2. 8/28/2015 Tactical Asset Allocation 1 Tactical Asset Allocation session 5 Andrei Simonov

3. 8/28/2015 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.

4. 8/28/2015 Tactical Asset Allocation 3 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?

5. 8/28/2015 Tactical Asset Allocation 4 Conditioning Information and Portfolio Analysis Er Vol Add conditioning information and weights change through time. Frontier shifts.

6. 8/28/2015 Tactical Asset Allocation 5 Optimal portfolio for risk-averse investor

7. 8/28/2015 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)

8. 8/28/2015 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.

9. 8/28/2015 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. 8/28/2015 Tactical Asset Allocation 9 Example: dividend yield May not be sustained out of sample

11. 8/28/2015 Tactical Asset Allocation 10 Risk and return over the business cycle

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

13. 8/28/2015 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 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.

14. Recent recessions in retrospect Business cycleYield curve NBER Peak NBER Trough Legth of Cycle InversionLeadNormalLeadLength of Inversion Dec-69Nov 7011Oct-6814Feb-70916 Nov-73Mar-7516Jun-735Jan-75219 Jan-80Jul-806Nov-7814May-80218 Jul-81Nov-8216Oct-809Oct-811312 Jul-90Mar-918May-8914Feb-90139 Averages11 715 Mar-01Nov-018Jul-008Mar-0188 Dec-07June-0918Aug-0616May-07129 Andrei Simonov - debt and money markets 13

15. 8/28/2015 Tactical Asset Allocation 14 Yield Curve Inverts Before Last Six Recessions (5-year Treasury note minus 3-month Treasury bill yield-secondary) % Real annual GDP growth Yield curve Recession Correct 2 Recessions Correct Recession Correct Yield curve accurate in recent recession Recession Correct Annual GDP growth or Yield Curve % Data though April 11, 2006 Source: Campbell R. Harvey. Recent flattening

16. 8/28/2015 Tactical Asset Allocation 15 Yield Curve Inverts Before Last Six Recessions (5-year Treasury note minus 3-month Treasury bill yield – constant maturity) % Real annual GDP growth Yield curve Recession Correct 2 Recessions Correct Recession Correct Yield curve accurate in recent recession Recession Correct Annual GDP growth or Yield Curve % Data though April 11, 2006 Source: Campbell R. Harvey. Recent flattening

17. 8/28/2015 Tactical Asset Allocation 16 Recent Annualized One-Quarter GDP Growth (10-year and 5-year Yield Curves-secondary market) % Real annualized one-quarter GDP growth Annualized 1-quarter GDP growth Both curves invert 2000Q3 10-year 5-year Yield curve Data though April 11, 2006

18. 8/28/2015 Tactical Asset Allocation 17 Recent Annualized One-Quarter GDP Growth (10-year and 5-year Yield Curves-constant maturity) % Real annualized one-quarter GDP growth Annualized 1-quarter GDP growth Both curves invert 2000Q3 10-year 5-year Yield curve Data though April 2006

19. 8/28/2015 Tactical Asset Allocation 18

20. Current yield curve(May 23rd, 12) 8/28/2015 Tactical Asset Allocation 19

21. June 2011 Meeting Outcomes Implied probability 0.0% - 0.25% 0.50% 0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

22. June 2012 meeting outcome 21

23. August 2011 Meeting Outcomes Implied probability 0.0% - 0.25% 0.50% 0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

24. September 2011 Meeting Outcomes Implied probability 0.0% - 0.25% 0.50%0.75% Producer Price index (Apr); Retail Sales (Apr); Business Inventories (Mar); Bernanke Speech

25. Duke survey: optimism is up. Pessimistic /Optimistic CFOs 8/28/2015 Tactical Asset Allocation 24

26. 8/28/2015 Tactical Asset Allocation 25 Annual Real Economic Growth After Yield Curve Inversions

27. 8/28/2015 Tactical Asset Allocation 26 Stock Returns and U.S. Yield Curve Average Monthly Returns in % Data through November 2000

28. 8/28/2015 Tactical Asset Allocation 27 Average Monthly Stock Returns After Yield Curve Inversions Equally weighted Value weighted Based on 19 countries.

