10/4/2015 Tactical Asset Allocation 1 Tactical Asset Allocation session 5 Andrei Simonov.

04/19/23Tactical Asset Allocation1

Tactical Asset Allocation Tactical Asset Allocation sessionsession 5 5

Andrei Simonov


AgendaAgenda

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.


What is TAA?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-than-benchmark 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?


Conditioning Information and Portfolio Conditioning Information and Portfolio AnalysisAnalysis

Er

Vol

Add conditioninginformation and weightschange through time. Frontier shifts.


Optimal portfolio for risk-averse investorOptimal portfolio for risk-averse investor

1V1

RV11R

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portfoliomin var

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Equilibrium and TAAEquilibrium and TAALet us assume that there exists long-term

expected returns vector e. However, due to predictability of asset returns, eE(R)

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portfoliomin var

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How to do it?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.


Example: Incredible January EffectExample: 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 ).


Example: dividend yieldExample: dividend yield

Fama-French (1988). 1927-1986 Holding period

Coeff. t(coeff) R2

M 0.21 1.40 0.00 Q 1.07 2.10 0.01

1 2.47 1.27 0.01 2 7.38 2.04 0.09 3 9.94 2.21 0.13 4 12.86 2.43 0.19

• May not be sustained out of sample


Risk and return over the business cycleRisk and return over the business cycle mtmtmttm RrRE var, ????

G-7 output output level

potential line

end. recess beg. expan end. expan beg. recess Average returns

15.23% 10.36% 6.96% 2.86%

Return volatility

12.59% 10.63% 16.85% 26.98%

US Term Structure 1970-1995US Term Structure 1970-1995

Andrei Simonov - debt and money markets11

04/19/23Andrei Simonov - debt and money markets12

Evaluation of 2001 and 2008 Evaluation of 2001 and 2008 RecessionsRecessions

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.

Recent recessions in retrospectRecent recessions in retrospectBusiness cycle Yield curve

NBER Peak

NBER Trough

Legth of Cycle

Inversion Lead Normal Lead Length of 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

Averages 11 11 7 15

Mar-01 Nov-01 8 Jul-00 8 Mar-01 8 8

Dec-07 June-09 18 Aug-06 16 May-07 12 9

Andrei Simonov - debt and money markets13




Date10 Year Treasury Yield

3 Month Treasury Yield (Bond Equivalent Basis) Spread Rec_prob

14-Apr 2.71 0.03 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%

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1/4 1/16 1/28 2/9 2/21 3/5 3/17 3/29 4/10 4/22 5/4

June 2011 Meeting OutcomesImplied probability

0.0% - 0.25%

0.50%

0.75%

Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

June 2012 meeting outcome

18

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

3/1 3/8 3/15 3/22 3/29 4/5 4/12 4/19 4/26 5/3 5/10

August 2011 Meeting OutcomesImplied probability

0.0% - 0.25%

0.50%

0.75%


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

4/1 4/6 4/11 4/16 4/21 4/26 5/1 5/6 5/11

September 2011 Meeting OutcomesImplied

probability

0.0% - 0.25%

0.50%

0.75%


Duke Duke survey: survey: Pessimistic /Pessimistic /Optimistic Optimistic CFOsCFOs



Annual Real Economic Growth After Annual Real Economic Growth After Yield Curve InversionsYield Curve Inversions

0.00%0.50%1.00%1.50%2.00%2.50%3.00%3.50%4.00%4.50%

Up to one year afterinversions

Other quarters


Stock Returns and U.S. Yield CurveStock Returns and U.S. Yield Curve

-0.5

0

0.5

1

1.5

2

2.5

3

AU AT BECA DK FR DE

HK IT JP NLNO SG ES SE CH

UK USW

O

Inversion Normal

Average Monthly Returns in %

Data throughNovember 2000


Average Monthly Stock Returns After Average Monthly Stock Returns After Yield Curve InversionsYield Curve Inversions

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

After first month ofinversion

Normal

Equally weighted

Value weighted

Based on 19 countries.

Trader’s calendar Trader’s calendar (from yahoo)(from yahoo)


Last Week Next Week

DateTime (ET)

Statistic For Actual Briefing Forecast Market Expects Prior Revised From

May 27 8:30 AMDurable Orders Apr 0.8% -2.0% -1.3% 3.6% 2.9%

May 27 8:30 AMDurable Goods -ex transportation Apr 0.1% -0.4% -0.2% 2.9% 2.4%

May 27 9:00 AMCase-Shiller 20-city Index Mar 12.4% 12.0% 11.8% 12.9% -

May 27 9:00 AMFHFA Housing Price Index Mar 0.7% NA NA 0.6% -

May 27 10:00 AMConsumer Confidence May 83.0 81.5 82.7 81.7 82.3

May 28 7:00 AMMBA Mortgage Index 05/24 -1.2% NA NA 0.9% -

May 29 8:30 AMInitial Claims 05/24 300K 325K 318K 327K 326K

May 29 8:30 AMContinuing Claims 05/17 2631K 2650K 2650K 2648K 2653K

May 29 8:30 AMGDP - Second Estimate Q1 -1.0% -0.5% -0.5% 0.1% -

May 29 8:30 AMGDP Deflator - Second Estimate Q1 1.3% 1.3% 1.3% 1.3% -

May 29 10:00 AMPending Home Sales Apr 0.4% 1.0% 1.0% 3.4% -

May 29 10:30 AMNatural Gas Inventories 05/24 114 bcf NA NA 106 bcf -

May 29 11:00 AMCrude Inventories 05/24 1.657M NA NA -7.226M -

May 30 8:30 AMPersonal Income Apr - 0.3% 0.3% 0.5% -

May 30 8:30 AMPersonal Spending Apr - 0.1% 0.2% 0.9% -

May 30 8:30 AMPCE Prices - Core Apr - 0.2% 0.2% 0.2% -

May 30 9:45 AMChicago PMI May - 60.0 60.3 63.0 -

May 30 9:55 AMMichigan Sentiment - Final May - 81.0 81.4 81.8 -


What variables matter?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


Do informational variables have Do informational variables have predictive ability?predictive ability? Info variables:

– January dummy

– Past excess return on Equally weighted CRSP index

– Spread between 1 and 3 mo T-bills

– Dividend yield

– Spread between Baa and Aaa corporate bonds

– 1-mo T-bill rate


Here how it looks like...


