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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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SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to increases in the Effective Federal Funds Rate (EFFR)? Using monthly values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during July 2000 (limited by availability of EFFR) through May 2018 (215 months), we find that: Keep Reading

Momentum Strategy, Value Strategy and Trading Calendar Updates

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 May 2018.

Preliminary Momentum Strategy and Value Strategy Updates

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 June 2018. For SACEMS, the top three positions are unlikely to change by the close. For SACEVS, allocations are very unlikely to change by the close.

SACEVS and SACEMS from a European Perspective

A European subscriber asked about the effect of the dollar-euro exchange rate on the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we each month adjust the gross returns for these strategies for the change in the dollar-euro exchange rate that month. We consider all strategy variations: Best Value and Weighted for SACEVS; and, Top 1, equally weighted (EW) Top 2 and EW Top 3 for SACEVS. We focus on SACEVS Best Value and SACEMS EW Top 3. We consider effects on four gross performance metrics: average monthly return; standard deviation of monthly returns; compound annual growth rate (CAGR); and, maximum drawdown (MaxDD). Using monthly returns for the strategies and monthly changes in the dollar-euro exchange rate since August 2002 for SACEVS and since August 2006 for SACEMS, both through April 2018, we find that: Keep Reading

Testing a Countercyclical Asset Allocation Strategy

“Countercyclical Asset Allocation Strategy” summarizes research on a simple countercyclical asset allocation strategy that systematically raises (lowers) the allocation to an asset class when its current aggregate allocation is relatively low (high). The underlying research is not specific on calculating portfolio allocations and returns. To corroborate findings, we use annual mutual fund and exchange-traded fund (ETF) allocations to stocks and bonds worldwide from the 2018 Investment Company Fact Book, Data Tables 3 and 11 to determine annual countercyclical allocations for stocks and bonds (ignoring allocations to money market funds). Specifically:

  • If actual aggregate mutual fund/ETF allocation to stocks in a given year is above (below) 60%, we set next-year portfolio allocation below (above) 60% by the same percentage.
  • If actual aggregate mutual fund/ETF allocation to bonds in a given year is above (below) 40%, we set next-year portfolio allocation below (above) 40% by the same percentage.

We then apply next-year allocations to stock (Fidelity Fund, FFIDX) and bond (Fidelity Investment Grade Bond Fund, FBNDX) mutual funds that have long histories. Based on Fact Book annual publication dates, we rebalance at the end of April each year. Using the specified actual fund allocations for 1984 through 2017 and FFIDX and FBNDX May through April total returns and April 1-year U.S. Treasury note (T-note) yields for 1985 through 2018, we find that: Keep Reading

SACEVS and SACEMS Performance by Calendar Month

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:

Keep Reading

Putting Strategic Edges and Tactical Views into Portfolios

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

Home Prices and the Stock Market

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

“Pulling the Goalie” Metaphor for Investors

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

Preliminary Momentum Strategy and Value Strategy Updates

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.

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