Strategic Allocation

Do simple stock market valuation ratios work for tactical allocation? In his April 2015 paper entitled “Multiples, Forecasting, and Asset Allocation”, Javier Estrada investigates whether investors can outperform a 60-40 stocks-bonds benchmark portfolio via tactical strategies based on one of three simple stock market valuation ratios: (1) dividend-price ratio (D/P); (2) price-earnings ratio (P/E); or, (3) cyclically adjusted price-earnings ratio (CAPE, or P/E10). The valuation‐based strategies take aggressive (conservative) stances when stocks are cheap (expensive) via combinations of the following rules:

• Designate stocks as cheap (expensive) when a valuation ratio is below (above) its inception-to-date mean by one standard deviation (1SD) or two standard deviations (2SD).
• Use 60-40 stocks-bonds allocations when stocks are not cheap or expensive. When stocks are cheap (expensive), shift toward stocks (bonds) by 20% to 80-20 (40-60) or by 30% to 90-10 (30-70). 
• Rebalance either annually or monthly.

For the benchmark portfolio and the valuation-based portfolios when in 60-40 stance, rebalancing occurs only when the stock allocation drifts below 55% or above 65%. To accrue at least 20 years of data for initial valuations, strategy performance measurements span 1920 through 2014 (95 years). Calculations lag dividends and earnings by three months to ensure real-time availability. Testing ignores trading frictions and tax implications. Using monthly S&P 500 Index total returns and the yield on 90-day U.S. Treasury bills (T-bills) during September 1899 through December 2014, he finds that: Keep Reading

Do multi-class mutual funds exhibit good asset class allocation timing? In their April 2015 paper entitled “Multi-Asset Class Mutual Funds: Can They Time the Market? Evidence from the US, UK and Canada”, Andrew Clare, Niall O’Sullivan, Meadhbh Sherman and Steve Thomas investigate whether mutual fund managers time allocations across asset classes skillfully. They focus on three asset classes: equities, government bonds and corporate bonds. They apply two alternative methodologies: (1) returns-based, relating each asset class beta for a fund to next-month return for that class; and, (2) holdings-based, relating changes in asset class weights within a fund to next-month class returns. Using monthly returns and holdings for 617 U.S., UK and Canadian multi-asset class mutual funds during 2000 through 2012, they find that:

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When and why do investors make changes in asset class allocations? In the March 2015 version of their paper entitled “Global Asset Allocation Shifts”, Tim Kroencke, Maik Schmeling and Andreas Schrimpf examine the asset reallocation decisions of U.S. mutual fund investors. They focus on shifts between U.S. equities and U.S. bonds (rotation) and between U.S. assets and non-U.S. assets (diversification). Specifically, they address: (1) principal factors explaining reallocations; (2) the link between monetary policy announcements and allocation shifts; and, (3) the search for bond yield and asset returns as drivers of allocation shifts. Using detailed U.S. mutual fund data on investor allocations to U.S. equities, non-U.S. equities and fixed income (comprising a total of about $6.6 trillion in assets) during January 2006 through December 2014, they find that: Keep Reading

Do variable retirement spending strategies offer greater utility than fixed-amount or fixed-percentage strategies? In his March 2015 paper entitled “Making Sense Out of Variable Spending Strategies for Retirees”, Wade Pfau compares via simulation ten retirement spending strategies based on a common set of assumptions. He classifies these strategies into two categories: (1) those based on decision rules (such as fixed real spending and fixed percentage spending); and, (2) actuarial models based on remaining portfolio balance and estimated remaining longevity. His bases comparisons on 10,000 Monte Carlo runs for each strategy. He assumes a retirement portfolio of 50% U.S. stocks and 50% U.S. government bonds with initial value $100,000, rebalanced annually after end-of-year 0.5% fees and beginning-of-year withdrawals. He calibrates initial spending where feasible by imposing a probability of X% (X=10) that real spending falls below $Y (Y=1,500) by year Z of retirement (Z=30). He treats terminal wealth as unintentional (in fact, undesirable), with the essential trade-off between spending more now and having to cut spending later. He ignores tax implications. Using historical return data from Robert Shiller and current levels of inflation and interest rates (see the chart below), he finds that: Keep Reading

In the introduction to his 2015 book entitled The 3% Signal: The Investing Technique that Will Change Your Life, author Jason Kelly states: “Ideas count for nothing; opinions are distractions. The only thing that matters is the price of an investment and whether it’s below a level indicating a good time to buy or above a level indicating a good time to sell. We can know that level and monitor prices on our own, no experts required, and react appropriately to what prices and the level tell us. Even better, we can automate the reaction because it’s purely mathematical. This is the essence of the 3 percent signal [3Sig]. …Used with common market indexes, this simple plan beats the stock market. …The performance advantage of the 3 percent signal can be yours after just four fifteen-minute calculations per year…” Based on his experience and analyses, he concludes that: Keep Reading

