Strategic Allocation

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we consider also the Simple Asset Class ETF Value Strategy (SACEVS)? We focus on: (1) the “Best Value” version of SACEVS, which each month picks one of three exchange-traded funds (ETF) corresponding to the most undervalued of U.S. term, credit and equity risk premiums (or cash if none of the three premiums are undervalued); and, (2) the “EW Top 3” version of SACEMS, which each month equally weights the top three of nine ETFs/cash with the highest total returns over a specified lookback interval. Using monthly total returns for SACEVS Best Value asset selections since August 2002 and for SACEMS EW Top 3 asset selections since August 2006, all through March 2018, we find that:

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What is the best way to put strategic edges and tactical views into investment portfolios? In their March 2018 paper entitled “Model Portfolios”, Debarshi Basu, Michael Gates, Vishal Karir and Andrew Ang describe and illustrate a three-step optimized asset allocation process incorporating investor preferences and beliefs that is rigorous, repeatable, transparent and scalable. The three steps are: 

1. Select a benchmark portfolio matched to investor risk tolerance via simple combination of stocks and bonds. They represent stocks with a mix of 70% MSCI All World Country Index and 30% MSCI USA Index. They represent bonds with Barclays US Universal Bond Index. In their first illustration, they focus on 20-80, 60-40 and 80-20 stocks-bonds benchmarks, rebalanced quarterly.
2. Construct a strategic portfolio with the same expected volatility as the selected benchmark but generates a higher long-term Sharpe ratio by including optimized exposure to styles/factors expected to outperform the market over the long run. Key inputs are long-run asset returns and covariances plus a risk aversion parameter. In their first illustration, they constrain the strategic model portfolio to have the same overall equity exposure and regional equity exposures as the selected benchmark.
3. Add tactical modifications to the strategic portfolio by varying strategic positions based on short-term expected returns and risks. In their second illustration, they employ a 100-0 stocks-bonds benchmark consisting of 80% MSCI USA Net Total Return Index and 20% MSCI USA Minimum Volatility Net Total Return Index. The corresponding strategic portfolio reflecting long-term expectations is an equally weighted combination of value, momentum, quality, size and minimum volatility equity factor indexes. They specify short-term return and risk expectations based on four indicators involving: economic cycle variables; aggregate stock valuation metrics; factor momentum; and, dispersion of factor measures (such as difference in valuations between value stocks and growth stocks). They apply these indicators to underweight or overweight strategic positions using an optimizer. They rebalance these portfolios monthly. 

For their asset universe, they focus on indexes accessible via Exchanged Traded Funds (ETFs). Using monthly data for five broad capitalization-weighted equity indexes, six broad bond/credit indexes of varying durations and six style/factor (smart beta) equity indexes as available during January 2000 through June 2017, they find that: Keep Reading

Homes typically represent a substantial fraction of investor wealth. Are there reliable relationships between U.S. home prices and the U.S. stock market? For example, does a rising stock market stimulate home prices? Do falling home prices point to offsetting liquidation of equity positions. Do homes effectively diversify equity holdings? Using monthly levels of the non-seasonally adjusted S&P/Case-Shiller U.S. National Home Price Index (Home Price Index) and the S&P 500 Index during January 1987 through December 2018 (31 years), and annual median sales prices for existing homes from RealEstateABC.com and the National Association of Realtors spanning 1968 through 2017 (50 years), we find that: Keep Reading

Can sacrificing little goals satisfy bigger ones? In the March 2018 draft of their paper entitled “Pulling the Goalie: Hockey and Investment Implications”, Clifford Asness and Aaron Brown ponder when a losing hockey coach should pull the goalie as a metaphor for focusing on portfolio-level return and portfolio-level risk management. Based on statistical analysis of hockey scenarios and broad examples from investing, they conclude that: Keep Reading

We have updated monthly Simple Asset Class ETF Momentum Strategy (SACEMS) winners and associated performance data at “Momentum Strategy”. We have updated monthly Simple Asset Class ETF Value Strategy (SACEVS) allocations and associated performance data at “Value Strategy”. We have also updated performance data for the “Combined Value-Momentum Strategy”.

We have updated the “Trading Calendar” to incorporate data for March 2018.

The home page, “Momentum Strategy” and “Value Strategy” now show preliminary Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) positions for April 2018. For SACEMS, the top three positions could change by the close. For SACEVS, allocations are unlikely to change by the close.

The home page, “Momentum Strategy” and “Value Strategy” now show preliminary Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) positions for February 2018. For SACEMS, past returns for the first and second positions and for the third and fourth positions are close, such that rankings could change by the close. For SACEVS, allocations are unlikely to change by the close.

An anomaly in the source data surfaced this month. Returns for December 2017 for dividend-paying ETFs changed between the end of December 2017 and the end of January 2018. It appears that data available as of the December market close did not account for dividend ex-dates during December. This anomaly has two implications:

1. December 2017 returns previously reported for SACEMS and SACEVS (and alternatives using dividend paying ETFs) were too low. We are correcting these returns.
2. More seriously, incorporation of December 2017 dividends causes a change in the SACEMS top three winners for December 2017, which we determine based on total returns. Since the historical SACEMS performance we present is based on fully updated backtests, the data anomaly introduces a disconnect between backtest and live portfolio performances. In this case, the backtest performs better than a live portfolio. If this issue recurs, we will consider other data management approaches.

Recall the prior data instability reported in “Simple Asset Class ETF Momentum Strategy Data Changes”. Over the long run, data instability issues may cancel with respect to live portfolio performance.

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from the above set of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and rough gross annual Sharpe ratio (average annual return divided by standard deviation of annual returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through December 2017, we find that: Keep Reading

Subscribers have suggested an alternative approach for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) designed to suppress trading by holding past winners until they fall further in the rankings than in the baseline specification. SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

There are three versions of SACEMS: (1) top one of the nine ETFs (Top 1); (2) equally weighted top two (EW Top 2); and, (3) equally weighted top three (EW Top 3). To test the suggestion, we specify three “sticky” versions of SACEMS as follows:

1. Top 1 Sticky – retains the past winner until it drops out of the top 2.
2. EW Top 2 Sticky – retains past winners until they drop out of the top 3.
3. EW Top 3 Sticky – retains past winners until they drop out of the top 4.

We compare sticky and baseline strategies using the tabular performance statistics used for the baseline. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through December 2017, we find that:

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“SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). “SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 3 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through December 2017, we find that:

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