Tactical Asset Allocation session 5 Andrei Simonov Tactical

Tactical Asset Allocation session 5 Andrei Simonov Tactical Asset Allocation 1 12/12/2021

Agenda l l l 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. Tactical Asset Allocation 2 12/12/2021

What is TAA? l l 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? Tactical Asset Allocation 3 12/12/2021

Conditioning Information and Portfolio Analysis Er Add conditioning information and weights change through time. Frontier shifts. Vol Tactical Asset Allocation 4 12/12/2021

Optimal portfolio for risk-averse investor Tactical Asset Allocation 5 12/12/2021

Equilibrium and TAA l Let us assume that there exists long-term expected returns vector e. However, due to predictability of asset returns, e E(R) Tactical Asset Allocation 6 12/12/2021

How to do it? We need a model that explains the connection between today’s variables and tomorrow returns. l Candidates: economic business cycle variables and Jan. Effect. l Tactical Asset Allocation 7 12/12/2021

Example: Incredible January Effect Excess returns associated with small firms w. r. t. Large-cap stocks l Ritter: Tax effect. Is it so? l Incredibly Shrinking January Effect (William J. Bernstein ). l Tactical Asset Allocation 8 12/12/2021

Example: dividend yield • May not be sustained out of sample Tactical Asset Allocation 9 12/12/2021

Risk and return over the business cycle Tactical Asset Allocation 10 12/12/2021

US Term Structure 1970 -1995 Andrei Simonov - debt and money markets 11

Evaluation of 2001 and 2008 Recessions l In July 2000, the Yield Curve inverted forecasting recession to begin in June 2001. l l 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. l l l 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 12 12/12/2021

Business cycle Yield curve Recent recessions in retrospect 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 Andrei Simonov - debt and money markets 13

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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% Tactical Asset Allocation 16 12/12/2021

June 2011 Meeting Outcomes Implied probability 0. 0% - 0. 25% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech 0. 75% 0. 50%

l June 2012 meeting outcome 18

August 2011 Meeting Outcomes Implied probability 0. 0% - 0. 25% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech 0. 75% 0. 50%

September 2011 Meeting Outcomes Implied probability 0. 0% - 0. 25% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech 0. 75% 0. 50%

Duke survey: Pessimistic /Optimistic CFOs Tactical Asset Allocation 21 12/12/2021

Annual Real Economic Growth After Yield Curve Inversions Tactical Asset Allocation 22 12/12/2021

Stock Returns and U. S. Yield Curve Average Monthly Returns in % Data through November 2000 Tactical Asset Allocation 23 12/12/2021

Average Monthly Stock Returns After Yield Curve Inversions Equally weighted Value weighted Based on 19 countries. Tactical Asset Allocation 24 12/12/2021

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 Date For Actual Briefing Forecast Market Expects Prior Revised From May 27 10: 00 AM Consumer Confidence May 28 May 29 7: 00 AM MBA Mortgage Index 8: 30 AM Initial Claims 8: 30 AM Continuing Claims 05/24 05/17 -1. 2% 300 K 2631 K NA 325 K 2650 K NA 318 K 2650 K 0. 9% 327 K 2648 K 326 K 2653 K May 29 8: 30 AM GDP - Second Estimate Q 1 -1. 0% -0. 5% 0. 1% - May 29 8: 30 AM GDP Deflator - Second Estimate Q 1 1. 3% - 0. 4% 1. 0% Last Week 10: 00 AM Pending Home Sales May 29 Apr Next 3. 4% Week - May 29 10: 30 AM Natural Gas Inventories 05/24 114 bcf NA NA 106 bcf - May 29 May 30 11: 00 AM Crude Inventories 8: 30 AM Personal Income 8: 30 AM Personal Spending 05/24 Apr 1. 657 M - NA 0. 3% 0. 1% NA 0. 3% 0. 2% -7. 226 M 0. 5% 0. 9% - 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 - Tactical Asset Allocation 25 12/12/2021

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 Tactical Asset Allocation 26 12/12/2021

Do informational variables have predictive ability? l 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 27 12/12/2021

l Tactical Asset Allocation Here how it looks like. . . 28 12/12/2021

Performance & Business Cycle Data through June 2002 Tactical Asset Allocation 29 12/12/2021

Performance & Business Cycle (2) Data through June 2002 Tactical Asset Allocation 30 12/12/2021

Performance & Business Cycle (3) Data through June 2002 Tactical Asset Allocation 31 12/12/2021

3. Performance & Business Cycle (4) Data through June 2002 Tactical Asset Allocation 32 12/12/2021

How important are global factors? l l Based on Ferson-Harvey RFS 95 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 10 yr 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. Tactical Asset Allocation 33 12/12/2021

So, what matters? ”Global only” model is already good enough l Adding local factors increases explanatory power of the model l Tactical Asset Allocation 34 12/12/2021

Changes in b vs changes in risk premium l Only 2 -4% of variation is due to beta’s. Tactical Asset Allocation 35 12/12/2021

What about currency risk premium? Currency specificiy: zero-sum game l Dumas-Solnik: currency risk premia exists. It is time-varying and predictable l Tactical Asset Allocation 36 12/12/2021

Caveats: l Data snooping – Foster, Smith and Whaley (98): by choosing to max R 2 via choice of instruments one can get significance when there is none. – Not clear how to use as list of instruments already exists. . . l In-sample vs. Out-of-sample validation Tactical Asset Allocation 37 12/12/2021

Caveats(2) Statistical biases: autocorrelation, heteroscedastisity (via Monte-Carlo simulations). l Non-normality, excess skewness and kurtosis l Tactical Asset Allocation 38 12/12/2021

How to deal with statistical issues? l 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. Tactical Asset Allocation 39 12/12/2021

U. S. Risk Premium Survey Background l Graham/Harvey: Survey CFOs every quarter l l l Q 2 2000 through Q 4 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 Tactical Asset Allocation 40 12/12/2021

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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% CIMedian. Minimum. Maximum 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 43 12/12/2021

U. S. Risk Premium Momentum in Expectations for 1 -year Premium Tactical Asset Allocation 44 12/12/2021

U. S. Risk Premium Extreme Returns Cause Disagreement Tactical Asset Allocation 45 12/12/2021

U. S. Risk Premium Positive Relation Between Disagreement and Expected 10 -year Returns Tactical Asset Allocation 46 12/12/2021

Conclusion: TAA can be an important tool in asset allocation methodology. l It is based on time variation of real economic risk premia. l Selection of predictors is important. l We are still in ”top-down” paradigm. l Devil is in the details= implementation matters. l Tactical Asset Allocation 47 12/12/2021