Performance & Business CyclePerformance & Business Cycle

-30

-20

-10

0

10

20

30

Expansion geometric mean Recession geometric mean

Average Annual Returns During U.S. Business Cycle Phases

Data through June 2002


Performance & Business Cycle (2)Performance & Business Cycle (2)

0

10

20

30

40

50

60

Australi

a

Austria

Belg

ium

Canad

a

Den

mar

k

Finlan

d

France

Ger

man

y

Hong K

ong

Irelan

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aly

Japan

Nether

lands

New

Zea

land

Norway

Portugal

Spain

Swed

en

Switzer

land

UK USW

orld

World

ex-U

S

EAFE

Expansion std.dev. Recession std.dev.

Average Annual Volatility During U.S. Business Cycle Phases



Performance & Business Cycle (3)Performance & Business Cycle (3)

-0.2

0

0.2

0.4

0.6

0.8

1

Australi

a

Austria

Belg

ium

Canad

a

Den

mar

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Finlan

d

France

Ger

man

y

Hong K

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aly

Japan

Nether

lands

New

Zea

land

Norway

Portugal

Spain

Swed

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Switzer

land

UK USW

orld

World

ex-U

S

EAFE

Expansion correlation with US Recession correlation with US

Correlations During U.S. Business Cycle Phases



3. Performance & Business Cycle (4)3. Performance & Business Cycle (4)

0

5

10

15

20

25

30

35

40

45

Australi

a

Austria

Belg

ium

Canad

a

Den

mar

k

Finlan

d

France

Ger

man

y

Hong K

ong

Irelan

d It

aly

Japan

Nether

lands

New

Zea

land

Norway

Portugal

Spain

Swed

en

Switzer

land

UK US

World

World

ex-U

S

EAFE

Expansion covariance with US Recession covariance with US

Covariances During U.S. Business Cycle Phases



How important are global factors?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 T-bills, Eurodollar/US treasury spread, lagged market return, January dummy.

Local informational variables: Country x div. Yield, 30-day t-bill rate, term spread, lagged MSCI country x market return.

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So, what So, what matters?matters?

”Global only” model is already good enough

Adding local factors increases explanatory power of the model


Changes in Changes in vs changes in risk premium vs changes in risk premium

Only 2-4% of variation is due to beta’s.

)()'(

)()'('

EZEVarE

EZEVarEZEVar


What about currency risk premium?What about currency risk premium?

Currency specificiy: zero-sum gameDumas-Solnik: currency risk premia

exists. It is time-varying and predictable


Caveats: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


Caveats(2)Caveats(2)

Statistical biases: autocorrelation, heteroscedastisity (via Monte-Carlo simulations).

Non-normality, excess skewness and kurtosis


How to deal with statistical issues?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 .


U.S. Risk PremiumU.S. Risk Premium

Survey BackgroundSurvey 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




Duke CFO magazine Global Business Outlook survey - U.S. - First Quarter, 201014. On February 12, 2010 the annual yield on 10-yr treasury bonds was 3.7%. Please complete the following:

Mean SD 95% CIMedianMinimumMaximum TotalOver the next 10 years, I expect the average annual S&P500 return will be: There is a 1-in-10 chance it will beless than: 1.30 8.13 0.61 - 1.99 2 -50 75 535Over the next 10 years, I expect the average annual S&P500 return will be: Expected return: 7.62 9.66 6.81 - 8.43 6 -20 100 544Over the next 10 years, I expect the average annual S&P500 return will be: There is a 1-in-10 chance it will begreater than: 11.76 11.43 0.79 - 12.72 10 -10 100 537Over the next year, I expect the average annual S&P 500return will be: There is a 1-in-10 chance it will be lessthan: -3.31 11.64 -4.30 - -2.33 0 -50 75 535Over the next year, I expect the average annual S&P 500return will be: Expected return: 5.62 8.44 4.91 - 6.33 5 -25 100 544Over the next year, I expect the average annual S&P 500return will be: There is a 1-in-10 chance it will begreater than: 11.39 8.81 10.65 - 12.14 10 -10 95 534



Momentum in Expectations for 1-year Momentum in Expectations for 1-year PremiumPremium



Extreme Returns Cause DisagreementExtreme Returns Cause DisagreementA. Disagreement over the one-year premium and past returns

y = -0.0614x + 3.9079R2 = 0.1684

y = 0.0194x2 + 0.0247x + 3.3696

R2 = 0.5892

0

1

2

3

4

5

6

-15 -10 -5 0 5 10

Past one-month excess S&P 500 return

Dis

agre

emen

t ove

r th

e on

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ar p

rem

ium



Positive Relation Between Disagreement Positive Relation Between Disagreement and Expected 10-year Returnsand Expected 10-year Returns

B. Ten-year premium and disagreement

y = 0.9777x + 1.5936R2 = 0.3165

0

1

2

3

4

5

6

7

8

1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9

Disagreement of ten-year premium forecasts

Mea

n te

n-ye

ar p

rem

ium


Conclusion: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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10/4/2015 Tactical Asset Allocation 1 Tactical Asset Allocation session 5 Andrei Simonov.

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