Tactical Asset Allocation
session 5
Andrei Simonov
Tactical Asset Allocation
1
4/13/2015
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 e 1T  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
output level
potential line
Average
returns
Return
volatility
end. recess beg. expan end. expan beg. recess
15.23%
10.36%
6.96%
2.86%
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12.59%
10.63%
16.85%
26.98%
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US Term Structure 1970-1995
11
Andrei Simonov - debt and money markets
Evaluation of 2001 and 2008
Recessions

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.
In August 2006 , the Yield Curve inverted forecasting recession
to begin in July 2007.



Official NBER Peak is December 2007 (Yield Curve within two quarters
accurate).
In May 2007, the Yield Curve returned to normal forecasting the end of
the recession in January 2008.
On September 20, 2010 the NBER announced the official end of the
recession was June 2009.
Andrei Simonov - debt and money markets
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Business cycle
Yield curve
Recent
recessions
in
retrospect
NBER
NBER
Legth of
Inversion Lead
Normal
Lead
Length of
Peak
Trough
Cycle
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
7
15
Averages
Inversion
11
11
Mar-01
Nov-01
8
Jul-00
8
Mar-01
8
8
Dec-07
June-09
18
Aug-06
16
May-07
12
9
13
Andrei Simonov - debt and money markets
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5 years and 3 month treasuries and spread (slope)
8
7
6
5
4
3
2
1
0
-1
GS3M
GS5
Spread
-2
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3 Month
Treasury Yield
(Bond
10 Year
Equivalent
Treasury Yield Basis)
Spread
Date
14-Apr
2.71
0.03
Rec_prob
2.68
5.38%
14-May
4.19%
14-Jun
2.52%
14-Jul
1.61%
14-Aug
1.25%
14-Sep
1.07%
14-Oct
1.54%
14-Nov
1.35%
14-Dec
1.01%
15-Jan
1.02%
15-Feb
1.33%
15-Mar
1.31%
15-Apr
1.29%
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June 2011 Meeting Outcomes
1.0
Implied probability
0.9
0.0% - 0.25%
0.8
0.7
0.6
Producer Price index (Apr); Retail
Sales (Apr); Business Inventories
(Mar); Bernanke Speech
0.5
0.4
0.3
0.75%
0.2
0.50%
0.1
0.0
1/4
1/16 1/28
2/9
2/21
3/5
3/17 3/29 4/10 4/22
5/4

