curve Inversions
4.50%
4.00%
3.50%
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
0.00%
Up to one year after
inversions
Tactical Asset Allocation
Other quarters
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Stock Returns and U.S. Yield Curve
Average Monthly Returns in %
3
2.5
2
1.5
1
0.5
0
Data through
November 2000
Tactical Asset Allocation
Inversion
W
O
US
UK
CH
SE
ES
SG
NO
NL
JP
IT
HK
DE
FR
DK
CA
BE
AT
AU
-0.5
Normal
27
3/21/2016
Average Monthly Stock Returns After
Yield Curve Inversions
1.40
1.20
1.00
0.80
Equally weighted
0.60
Value weighted
0.40
0.20
0.00
After first month of
inversion
Normal
Based on 19 countries.
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Trader’s calendar (from thestreet.com)
Time Indicator
(EST) (click for definition)
Source
(click for press release)
Forecast
Previous
(revised)
Previous
(original)
n.a.
+0.8%
+0.8%
n.a.
+2.2%
+2.5%*
n.a.
--
675.5
--
n.a.
--
432.3
--
n.a.
--
-7
--
+305,000
--
+293,000
---
n.a.
+0.9%
---
+306,000
+3.7%
--
n.a.
--
+1.5%
Census Bureau
--
.860M
--
.858M
Bureau of the Public Debt
The Treasury announces the size of its next monthly two-year note
auction, next Tuesday.
National Association of Realtors
--
6.10M
--
6.12M
Economic Cycle Research Institute
--
n.a.
--
+6.1%
Actual
Monday, May 21
No releases.
Tuesday, May 22
9 a.m. ICSC-UBS Weekly Chain Store Sales Snapshot for International Council of Shopping Centers
-1.5%
the week ended May 19
and UBS
9 a.m. Johnson Redbook Retail Sales Index for the week
Redbook Research
+2.0%
ended May 19, vs. April
Wednesday, May 23
9 a.m. Mortgage Applications Survey for the week ended
-May 18 -- Market Composite Index
Mortgage Bankers Association
Purchase Index
9 a.m. Consumer Comfort Index for the week ended May
20
Thursday, May 24
8:30
Initial Jobless Claims for the week ended May 19
a.m.
8:30
a.m.
Four-week average
Durable goods orders for April
Ex-transportation
10 a.m. New home sales for April
2:30
p.m.
Treasury auction announcement
Friday, May 25
10 a.m. Existing Home Sales for April
10:30
Weekly Leading Index for the week ended May 18
a.m.
Tactical Asset Allocation
ABC News and Washington Post
Labor Department
Census Bureau
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What variables matter?
Methodology:
1.
Exploratory: regressing
returns at t on
informational variables at
t-1
2.
”Correct one”: first
finding economic risk
premia (a la APT) and
then regressing it on
informational variables at
t-1
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Do informational variables have
predictive ability?

