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

10/4/2015 Tactical Asset Allocation 2 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.

10/4/2015 Tactical Asset Allocation 3 What is TAA? Exists since early-to-mid- 80-ies. By now $ 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?

10/4/2015 Tactical Asset Allocation 4 Conditioning Information and Portfolio Analysis Er Vol Add conditioning information and weights change through time. Frontier shifts.

10/4/2015 Tactical Asset Allocation 5 Optimal portfolio for risk-averse investor

10/4/2015 Tactical Asset Allocation 6 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)

10/4/2015 Tactical Asset Allocation 7 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.

10/4/2015 Tactical Asset Allocation 8 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 ).

10/4/2015 Tactical Asset Allocation 9 Example: dividend yield May not be sustained out of sample

10/4/2015 Tactical Asset Allocation 10 Risk and return over the business cycle

US Term Structure Andrei Simonov - debt and money markets 11

10/4/2015 Andrei Simonov - debt and money markets 12 Evaluation of 2001 and 2008 Recessions In July 2000, the Yield Curve inverted forecasting recession to begin in June 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 On July 17, 2003 the NBER announced the official end of the recession was November In August 2006, the Yield Curve inverted forecasting recession to begin in July 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 On September 20, 2010 the NBER announced the official end of the recession was June 2009.

Recent recessions in retrospect Business cycleYield curve NBER Peak NBER Trough Legth of Cycle InversionLeadNormalLeadLength of Inversion Dec-69Nov 7011Oct-6814Feb Nov-73Mar-7516Jun-735Jan Jan-80Jul-806Nov-7814May Jul-81Nov-8216Oct-809Oct Jul-90Mar-918May-8914Feb Averages Mar-01Nov-018Jul-008Mar-0188 Dec-07June-0918Aug-0616May Andrei Simonov - debt and money markets 13

10/4/2015 Tactical Asset Allocation 14

10/4/2015 Tactical Asset Allocation 15

10/4/2015 Tactical Asset Allocation 16 Date 10 Year Treasury Yield 3 Month Treasury Yield (Bond Equivalent Basis)SpreadRec_prob 14-Apr % 14-May4.19% 14-Jun2.52% 14-Jul1.61% 14-Aug1.25% 14-Sep1.07% 14-Oct1.54% 14-Nov1.35% 14-Dec1.01% 15-Jan1.02% 15-Feb1.33% 15-Mar1.31% 15-Apr1.29%

June 2011 Meeting Outcomes Implied probability 0.0% % 0.50% 0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

June 2012 meeting outcome 18

August 2011 Meeting Outcomes Implied probability 0.0% % 0.50% 0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

September 2011 Meeting Outcomes Implied probability 0.0% % 0.50%0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

Duke survey: Pessimistic /Optimistic CFOs 10/4/2015 Tactical Asset Allocation 21

10/4/2015 Tactical Asset Allocation 22 Annual Real Economic Growth After Yield Curve Inversions

10/4/2015 Tactical Asset Allocation 23 Stock Returns and U.S. Yield Curve Average Monthly Returns in % Data through November 2000

10/4/2015 Tactical Asset Allocation 24 Average Monthly Stock Returns After Yield Curve Inversions Equally weighted Value weighted Based on 19 countries.

Trader’s calendar (from yahoo) 10/4/2015 Tactical Asset Allocation 25 Last WeekNext Week Date Time (ET) StatisticForActualBriefing ForecastMarket ExpectsPriorRevised From May 278:30 AMDurable OrdersApr0.8%-2.0%-1.3%3.6%2.9% May 278:30 AMDurable Goods -ex transportationApr0.1%-0.4%-0.2%2.9%2.4% May 279:00 AMCase-Shiller 20-city IndexMar12.4%12.0%11.8%12.9%- May 279:00 AMFHFA Housing Price IndexMar0.7%NA 0.6%- May 2710:00 AMConsumer ConfidenceMay May 287:00 AMMBA Mortgage Index05/24-1.2%NA 0.9%- May 298:30 AMInitial Claims05/24300K325K318K327K326K May 298:30 AMContinuing Claims05/172631K2650K 2648K2653K May 298:30 AMGDPGDP - Second EstimateQ1-1.0%-0.5% 0.1%- May 298:30 AMGDPGDP Deflator - Second EstimateQ11.3% - May 2910:00 AMPending Home SalesApr0.4%1.0% 3.4%- May 2910:30 AMNatural Gas Inventories05/24114 bcfNA 106 bcf- May 2911:00 AMCrude Inventories05/ MNA M- May 308:30 AMPersonal IncomeApr-0.3% 0.5%- May 308:30 AMPersonal SpendingApr-0.1%0.2%0.9%- May 308:30 AMPCEPCE Prices - CoreApr-0.2% - May 309:45 AMChicago PMIMay May 309:55 AMMichigan SentimentMichigan Sentiment - FinalMay

10/4/2015 Tactical Asset Allocation 26 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

10/4/2015 Tactical Asset Allocation 27 Do informational variables have 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

10/4/2015 Tactical Asset Allocation 28 Here how it looks like...

10/4/2015 Tactical Asset Allocation 29 Performance & Business Cycle Data through June 2002

10/4/2015 Tactical Asset Allocation 30 Performance & Business Cycle (2) Data through June 2002

10/4/2015 Tactical Asset Allocation 31 Performance & Business Cycle (3) Data through June 2002

10/4/2015 Tactical Asset Allocation Performance & Business Cycle (4) Data through June 2002

10/4/2015 Tactical Asset Allocation 33 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.

10/4/2015 Tactical Asset Allocation 34 So, what matters? ”Global only” model is already good enough Adding local factors increases explanatory power of the model

10/4/2015 Tactical Asset Allocation 35 Changes in  vs changes in risk premium Only 2-4% of variation is due to beta’s.

10/4/2015 Tactical Asset Allocation 36 What about currency risk premium? Currency specificiy: zero-sum game Dumas-Solnik: currency risk premia exists. It is time-varying and predictable

10/4/2015 Tactical Asset Allocation 37 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

10/4/2015 Tactical Asset Allocation 38 Caveats(2) Statistical biases: autocorrelation, heteroscedastisity (via Monte-Carlo simulations). Non-normality, excess skewness and kurtosis

10/4/2015 Tactical Asset Allocation 39 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 .

10/4/2015 Tactical Asset Allocation 40 U.S. Risk Premium Survey Background Graham/Harvey: Survey CFOs every quarter Q through Q (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

10/4/2015 Tactical Asset Allocation 41

10/4/2015 Tactical Asset Allocation 42

10/4/2015 Tactical Asset Allocation 43 Duke CFO magazine Global Business Outlook survey - U.S. - First Quarter, On February 12, 2010 the annual yield on 10-yr treasury bonds was 3.7%. Please complete the following: MeanSD95% CIMedianMinimumMaximumTotal 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: Over the next 10 years, I expect the average annual S&P 500 return will be: Expected return: 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: 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: 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-in-10 chance it will be greater than:

10/4/2015 Tactical Asset Allocation 44 U.S. Risk Premium Momentum in Expectations for 1-year Premium

10/4/2015 Tactical Asset Allocation 45 U.S. Risk Premium Extreme Returns Cause Disagreement

10/4/2015 Tactical Asset Allocation 46 U.S. Risk Premium Positive Relation Between Disagreement and Expected 10-year Returns

10/4/2015 Tactical Asset Allocation 47 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.