Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

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Effects of Execution Delay on Simple Asset Class ETF Value Strategy

“Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy” investigates how delaying signal execution affects strategy performance. How does execution delay affect the performance of the complementary Best Value version of the “Simple Asset Class ETF Value Strategy”? This latter strategy each quarter allocates all funds to the one of the following asset class exchange-traded funds (ETF) associated with the most undervalued risk premium (term, credit or equity), or to cash if none are undervalued:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

To investigate, we compare 23 variations of the strategy that all use end-of-quarter (EOQ) to determine the best value asset but shift execution from the contemporaneous EOQ to the next open or to closes over the next 21 trading days (about one month). For example, an EOQ+5 Close variation uses an EOQ cycle to determine winners but delays execution until the close five trading days after EOQ. Using daily dividend-adjusted opens and closes for the risk premium proxies and the yield for Cash from the end of September 2002 through the end of March 2015 (51 quarters), we find that:

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Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy

“Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?” investigates whether using a monthly cycle other than end-of-month (EOM) to determine the winning asset improves performance of the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

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)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of the strategy that all use EOM to determine the winning asset but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, an EOM+5 Close variation uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash from the end of July 2002 (or inception if not available then) through the end of March 2015 (153 months), we find that: Keep Reading

Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?

As explored for a 10-month simple moving average (SMA) in “Optimal Cycle for Monthly SMA Signals?”, subscribers have inquired whether there is a best time of the month for measuring momentum in the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

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)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. Using daily dividend-adjusted closes for the asset class proxies and the yield for Cash from late July 2002 (or inception if not available then) through early April 2014 (about 153 months), we find that: Keep Reading

Timing of Asset Class Allocations by Multi-class Funds

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 U.S. Mutual Fund Investors Reallocate

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

Comparison of Variable Retirement Spending Strategies

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

A Few Notes on The 3% Signal

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

SACEMS-SACEVS Mutual Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we relate quarterly returns for the SACEVS Best Value and the SACEMS Top 1 exchange-traded fund (ETF) selections and look at the performance of an equally weighted portfolio of these two strategies (50-50). Using quarterly gross returns for SACEVS Best Value and SACEMS Top 1 during January 2003 through December 2014, we find that: Keep Reading

Simple Asset Class Value Strategy Applied to Mutual Funds

“Simple Asset Class ETF Value Strategy” finds that investors may be able to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). However, the backtesting period is limited by available histories for ETFs and for the series used to estimate risk premiums. To construct a longer test, we make the following substitutions for potential holdings (selected for length of available samples):

To enable estimation of risk premiums over a longer history, we also substitute:

We retain quarterly average yields for Moody’s Seasoned Baa Corporate Bonds for calculation of the credit risk premium. As with ETFs, we consider two alternative strategies for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a quarterly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). Using quarterly risk premium calculation data during January 1934 through December 2014 (limited by availability of Moody’s Baa data), and quarterly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through December 2014 (139 quarters), we find that:

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Simple Asset Class ETF Value Strategy

Does a simple relative value strategy applied to tradable asset class proxies produce attractive results? To investigate, we test a simple strategy on the following three asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

This set of ETFs relates to three factor risk premiums: (1) the difference in yields between Treasury bills and Treasury note/bonds indicates the term risk premium; (2) the difference in yields between corporate bonds and Treasury notes/bonds indicates the credit (default) risk premium; and, (3) the difference in yields between equities and Treasury notes/bonds indicates the equity risk premium. We consider two alternative strategies for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a quarterly rebalanced portfolio of 60% stocks and 40% U.S. Treasury notes (60-40 SPY-IEF). Using quarterly S&P 500 Index levels and earnings, quarterly average yields for 3-month Constant Maturity U.S. Treasury bills (T-bills), quarterly average yields for 10-year Constant Maturity U.S. Treasury notes (T-notes), quarterly average yields for Moody’s Seasoned Baa Corporate Bonds during March 1989 through December 2014 (limited by availability of earnings data), and quarterly dividend-adjusted closing prices for the above three asset class ETFs during September 2002 through December 2014 (45 quarters, limited by availability of IEF and LQD), we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for April 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
15.1% 15.8%
Top 3 ETFs SPY
15.3% 7.7%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 2nd Quarter 2015 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
13.7% 9.6% 8.6%
Strategy Overview
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