Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for December 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for December 2022 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Effect of Trading Frictions on SACEMS

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

Retirement Income Planning Model

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

U.S. Federal Taxes and SACEVS, SACEMS

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:

Keep Reading

Longer Test of Simplest Asset Class ETF Momentum Strategy

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

Ascendance of Automated ETF Allocation Models

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:

Keep Reading

Diversifying across Growth/Inflation States of the Economy

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

Testing the 3-ETF Strategy

A subscriber asked for a performance comparison between 50% Simple Asset Class ETF Value Strategy (SACEVS) Best Value-50% Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted top two (EW Top 2), rebalanced monthly (SACEVS-SACEMS 50-50), and the following monthly rebalanced allocations to three exchange-traded funds (3-ETF):

Using monthly returns for SACEVS-SACEMS 50-50 and month-end dividend-adjusted prices for VTI, VXUS and BND during January 2011 (limited by inception of VXUS) through January 2021, we find that: Keep Reading

Update on Classic Portfolio Allocations with Leveraged ETFs

Can investors use leveraged exchange-traded funds (ETF) as building blocks for long-term portfolios? In his January 2021 presentation package entitled “One Year Later. Leveraged ETFs in Portfolio Construction and Portfolio Protection”, Mikhail Smirnov updates multi-year performance of a monthly rebalanced partially 3X-leveraged portfolio consisting of:

  • 40% ProShares UltraPro QQQ (TQQQ)
  • 20% Direxion Daily 20+ Year Treasury Bull 3X Shares (TMF)
  • 40% iShares 20+ Year Treasury Bond ETF (TLT)

The last three years are out-of-sample with respect to specification of this portfolio. He also looks at a more conservative portfolio of 20% TQQQ and 80% TLT, rebalanced monthly. Using pre-inception simulated and actual monthly total returns for these ETFs during January 1, 2005 through January 15, 2021, he finds that: Keep Reading

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 July 2006 for SACEMS, both through December 2020, we find that: Keep Reading

Combining an All Weather Portfolio, Crash Protection and Unemployment Trend

Is it possible to achieve an attractive return with very low risk from a small universe of exchange-traded funds (ETF)? In the January 2021 revision of his paper entitled “Lazy Momentum with Growth-Trend Timing: Resilient Asset Allocation (RAA)”, Wouter Keller constructs and tests the Resilient Asset Allocation (RAA) strategy as a more aggressive version of the Lethargic Asset Allocation (LAA) strategy. LAA switches between risky (equal-weighted  QQQ, IWD, IEF and GLD) and defensive (equal-weighted IWD, GLD, SHY and IEF) portfolios based on trend in the U.S. unemployment rate (bullish when above and bearish when below its 12-month simple moving average). RAA alters LAA in three ways:

  1. Changes the risky portfolio to an All Weather-like portfolio, with five equal-weighted assets (QQQ, IWN, IEF, TLT, GLD) instead of four and only two equal-weighted assets (IEF and TLT) as a risk-off portfolio.
  2. Adds “canary” crash protection from the Defensive Asset Allocation strategy for signaling the market trend with a fast filter (bearish when either or both of VWO or BND turn bad).
  3. Slows the unemployment rate trend signal, simply comparing recent unemployment rate with that of a year ago.

RAA is in the defensive portfolio only when both the canary and unemployment rate trend signals are bearish. Using a combination of modeled and (as available) actual monthly price data for the specified ETFs during December 1970 through November 2020, he finds that: Keep Reading

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