Why and how should investors pursue investment premiums associated with factors that explain performance differences among related assets (like common stocks)? In the January 2015 version of his paper entitled “Better Investing Through Factors, Regimes and Sensitivity Analysis”, Cristian Homescu summarizes recent research on: (1) factor-based investing; (2) enhancement of factor-based investing via regime switching models; and, (3) strategy robustness testing. Factor investing means systematic targeting of premiums associated with factors that explain an exploitable portion of return and risk differences among securities within one or several asset classes. Based on recent streams of research, he concludes that:

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A subscriber hypothesized that combining short-term reversal with intermediate-term momentum would enhance momentum strategy performance. To investigate, we test a modification of the “Simple Asset Class ETF Momentum Strategy”, which each month allocates all funds at the end of each month to the one of the following asset class exchange-traded funds (ETF) or Cash with the highest total return over the past five months (Top 1):

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)

The modification each month first identifies the top three ETFs or Cash based on past five-month returns and then picks the one of these three with the lowest return over the past five trading days (Top 3 Loser). This approach should pick intermediate-term winners that tend to benefit (or at least not suffer) from any reversal of short-term movements. Using daily and monthly dividend-adjusted closing prices for the asset class proxies and for SPDR S&P 500 (SPY) and the yield for Cash during February 2006 (when all ETFs are first available) through February 2015 (109 months), we find that: Keep Reading

Is the reward for holding risky bonds material and distinct from the reward for holding stocks and the reward for holding longer term bonds? In their February 2015 paper entitled “Credit Risk Premium: Its Existence and Implications for Asset Allocation”, Attakrit Asvanunt and Scott Richardson measure and explore the predictability and diversification power of the credit (or default) risk premium associated with corporate bonds. They focus on the premium associated with creditworthiness of bonds by first removing the influence of duration/interest rates. They also test whether the credit risk premium diversifies the equity risk premium and the bond term premium. Using data for U.S. corporate bonds, the U.S. stock market, U.S. Treasury securities and economic indicators during 1927 through 2014 and for credit default swaps (CDS) during 2004 through 2014, they find that: Keep Reading

Does optimal asset allocation, as measured by Sharpe ratio, depend on investment horizon? In their January 2015 paper entitled “Optimal Asset Allocation Across Investment Horizons”, Ronald Best, Charles Hodges and James Yoder explore the optimal (highest Sharpe ratio) mix of long-term U.S. corporate bonds and large-capitalization U.S. common stocks across investment horizons from one to 25 years. They test portfolios ranging from 100%-0% to 0%-100% stocks-bonds in 5% increments with annual rebalancing. They estimate annual returns for stocks and bonds based on 87 years of historical data. They simulate the portfolio return distribution for a given n-year holding period via 2,500 iterations for each of two methods:

1. Randomly select with replacement n years from the 87 years in the historical sample and use the annual returns for U.S. Treasury bills (T-bills, the risk-free rate), stocks and bonds for those n years in the order selected to calculate portfolio gross compound n-year excess returns. This method assumes year-to-year independence (zero autocorrelations) of annual returns for stocks and bonds, meaning no momentum or reversion.
2. Randomly select a year from the first 87 – (n-1) years in the historical sample and use the annual returns for T-bills, stocks and bonds for that and the next n-1 consecutive years to calculate portfolio gross compound n-year excess returns. This method preserves historical autocorrelations in return series.

Using annual returns for T-bills, U.S. large-capitalization common stocks and U.S. long-term corporate bonds during 1926 through 2012, they find that: Keep Reading

What is the best mix of stocks and bonds to hold during retirement worldwide? In his January 2015 paper entitled “The Retirement Glidepath: An International Perspective”, Javier Estrada compares outcomes for different stocks-bonds allocation strategies during retirement from a global perspective. He considers declining equity, rising equity and static glidepaths with an annual withdrawal rate of 4% (of the portfolio value at retirement) and annual rebalancing during a 30-year retirement period. He tests the following glidepaths:

• Four declining equity strategies that begin with 100%-0%, 90%‐10%, 80%‐20% and 70%‐30% stocks-bonds allocations and shift toward bonds linearly via annual rebalancing.
• Four mirror-image rising equity strategies that begin with 0%-100%, 10%-90%, 20%-80% and 30%-70% stocks-bonds allocations and shift toward stocks linearly via annual rebalancing.
• Eleven static allocations ranging from 100%-0% to 0%-100% stocks-bonds allocations maintained via annual rebalancing, with focus on conventional or near-conventional 60%-40%, 50%-50% and 40%-60% allocations.

He focuses on the failure rate of these strategies during 81 overlapping 30-year retirement periods during 1900-2009. He also considers average and median terminal wealth/bequest, tail risk, annual volatility (standard deviation of annual returns) and upside potential. He defines tail risk (downside risk) as average terminal wealth for the 1%, 5% or 10% lowest values from the 81 periods. Using annual total real returns for stocks and government bonds for 19 countries (in local currency adjusted by local inflation) and for the world market (in dollars adjusted by U.S. inflation) during 1900 through 2009 (110 years), he finds that: Keep Reading