June 2012 meeting outcome
18
August 2011 Meeting Outcomes
Implied probability
1.0
0.9
0.0% - 0.25%
0.8
0.7
0.6
Producer Price index (Apr); Retail
Sales (Apr); Business Inventories
(Mar); Bernanke Speech
0.5
0.4
0.3
0.75%
0.2
0.50%
0.1
0.0
3/1
3/8
3/15 3/22 3/29
4/5
4/12 4/19 4/26
5/3
5/10
September 2011 Meeting Outcomes
Implied probability
1.0
0.9
0.0% - 0.25%
0.8
0.7
0.6
Producer Price index (Apr); Retail
Sales (Apr); Business Inventories
(Mar); Bernanke Speech
0.5
0.4
0.3
0.2
0.75%
0.1
0.50%
0.0
4/1
4/6
4/11
4/16
4/21
4/26
5/1
5/6
5/11
Duke
survey:
Pessimistic
/Optimistic
CFOs
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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
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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
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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 yahoo)
May 27
Time
Statistic
(ET)
8:30 AM Durable Orders
Apr
0.8%
-2.0%
-1.3%
3.6%
2.9%
May 27
8:30 AM Durable Goods -ex transportation Apr
0.1%
-0.4%
-0.2%
2.9%
2.4%
May 27
9:00 AM Case-Shiller 20-city Index
Mar
12.4%
12.0%
11.8%
12.9%
-
May 27
9:00 AM FHFA Housing Price Index
Mar
0.7%
NA
NA
0.6%
-
83.0
81.5
82.7
81.7
82.3
-1.2%
300K
2631K
NA
325K
2650K
NA
318K
2650K
0.9%
327K
2648K
326K
2653K
Date
For
Actual Briefing Forecast Market Expects
Prior
Revised From
May 27
10:00 AM Consumer Confidence
May
May 28
May 29
May 29
7:00 AM MBA Mortgage Index
8:30 AM Initial Claims
8:30 AM Continuing Claims
05/24
05/24
05/17
May 29
8:30 AM GDP - Second Estimate
Q1
-1.0%
-0.5%
-0.5%
0.1%
-
May 29
8:30 AM GDP Deflator - Second Estimate Q1
1.3%
1.3%
1.3%
1.3%
-
0.4%
1.0%
1.0%
Next3.4%
Week
-
Last Week
10:00 AM Pending Home Sales
May 29
Apr
May 29
10:30 AM Natural Gas Inventories
05/24
114 bcf
NA
NA
106 bcf
-
May 29
May 30
May 30
11:00 AM Crude Inventories
8:30 AM Personal Income
8:30 AM Personal Spending
05/24
Apr
Apr
1.657M
-
NA
0.3%
0.1%
NA
0.3%
0.2%
-7.226M
0.5%
0.9%
-
May 30
May 30
8:30 AM PCE Prices - Core
9:45 AM Chicago PMI
Apr
May
-
0.2%
60.0
0.2%
60.3
0.2%
63.0
-
May 30
9:55 AM Michigan Sentiment - Final
May
-
81.0
81.4
81.8
-
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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
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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 lan
o d
Po rwa
rtu y
g
Sp al
Sw Swe ai n
itz 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 anc
H erm e
on a
g ny
K
Ire ong
lan
Ita d
N J ly
e
N t he apa
ew rl n
Ze and
a s
N land
or
Po wa
rtu y
g
Sp al
Sw Swe ai n
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
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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
lan
Ita d
N J ly
e
N t he apa
ew rl n
Ze and
a s
N land
or
Po wa
rtu y
g
Sp al
Sw Swe ai n
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 stria
lg
Ca ium
D na
en da
m
Fi ark
nl
Fr and
G an
H erm ce
on a
g ny
K
Ire ong
lan
Ita d
N J ly
e
N t he apa
ew rl n
Ze and
a s
N land
or
Po wa
rtu y
g
Sp al
Sw Swe ai n
itz den
er
lan
d
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
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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.
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What about currency risk premium?
Currency specificiy: 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 2008 (52 quarters)
Current survey attracts about 500 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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Duke CFO magazine Global Business Outlook survey - U.S. - First Quarter, 2010
14. On February 12, 2010 the annual yield on 10-yr treasury bonds was 3.7%.
Please complete the following:
Mean SD
Over the next 10 years, I expect the average annual S&P
500 return will be: There is a 1-in-10 chance it will be
less than:
1.30 8.13
Over the next 10 years, I expect the average annual S&P
500 return will be: Expected return:
7.62 9.66
Over the next 10 years, I expect the average annual S&P
500 return will be: There is a 1-in-10 chance it will be
greater than:
11.76 11.43
Over the next year, I expect the average annual S&P 500
return will be: There is a 1-in-10 chance it will be less
than:
-3.31 11.64
Over the next year, I expect the average annual S&P 500
return will be: Expected return:
5.62 8.44
Over the next year, I expect the average annual S&P 500
return will be: There is a 1-in-10 chance it will be
greater than:
11.39 8.81
Tactical Asset Allocation
95% CIMedianMinimumMaximum Total
0.61 - 1.99
2
-50
75
535
6.81 - 8.43
6
-20
100
544
0.79 - 12.72
10
-10
100
537
-4.30 - -2.33
0
-50
75
535
4.91 - 6.33
5
-25
100
544
10.65 - 12.14 10
-10
95
534
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U.S. Risk Premium
Momentum in Expectations for 1-year
Premium
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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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-10
-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
Tactical Asset Allocation
2.7
2.9
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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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