29. Trader’s calendar (from thestreet.com) 8/28/2015 Tactical Asset Allocation 28 Time (EST) Indicator (click for definition) Source (click for press release) ActualForecast Previous (revised) Previous (original) Monday, May 21 No releases. Tuesday, May 22 9 a.m. ICSC-UBS Weekly Chain Store Sales Snapshot for the week ended May 19 International Council of Shopping Centers and UBS -1.5%n.a.+0.8% 9 a.m. Johnson Redbook Retail Sales Index for the week ended May 19, vs. April Redbook Research+2.0%n.a.+2.2%+2.5%* Wednesday, May 23 9 a.m. Mortgage Applications Survey for the week ended May 18 -- Market Composite Index Mortgage Bankers Association --n.a.--675.5 Purchase Index--n.a.--432.3 9 a.m. Consumer Comfort Index for the week ended May 20 ABC News and Washington Post--n.a.---7 Thursday, May 24 8:30 a.m. Initial Jobless Claims for the week ended May 19 Labor Department --+305,000--+293,000 Four-week average--n.a.--+306,000 8:30 a.m. Durable goods orders for April Census Bureau --+0.9%--+3.7% Ex-transportation--n.a.--+1.5% 10 a.m. New home sales for AprilCensus Bureau--.860M--.858M 2:30 p.m. Treasury auction announcement Bureau of the Public Debt The Treasury announces the size of its next monthly two-year note auction, next Tuesday. Friday, May 25 10 a.m. Existing Home Sales for AprilNational Association of Realtors--6.10M--6.12M 10:30 a.m. Weekly Leading Index for the week ended May 18Economic Cycle Research Institute--n.a.--+6.1%

30. 8/28/2015 Tactical Asset Allocation 29 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

31. 8/28/2015 Tactical Asset Allocation 30 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

32. 8/28/2015 Tactical Asset Allocation 31 Here how it looks like...

33. 8/28/2015 Tactical Asset Allocation 32 Performance & Business Cycle Data through June 2002

34. 8/28/2015 Tactical Asset Allocation 33 Performance & Business Cycle (2) Data through June 2002

35. 8/28/2015 Tactical Asset Allocation 34 Performance & Business Cycle (3) Data through June 2002

36. 8/28/2015 Tactical Asset Allocation 35 3. Performance & Business Cycle (4) Data through June 2002

37. 8/28/2015 Tactical Asset Allocation 36 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.

38. 8/28/2015 Tactical Asset Allocation 37 So, what matters? ”Global only” model is already good enough Adding local factors increases explanatory power of the model

39. 8/28/2015 Tactical Asset Allocation 38 Changes in  vs changes in risk premium Only 2-4% of variation is due to beta’s.

40. 8/28/2015 Tactical Asset Allocation 39 What about currency risk premium? Currency specificiy: zero-sum game Dumas-Solnik: currency risk premia exists. It is time-varying and predictable

41. 8/28/2015 Tactical Asset Allocation 40 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

42. 8/28/2015 Tactical Asset Allocation 41 Caveats(2) Statistical biases: autocorrelation, heteroscedastisity (via Monte-Carlo simulations). Non-normality, excess skewness and kurtosis

43. 8/28/2015 Tactical Asset Allocation 42 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 .

44. 8/28/2015 Tactical Asset Allocation 43 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

45. 8/28/2015 Tactical Asset Allocation 44 U.S. Risk Premium Ten-Year Premium

46. 8/28/2015 Tactical Asset Allocation 45

47. 8/28/2015 Tactical Asset Allocation 46 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: 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:1.308.130.61 - 1.992-5075535 Over the next 10 years, I expect the average annual S&P 500 return will be: Expected return:7.629.666.81 - 8.436-20100544 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:11.7611.43 0.79 - 12.7210-10100537 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:-3.3111.64 -4.30 - -2.330-5075535 Over the next year, I expect the average annual S&P 500 return will be: Expected return:5.628.444.91 - 6.335-25100544 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:11.398.81 10.65 - 12.1410-1095534

48. 8/28/2015 Tactical Asset Allocation 47 U.S. Risk Premium Momentum in Expectations for 1-year Premium

49. 8/28/2015 Tactical Asset Allocation 48 U.S. Risk Premium Extreme Returns Cause Disagreement

50. 8/28/2015 Tactical Asset Allocation 49 U.S. Risk Premium Positive Relation Between Disagreement and Expected 10-year Returns

51. 8/28/2015 Tactical Asset Allocation 50 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.