Strategic Allocation

Does the conventional wisdom of a declining allocation to stocks throughout retirement really work best? In their September 2013 paper entitled “Reducing Retirement Risk with a Rising Equity Glidepath”, Wade Pfau and Michael Kitces explore alternative stocks-bonds allocations during retirement. They consider retirees planning for annual withdrawals of an inflation-adjusted 4% or 5% of retirement date assets over 20, 30 or 40 years. They consider three scenarios for future stocks/bonds return statistics (see the table below): (1) assumptions prepared for the MoneyGuidePro financial planning software as of July 2013; (2) a pessimistic scenario more closely calibrated to the current low-interest rate environment, but with an historical equity risk premium; and, (3) an optimistic scenario with stock and bond returns based on historical averages for 1926 through 2011. They assume year-end withdrawals and rebalancings of residual assets to target allocations, with withdrawals covering tax obligations. If a withdrawal pushes the account balance to zero, the portfolio fails. They also consider both the potential failure magnitude and upside potential. They consider 11 at-retirement equity allocations ranging from 0% to 100% in 10% increments gliding linearly to each of 11 at-horizon equity allocations ranging from 0% to 100% in 10% increments (a total of 121 glidepaths). Using outputs from 10,000 Monte Carlo simulations for each of the 121 glidepaths for each combination of withdrawal rate, retirement horizon and future return scenario, they find that: Keep Reading

Are stocks so attractive over the long run that they crowd bonds and cash out of the optimal portfolio? In their September 2013 paper entitled “Optimal Portfolios for the Long Run”, David Blanchett, Michael Finke and Wade Pfau relate optimal portfolio equity allocation to investment horizon worldwide to determine whether stocks universally exhibit time diversification (whereby mean reversion of returns causes equity risk to decrease as investment horizon lengthens). In calculating optimal equity allocation, they employ a utility function to model how investors feel about the risk of good and bad outcomes (not volatility as measured by standard deviation of returns). They consider different levels of investor risk aversion on a scale of 1 to 20, with 20 extremely risk averse. They measure returns for both overlapping and independent investment intervals of 1 to 20 years. They constrain portfolios to long-only positions in three assets: government bills (cash), government bonds and stock indexes. Using annual real returns to local investors in bills, bonds and stock indexes for 20 countries during 1900 through 2012, they find that: Keep Reading

Should long-term investors focus on terminal wealth and ignore interim volatility? In his August 2013 paper entitled “Rethinking Risk”, Javier Estrada compares distributions of terminal wealths for $100 initial investments in stocks or bonds over investment horizons of 10, 20 or 30 years. He utilizes mean, median, tail (extreme 1%, 5% and 10%) and risk-adjusted performance metrics. He employs real returns for 19 country markets adjusted by local inflation and in local currency for individual country markets, and adjusted by U.S. inflation and in dollars for the (capitalization-weighted) World market. Using real annual total returns for indexes of stocks and government bonds in each country during 1900 through 2009 (101, 91, and 81 overlapping intervals of 10, 20, and 30 years), he finds that: Keep Reading

Does trading based on simple moving average crossings reliably improve the performance of a portfolio diversified across asset classes? In the February 2013 update of his paper entitled “A Quantitative Approach to Tactical Asset Allocation”, Mebane Faber examines the effects of applying a 10-month simple moving average (SMA10) timing rule separately to each of the following five total return indexes a part of an equally weighted, monthly rebalanced portfolio: (1) S&P 500 Index; (2) 10-Year Treasury note constant duration index; (3) MSCI EAFE international developed markets index; (4) Goldman Sachs Commodity Index (GSCI); and, (5) National Association of Real Estate Investment Trusts index. Specifically, at the end of each month, he enters from cash (exits to cash) any index crossing above (below) its SMA10. Entry and exit dates are the same a signal dates (requiring some anticipation of signals before the close). The return on cash is the 90-day Treasury bill (T-bill) yield. Calculations ignore trading frictions and tax implications. Using monthly total return series for selected indexes mostly during 1972 through 2012, he finds that: Keep Reading

Do the relationships among returns for stocks and the most heavily traded commodities (gold and crude oil) consistently offer risk diversification? In their July 2013 paper entitled “Gold, Oil, and Stocks”, Jozef Baruník, Evzen Kocenda and Lukas Vacha analyze the return relationships among stocks ( the S&P 500 Index), gold and oil (light crude) over the past 26 years. Specifically, they test the degrees to which their prices: (1) co-move; (2) reliably lead one another; and, share any long-term relationships (such as ratios to which they revert). They seek robustness of findings by employing a variety of methods, data sampling frequencies and investment horizons. Using intraday and daily prices of the most active rolling futures contracts for the S&P 500 Index, gold and light crude oil during 1987 through 2012, they find that: Keep Reading

How can investors capture returns from widely accepted risk factors associated with asset classes and subclasses? In the June 2013 version of his book chapter entitled “Factor Investing”, Andrew Ang provides advice on capturing risk premiums associated with factors such as value, momentum, illiquidity, credit risk and volatility risk. Based on the body of research, he concludes that: Keep Reading

In the introduction to his 2013 book entitled The Alternative Answer: The Nontraditional Investments That Drive the World’s Best-Performing Portfolios, author Bob Rice (Alternative Investment Editor at Bloomberg Television) states that his: “…basic approach is an adaptation of the strategic asset allocation model that endowments have used for years, one that reflects two critical modifications. First, there is great focus on liquidity and inflation-protected income. Second, it incorporates the latest analysis regarding portfolio construction, specifically regarding accumulation of risk premiums and avoidance of cross-asset vulnerabilities. …Modern investors need modern tools. And they exist; it’s just that there’s been no reliable user’s guide. Now, I hope, there is.” Based on the practices of selected “elite” investors, he concludes that: Keep Reading

Does adding a proxy for private equity to a diversified portfolio improve its performance? To check, we add PowerShares Global Listed Private Equity (PSP) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for PSP and the average pairwise correlation of PSP monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without PSP. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for PSP and the above nine asset class proxies from November 2006 (first return available for PSP) through April 2013 (78 monthly returns), we find that: Keep Reading

A subscriber suggested testing the diversification power of SPDR Barclays International Real Estate (RWX) as a distinct asset class. To check, we add RWX to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for RWX and the average pairwise correlation of RWX monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without RWX. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for RWX and the above nine asset class proxies from April 2007 (first return available for RWX) through April 2013 (73 monthly returns), we find that: Keep Reading

Does adding a proxy for the currency carry trade among developed economies (long futures on three currencies with the highest interest rates and short futures on three currencies with the lowest interest rates) to a diversified portfolio improve its performance? To check, we add PowerShares DB G10 Currency Harvest (DBV) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for DBV and the average pairwise correlation of DBV monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without DBV. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for DBV and the above nine asset class proxies from September 2006 (first return available for DBV) through April 2013 (79 monthly returns), we find that: Keep Reading