Strategic Allocation

A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

1. Vanguard Total Stock Market Index Investor Shares (VTSMX)
2. Vanguard Small Capitalization Index Investor Shares  (NAESX)
3. Fidelity Diversified International (FDIVX)
4. Vanguard Long-Term Treasury Investor Shares (VUSTX)
5. Fidelity New Markets Income Fund (FNMIX)
6. Vanguard REIT Index Investor Shares (VGSIX)
7. First Eagle Gold A (SGGDX)
8. Oppenheimer Commodity Strategy Total Return A (QRAAX) until in October 2011, and BlackRock Commodity Strategies Portfolio Institutional Shares (BICSX) thereafter
9. 3-month U.S. Treasury bills (Cash)

We rank mutual funds based on total (dividend-adjusted) returns over past (lookback) intervals of one to 12 months. We consider portfolios of past mutual fund winners based on Top 1 and on equally weighted (EW) Top 2 through Top 5. We consider as benchmarks: an equally weighted portfolio of all mutual funds, rebalanced monthly (EW All); buying and holding VTSMX; and, holding VTSMX when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (VTSMX:SMA10). Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through April 2021, we find that: Keep Reading

How can retirees estimate whether their investment strategy will sustain all the withdrawals they expect to make in retirement? In his February 2021 paper entitled “The Sustainability of (Global) Withdrawal Strategies”, Javier Estrada  presents two tools to monitor retirement plans for early signs of trouble:

1. One evaluates sustainability of an existing withdrawal strategy.
2. The other calculates the sustainable level of inflation-adjusted withdrawals at any given point during retirement.

Both change over time according to (1) deviations of actual returns from those used in modeling the retirement portfolio and (2) the number of years left in retirement. He also illustrates these tools for a sample of 21 countries and the world over a 120-year period assuming a 30-year retirement with 30 annual withdrawals at the beginning of each year. The terminal value one year after the final withdrawal is the bequest. All returns and portfolio values are in real (inflation-adjusted) terms. Using annual real total returns for stocks and government bonds for 21 countries in local currencies/inflation rates and the world in U.S. dollars/inflation rate from the Dimson-Marsh-Staunton database during 1900 through 2019, he finds that: Keep Reading

A subscriber suggested review of the “SPY-TLT Universal Investment Strategy”, which each day allocates 100% of funds to SPDR S&P 500 (SPY) and/or iShares 20+ Year Treasury Bond (TLT) with SPY-TLT allocations equal to that with the best risk-adjusted daily performance over the past few months. There are 11 SPY-TLT allocation percentage choices: 100-0, 90-10, 80-20, 70-30, 60-40, 50-50, 40-60, 30-70, 20-80, 10-90 and 0-100. We test a simplified version of the strategy as follows:

1. Each trading day, calculate dividend-adjusted close-to-close SPY and TLT returns.
2. As soon as enough days are available, calculate the ratio of average daily return to standard deviation of daily returns over the past 63 trading days (about three months) for each of the 11 allocation choices. This lookback interval is common for such analyses and is within the lookback interval range of 50-80 days suggested by the author.
3. For each day thereafter, maintain a portfolio with SPY-TLT allocations equal to those of the winning allocation choice over the specified lookback interval. We consider both same-close (requiring slight anticipation of the winning allocation choice) and next-open rebalancing executions (because such anticipation appears problematic).

We ignore small rebalancing frictions incurred daily when the allocation does not change. We initially ignore rebalancing frictions when the allocation does change, but then perform a frictions sensitivity test. Using daily dividend-adjusted opening and closing prices for SPY and TLT during July 30, 2002 (limited by TLT) through April 20, 2021, we find that: Keep Reading

In the preface to the 2021 edition of his book, The Gone Fishin’ Portfolio: Get Wise, Get Wealthy…and Get on With Your Life, Alexander Green sets the following goal: “[S]how readers the safest, simplest way to achieve and maintain financial independence. …I’ll cover the investment basics and unite them in a simple, straightforward investment strategy that will allow you to earn higher returns with moderate risk, ultralow costs, and a minimal investment of time and energy. …Setting up the Gone Fishin’ Portfolio is a snap. Maintaining it takes less than 20 minutes a year.” Based on his 35 years of experience as an investment analyst, portfolio manager and financial writer, he concludes that:

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A subscriber asked about the effect of trading frictions on Simple Asset Class ETF Momentum Strategy (SACEMS) performance across potential momentum measurement (lookback) intervals, assuming 0.1% one-way frictions for buying and selling exchange-traded funds (ETF). To investigate, we look at the impact of these frictions on the SACEMS Top 1 portfolio, which each month holds the one ETF from the SACEMS universe with the highest past return. We consider lookback intervals ranging from one month to 12 months. We focus on compound annual growth rates (CAGR), since frictions have little impact on maximum drawdown (MaxDD). Using SACEMS monthly holdings and gross returns during February 2007 through March 2021, we find that: Keep Reading

