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Value Investing Strategy (Strategy Overview)

Allocations for July 2022 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for July 2022 (Final)
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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.

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

SACEVS Applied to Mutual Funds

“Simple Asset Class ETF Value Strategy” (SACEVS) 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 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:

As with ETFs, we consider two alternatives 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 monthly rebalanced portfolio of 60% VFINX and 40% VFIIX. Using monthly risk premium calculation data during March 1934 through November 2020 (limited by availability of T-bill data), and monthly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through November 2020 (40+ years, limited by VFIIX), we find that:

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Fed Model Improvement?

Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled “Towards a Better Fed Model”, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to the yield of 10-year constant maturity U.S. Treasury notes (T-note). The seven estimates of future equity returns are based on autocorrelation-corrected quarterly regressions using 10 years of past quarterly data for one of: (1) Aggregate Investor Allocation to Equities (AIAE); (2) Cyclically-Adjusted Price-to-Earnings Ratio (CAPE); (3) Tobin’s Q (QRATIO); (4) Market Capitalization-to-Nominal GDP (MC/GDP); (5) Market Capitalization-to-Adjusted Gross Value Added (MC/AGVANF); (6) Market Capitalization-to-Household and Non-Profit Total Assets (MC/HHNPTA); and, (7) Household and Non-Profit Equity Allocation-to-Nominal GDP (HHNPEQ/GDP). He calculates AIAE as total market value of equities divided by the sum of total market value of equities and total par value of bonds, approximated by adding the liabilities of five categories of borrowers. He then tests for each alternative a tactical asset allocation (TAA) strategy that each month weights equities and bonds based on a modified z-score of the forecasted 10-year equity risk premium (equity return minus T-note yield) computed by subtracting the median and dividing by the standard deviation of actual monthly premiums over the past 10 years. If modified z-score is greater than 1 (less than -1), the strategy is 100% in equities (0% in equities). In between those thresholds, weights are based on linear interpolation. Using quarterly data from the Archival Federal Reserve Economic Database (ALFRED) and Robert Shiller’s data library and daily U.S. equity market returns and U.S. Treasury bond/note roll-adjusted futures returns as available from the end of the fourth quarter of 1951 through the end of the third quarter of 2020, he finds that: Keep Reading

Overcharging for Target-Date Funds?

Target-date funds (TDFs) are popular fund-of-funds retirement investments that offer asset class diversification and periodic rebalancing aimed at a specific retirement year. TDFs typically charge layers of fees (fund-of-funds fee plus fees of underlying funds). Can investors do better themselves? In their October 2020 paper entitled “Off Target: On the Underperformance of Target-Date Funds”, David Brown and Shaun Davies assess feasibility and costs of emulating TDFs with low-cost exchange-traded funds (ETF) based on publicly disclosed TDF initial portfolio allocations and dynamic adjustments (glide paths). They identify TDFs as those mutual funds with one of 2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055 or 2060 in their names. They replicate TDFs by matching each of their holdings to the one of 50 Vanguard ETFs (as available when they reach $50 million in assets) with the highest full-sample monthly correlation of returns. Using monthly returns, holdings and expense ratios for TDFs and the funds they hold and monthly returns for the 50 Vanguard ETFs during January 2006 through December 2017, they find that:

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Market Impacts of Growth in Target Date Funds

Are aggregate periodic stocks-bonds rebalancing actions of Target Date Funds (TDF), which trade against momentum, increasingly affecting U.S. stock market dynamics? In their October 2020 paper entitled “Retail Financial Innovation and Stock Market Dynamics: The Case of Target Date Funds”, flagged by a subscriber, Jonathan Parker, Antoinette Schoar and Yang Sun examine market impacts of Target Date Funds (TDFs), assets of which have grown from less than $8 billion in 2000 to more than $2.3 trillion (of roughly $21 trillion in U.S. mutual funds) in 2019. Using quarterly data on TDF holdings, monthly U.S. stock market and Vanguard Total Bond Market Index Fund (bond market) returns and monthly data for stocks held by and similar to those held by TDFs during the third quarter of 2008 through the fourth quarter of 2018 (excluding three quarters with suspect data), they find that:

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Alternative Simplest Asset Class Momentum Strategies

In response to “Tech Premium Boost for Simplest Asset Class Momentum Strategy?”, a subscriber asked about testing the combination of Vanguard Growth Index Fund (VUG) and Vanguard Total Bond Market Index Fund (BND) in the “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPDR S&P 500 (SPY) or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. To investigate, we run a horse race between the strategy executed with SPY and TLT (SPY-TLT), the strategy executed with Invesco QQQ Trust (QQQ) and TLT (QQQ-TLT) and the requested alternative (VUG-BND). We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) as performance metrics and assess robustness across lookback intervals of one to 12 months. Using monthly dividend-adjusted prices for SPY, QQQ, VUG, TLT and BND during April 2007 (limited by BND) through September 2020, we find that:

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Breaking Asset Ranking Systems into Pairs

Is there a better way to identify attractive and unattractive assets than simply ranking them? In the August 2020 version of their paper entitled “Decoding Systematic Relative Investing: A Pairs Approach”, Christian Goulding, Campbell Harvey and Alex Pickard examine a long-short strategy that periodically reforms a portfolio by evaluating all possible pairs within an asset universe based on:

  1. High positive signal-future return correlation for each asset on its own in a pair.
  2. Low (or negative) signal correlation between assets in the pair.
  3. Low (or negative) signal-future return correlations between one asset and the other in the pair.

They use these three inputs to calculate a (somewhat complex) composite score for each pair. Among pairs with the highest composite scores, the member with the higher (lower) signal goes to the long (short) side of the portfolio. They assess usefulness of the three conditions and the composite score using a momentum signal calculated as average past monthly return over a specified lookback interval minus its inception-to-date mean and divided by its inception-to-date standard deviation. They split their sample roughly in half and use the first half for detection of profitable pair strategies and the second half to measure out-of-sample performance. They further test an explicit tactical allocation strategy using a 12-month momentum lookback interval, a rolling 10-year monthly composite score and a scheme that weights the top four asset pairs according to respective composite scores. As a benchmark, they use a comparable conventional relative momentum strategy that simply ranks assets on momentum signal. Using monthly returns for 13 broad asset-class indexes encompassing equities, bonds, real estate investment trusts (REIT) and commodities (78 possible pairs) as available through May 2020, they find that:

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Portfolio Reformation Schedule and Equity Factor Returns

Does equity factor portfolio reformation (rebalancing) schedule materially affect portfolio performance? In their February 2020 paper entitled “Rebalance Timing Luck: The (Dumb) Luck of Smart Beta”, Corey Hoffstein, Nathan Faber and Steven Braun measure rebalance timing luck (RTL) in returns for long-only portfolios of S&P 500 stocks selected based on:

  • Value – trailing 12-month earnings yield.
  • Quality – average of rankings for return on equity, accruals ratio (reverse ranking) and leverage ratio (reverse ranking).
  • Momentum – return from 12 months ago to one month ago.
  • Low Volatility – 12-month realized volatility.

They quantify RTL as dispersion in portfolio performance (best minus worst) across different reformation schedules. They also vary number of stocks (50 to 400) and portfolio reformation frequency (annual, semi-annual or quarterly) to assess RTL sensitivity to these parameters. For corroboration, they measure RTL for replications of existing S&P Dow Jones Enhanced Value, Quality, Momentum and Low Volatility indexes. Using data for S&P 500 stocks starting July 2000 and for factor-based indexes starting January 2001, all through September 2019, they find that:

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