Info variables:
– January dummy
– Past excess return on Equally
weighted CRSP index
– Spread between 1 and 3 mo Tbills
– Dividend yield
– Spread between Baa and Aaa
corporate bonds
– 1-mo T-bill rate
Tactical Asset Allocation
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
Tactical Asset Allocation
Here how it
looks like...
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Performance & Business Cycle
Average Annual Returns During U.S. Business Cycle Phases
30
20
10
0
-10
-20
A
us
tr
A alia
u
Be stri
lg a
Ca ium
D nad
en a
m
Fi ark
nl
Fr and
G an
H erm ce
on a
g ny
K
Ire ong
la
n
Ita d
N J ly
e
N the apa
ew rl n
Ze and
a s
N l and
o
Po rwa
rtu y
g
Sp al
S
Sw we ain
it z de
er n
la
nd
U
K
U
W
or W S
ld or
ex ld
-U
EA S
FE
-30
Expansion geometric mean
Data through June 2002
Tactical Asset Allocation
Recession geometric mean
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us
tr
A alia
u
Be stri
lg a
Ca ium
D nad
en a
m
Fi ark
nl
Fr and
G an
H erm ce
on a
g ny
K
Ire ong
la
n
Ita d
N J ly
e
N the apa
ew rl n
Ze and
a s
N l and
or
Po wa
rtu y
g
Sp al
Sw Swe ain
it z de
er n
la
nd
U
K
U
W
or W S
ld or
ex ld
-U
EA S
FE
A
Performance & Business Cycle (2)
Average Annual Volatility During U.S. Business Cycle Phases
60
50
40
30
20
10
0
Expansion std.dev.
Data through June 2002
Tactical Asset Allocation
Recession std.dev.
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Performance & Business Cycle (3)
Correlations During U.S. Business Cycle Phases
1
0.8
0.6
0.4
0.2
0
A
us
tr
A alia
u
Be stri
lg a
Ca ium
D nad
en a
m
Fi ark
nl
Fr and
G an
H erm ce
on a
g ny
K
Ire ong
la
n
Ita d
N J ly
e
N the apa
ew rl n
Ze and
a s
N l and
or
Po wa
rtu y
g
Sp al
Sw Swe ain
it z de
er n
la
nd
U
K
U
W
or W S
ld or
ex ld
-U
EA S
FE
-0.2
Expansion correlation with US
Data through June 2002
Tactical Asset Allocation
Recession correlation with US
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3. Performance & Business Cycle (4)
Covariances During U.S. Business Cycle Phases
45
40
35
30
25
20
15
10
5
A
us
tr
A alia
u
Be stri
lg a
Ca ium
D na
en da
m
Fi ark
nl
Fr and
G an
H erm ce
on a
g ny
K
Ire ong
la
n
Ita d
N J ly
e
N the apa
ew rl n
Ze and
a s
N l and
or
Po wa
rtu y
g
Sp al
Sw Swe ain
it z den
er
la
nd
U
K
U
W
or W S
ld or
ex ld
-U
EA S
FE
0
Expansion covariance with US
Data through June 2002
Tactical Asset Allocation
Recession covariance with US
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How important are global factors?




Based on Ferson-Harvey RFS95
Question here is: what is more important, local or global
factors for predictability of asset returns.
Global Informational variables: : ”old friends”: 1 mo t-bill, div.
Yield on MSCI World index, spread between 10yr and 3 mo Tbills, Eurodollar/US treasury spread, lagged market return,
January dummy.
Local informational variables: Country x div. Yield, 30-day tbill rate, term spread, lagged MSCI country x market return.
E Rit Z t 1   0 Z t 1     ij Z t 1  j Z t 1    0l Z t 1,l 
K
L
j 1
l 1
 L
 L

     ijl Z t 1,l    jm Z t 1,m 
j 1  l 1
 m1

K
Tactical Asset Allocation
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So, what
matters?
”Global only”
model is already
good enough
 Adding local
factors increases
explanatory power
of the model

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Changes in  vs changes in risk premium
Var E  '  Z   E (  ' )Var E  Z E (  ) 
E ( ' )Var E  Z E ( )

Only 2-4% of variation is due to beta’s.
Tactical Asset Allocation
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Sweden (Robertsson, 2000):
Sweden
Market Index
Small Stocks
Bond Index
bond
–2.05
-1.09
–2.91
-1.27
–1.18
-0.32
Tactical Asset Allocation
bill
–1.02
-0.64
–0.75
-0.5
0.37
-0.18
mat
–0.42
-0.62
1.19
-0.65
0.02
-0.13
def
3.19
-2.27
0.65
-2.17
0.7
-0.56
fx
1.26
-0.5
–0.05
-0.58
0.14
-0.14
irs
1.24
-0.58
–0.02
-0.61
0.32
-0.14
ey
0.35
-0.29
–0.67
-0.34
0.1
-0.08
mb
0.01
-0.03
0.08
-0.05
–0.01
-0.01
irw
–6.50
-11.5
–13.6
-10
4.19
-2.41
R2
6.1
[0.00]
16.8
[0.00]
13.1
[0.00]
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What about currency risk premium?
Currency specificiyy: zero-sum game
 Dumas-Solnik: currency risk premia exists.
It is time-varying and predictable

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Caveats:

Data snooping
– Foster, Smith and Whaley (98): by choosing to
max R2 via choice of instruments one can get
significance when there is none.
– Not clear how to use as list of instruments already
exists...