How should financial advisers and investors approach retirement income planning? In their January 2021 paper entitled “A Model Approach to Selecting a Personalized Retirement Income Strategy”, Alejandro Murguia and Wade Pfau design and validate a questionnaire designed to quantify retirement income styles based on six preference scales:

1. Probability-based vs. Safety First (main) – depending on market growth vs. contractually promised.
2. Optionality vs. Commitment (main) – flexibility to respond to changing economic conditions/personal situation vs. fixed commitment.
3. Time-based vs. Perpetuity (secondary) – fixed horizon vs. indefinite retirement income.
4. Accumulation vs. Distribution (secondary) – portfolio growth vs. predictable income during retirement.
5. Front-loading vs. Back-loading (secondary) – higher income distributions during early retirement vs. consistent life-style throughout.
6. True vs. Technical Liquidity (secondary) – earmarked reserves/buffers vs. reserves taken from other goals.

The output is the Retirement Income Style Awareness (RISA)™ Profile. They then link profile types to four main retirement income strategies:

1. Systematic withdrawals with total return (conventional portfolio) investing.
2. Risk wrap with deferred annuities.
3. Protected income with immediate annuities.
4. Time segmentation or bucketing.

Based on the body of retirement investment research and survey feedback from 1,478 readers of RetirementResearcher.com, they conclude that: Keep Reading

A subscriber requested an assessment of U.S. federal capital gains tax impacts on the Simple Asset Class ETF Value Strategy (SACEVS), the Simple Asset Class ETF Momentum Strategy (SACEMS) and combinations of the two for investors with taxable accounts. Modeling such impacts is difficult due to complexity of the tax code and its highly idiosyncratic effects across individual taxpayers. To get a rough idea of federal tax impacts, we use annual (calendar year) data and make the following (close to worst case for SACEVS and SACEMS) simplifying assumptions:

• Federal taxes (income and capital gain) are per 2020 brackets.
• All SACEVS/SACEMS gains are short-term, treated as income at the marginal rate (some years not true for SACEVS, especially Best Value; generally true for SACEMS).
• All taxes for SACEVS and SACEMS are at marginal rates, debited annually, with losses carried forward and offsetting future gains until exhausted.
• Benchmarks are (1) buying and holding SPDR S&P 500 (SPY) and (2) a monthly rebalanced 60% SPY, 40% iShares 20+ Year Treasury Bond (TLT) portfolio. Benchmarks are essentially buy-and-hold, the latter because monthly deviations from target allocations are usually modest, with long-term capital gain tax debited only at the end of the sample period.

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance metrics. Using annual gross returns for SACEVS and the two benchmarks since 2003 and for SACEMS and SACEVS/SACEMS combinations since 2007, all through 2020, we find that:

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A subscriber asked for an extended test of a very simple momentum strategy that each month holds Vanguard 500 Index Fund Investor Shares (VFINX) or Vanguard Long-Term Treasury Fund Investor Shares VUSTX according to which of these funds has the highest total return over the last three months. To investigate, based on the way mutual funds report prices, we calculate past 3-month total returns using dividend-adjusted prices for month-ends and strategy returns using dividend adjusted prices for first days of the following month. We assume zero fund switching costs and no restrictions on monthly fund switching. We use buying and holding VFINX as a benchmark. Using the specified fund price series and monthly 3-month U.S. Treasury bill (T-bill) yield from the end of May 1986 (limited by VUSTX) through the beginning of March 2021, we find that: Keep Reading

Investors seeking low-cost, automated, tax-efficient and potentially alpha-generating solutions increasingly follow model portfolios of exchange-traded funds (ETF). Is there a top-down way to characterize those models? In their November 2020 paper entitled “Using Data Science to Identify ETF Model Followers”, Ananth Madhavan and Aleksander Sobczyk apply machine learning methods and cluster analysis to identify all models using at least three iShares ETFs based on monthly holdings data. Using monthly data on positions and accounts holding those positions across all iShares ETFs (370 at the end of the sample period) during January 2013 through June 2020, they find that:

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Can diversification across economic states improve portfolio performance? In their November 2020 paper entitled “Investing Through a Macro Factor Lens”, Harald Lohre, Robert Hixon, Jay Raol, Alexander Swade, Hua Tao and Scott Wolle study interactions between three economic “factors” (growth, defensive/U.S. Treasuries and inflation) and portfolio building blocks (asset classes and conventional factor portfolios). Their proxies for economic factors are: broad equity market for growth; U.S. Treasuries for defensive; and, spread between inflation-linked bonds and U.S. Treasuries for inflation. To diversify across economic states, they calculate historical performance of each portfolio building block during each of four economic regimes: (1) rising growth and rising inflation; (2) rising growth and falling inflation; (3) falling growth and rising inflation; and, (4) falling growth and falling inflation. They then look at benefits of adding defensive and inflation economic factor overlays to a classis 60%/40% global equities/bonds portfolio. Using monthly economic factor data and asset class/conventional factor portfolio returns during February 2001 through May 2020, they find that: Keep Reading