In-sample vs. Out-of-sample validation
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Caveats(2)
Statistical biases: autocorrelation,
heteroscedastisity (via Monte-Carlo
simulations).
 Non-normality, excess skewness and kurtosis

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How to deal with statistical issues?

Bootstrap methodology:
–
–
–
–
Form empirical distribution of returns
Generate time series of returns (length T).
Perform the regression of interest
See how many times there exists significance
on level a.
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U.S. Risk Premium
Survey Background

Graham/Harvey: Survey CFOs every quarter



Q2 2000 through Q4 2003 (15 quarters)
Current survey attracts about 400 respondents
Why CFOs?
– We know from previous surveys and interviews that the
CFOs use the risk premium for their capital budgeting
– Hence, they have thought hard about risk premium
– Should not be biased the way that analyst forecasts might
be
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U.S. Risk Premium
One-Year Premium

One-year risk premium variable. Currently, about 7%
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U.S. Risk Premium
Ten-Year Premium

Ten-year risk premium is stable. Currently, about 3.7%
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U.S. Risk Premium
Momentum in Expectations for 1-year
Premium
8
Mean one-year premium
7
6
5
4
y = 0.1912x + 3.8912
3
R2 = 0.5242
2
1
0
-15
-10
-5
0
5
10
15
Excess S&P 500 return in previous two months
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U.S. Risk Premium
Extreme Returns Cause Disagreement
A. Disagreement over the one-year premium and past returns
y = 0.0194x2 + 0.0247x + 3.3696
R2 = 0.5892
Disagreement over the one-year premium
6
5
4
3
y = -0.0614x + 3.9079
R2 = 0.1684
2
1
0
-15
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-5
0
Past one-month excess S&P 500 return
5
10
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U.S. Risk Premium
Positive Relation Between Disagreement
and Expected 10-year Returns
B. Ten-year premium and disagreement
8
Mean ten-year premium
7
6
5
4
3
2
y = 0.9777x + 1.5936
R2 = 0.3165
1
0
1.5
1.7
1.9
2.1
2.3
2.5
Disagreement of ten-year premium forecasts
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2.9
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U.S. Risk Premium
Example Confidence Intervals: September 16,
2002
95%
Confidence
Interval
Median Min
Mean
Standard
deviation
Over the next 10 years, I expect the average
annual S&P 500 return will be: There is a 1-in10 chance it will be less than:
3.65
2.35
3.40 - 3.89
4
Over the next 10 years, I expect the average
annual S&P 500 return will be: Expected return:
7.81
2.19
7.58 - 8.03
Over the next 10 years, I expect the average
annual S&P 500 return will be: There is a 1-in10 chance it will be greater than:
11.5
3.33
Over the next year, I expect the average
annual S&P 500 return will be: There is a 1-in10 chance it will be less than:
-2.98
Over the next year, I expect the average
annual S&P 500 return will be: Expected return:
Over the next year, I expect the average
annual S&P 500 return will be: There is a 1-in10 chance it will be greater than:
Max
Total
-3
10
351
8
0
15
373
11.15 - 11.84
11
4
20
355
6.86
-3.7 - -2.26
0
-20
10
348
4.95
2.78
4.66 - 5.24
5
0
12
345
9.96
4.56
9.47 - 10.44
10
0
20
343
Notes: 10-year bond yield 3.9%; 1-year bill yield 1.6%. Confidence interval based on standard deviation of the mean.
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Conclusion:
TAA can be an important tool in asset
allocation methodology.
 It is based on time variation of real
economic risk premia.
 Selection of predictors is important.
 We are still in ”top-down” paradigm.
 Devil is in the details= implementation
matters.

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