Strategic Allocation

How has “A Quantitative Approach to Tactical Asset Allocation”, authored by Meb Faber in 2006 and published in The Journal of Wealth Management in 2007, performed post-publication? In his April 2025 paper entitled “Global Tactical Asset Allocation Updated Results and Real-Market Implementation Using Python and IBKR”, Carlo Zarattini revisits this influential strategy, which at the end of each month during 1972 through 2005 allocates 20% of funds to each of the following indexes when the index is above its 200-day simple moving average (SMA200):

1. S&P 500 Index
2. MSCI EAFE Index
3. U.S. 10-Year Government Bonds
4. Goldman Sachs Commodity Index (GSCI)
5. National Association of Real Estate Investment Trusts (NAREIT)

The allocation for any index below its SMA200 goes to 3-month U.S. Treasury bills (T-bill). He updates strategy performance and explores how different rebalancing cycles and frequencies affect outcome. He tests a tranche approach to mitigate the risk of picking an unlucky rebalancing cycle/frequency and imposes 0.1% index-cash switching frictions as a robustness test. He further provides a Python script to automate rebalancing through Interactive Brokers. Using daily levels of the five indexes and the T-bill yield to support a January 2006 through March 2025 strategy extension, he finds that:

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

We have updated the Trading Calendar to incorporate data for May 2025.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for June 2025. SACEMS rankings probably will not change by the close. SACEVS allocations are unlikely to change by the close.

A subscriber asked for an update of the 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 a strategy that is each week in stocks or bonds according to whether the return on lumber is greater than the return on gold over the past 13 weeks (L-G Strategy). To test the latter strategy we use the following exchanged-traded fund (ETF) proxies:

• SPDR S&P 500 (SPY) for stocks.
• iShares 20+ Year Treasury Bond (TLT) for bonds.
• Invesco MSCI Global Timber (CUT) for lumber.
• SPDR Gold Shares (GLD) for gold.

Using weekly dividend-adjusted prices for SPY, TLT, CUT and GLD during early February 2008 (limited by inception of CUT) through April 2025 and roughly matched start and stop performance for monthly SACEVS-SACEMS 50-50 , we find that:

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Do alternative investments (real estate, venture capital, private equity, hedge funds, private credit, infrastructure and energy investments) offer attractive returns and diversification, buffering investors from the vagaries of the stock market? In his March 2025 paper entitled “The Demise of Alternative Investments”, Richard Ennis examines contributions of alternative investments (alts) to pension fund and endowment portfolio net performances. He first looks at costs of investing in alts. He then relates public pension fund and university endowment portfolio alt allocations to their excess returns (relative to risk-matched simple portfolios of U.S. and non-U.S. stocks and investment-grade U.S. bonds). Using results of prior research, a sample of 50 large U.S. public pension funds for the 16 fiscal years ended June 2024 and 16 years of composite performance data for large university endowments from the National Association of College and University Business Officers, he finds that:

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Subscribers asked whether substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) improves outcomes. To investigate, we substitute monthly QQQ dividend-adjusted returns for SPY dividend-adjusted returns in the two model strategies. We then compare the modified performance with the original baseline performance, including: gross compound annual growth rates (CAGR) at various horizons, average gross annual returns, standard deviations of gross annual returns, gross annual Sharpe ratios and maximum drawdowns (MaxDD) based on monthly measurements. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using the specified methodology and data to generate SACEVS monthly returns starting August 2002 and SACEMS monthly returns starting July 2006, all through March 2025, we find that:

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Can large language models (LLM) help improve portfolio performance metrics, portfolio optimization and strategy feature discovery? In his three January-February 2025 papers entitled “AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics”, “AlphaPortfolio: Discovery of Portfolio Optimization and Allocation Methods Using LLMs” and “AlphaQuant: LLM-Driven Automated Robust Feature Engineering for Quantitative Finance”, Kamer Yuksel explores use of specially trained LLMs to discover new:

• Enhanced portfolio risk-return metrics that outperform traditional approaches such as Sharpe ratio.
• Better portfolio optimization methods.
• Robust investment strategy features.

The development processes are iterative for continuous improvement. He assesses usefulness of discoveries with 15 years of historical data for 3,246 US stocks and ETFs, of which he uses the last few years for out-of-sample equal-weighted portfolio testing. Using these methods and this dataset, he finds that:

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The “Simple Asset Class ETF Value Strategy” (SACEVS) 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 changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through February 2025, we find that: Keep Reading

Can investors use leveraged exchange-traded funds (ETF) to construct attractive versions of simple 60%/40% (60/40) and 40%/60% (40/60) stocks-bonds portfolios? In their March 2020 presentation package entitled “Robust Leveraged ETF Portfolios Extending Classic 40/60 Portfolios and Portfolio Insurance”, flagged by a subscriber, Mikhail Smirnov and Alexander Smirnov consider several variations of classic stocks/bonds portfolios implemented with leveraged ETFs. They ultimately focus on 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)

To validate findings, we consider this portfolio and several 60/40 and 40/60 stocks/bonds portfolios. We look at net monthly performance statistics, along with compound annual growth rate (CAGR), maximum drawdown (MaxDD) based on monthly data and annual Sharpe ratio. To estimate monthly rebalancing frictions, we use 0.5% of amount traded each month. We use average monthly 3-month U.S. Treasury bill yield during a year as the risk-free rate in Sharpe ratio calculations for that year. Using monthly adjusted prices for TQQQ, TMF, TLT and for SPDR S&P 500 ETF Trust (SPY) and Invesco QQQ Trust (QQQ) to construct benchmarks during February 2010 (limited by TQQQ inception) through February 2025, we find that: Keep Reading

A subscriber suggested adding U.S. large-capitalization growth and value funds to the asset class proxy universe in the Simple Asset Class ETF Momentum Strategy (SACEMS) to capture associated growth and value streakiness. To investigate, we select Vanguard Growth Index Fund ETF Shares (VUG) and Vanguard Value Index Fund ETF Shares (VTV) as growth and value proxies. We compare performance statistics for SACEMS Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios for:

1. SPY – Baseline SACEMS with SPDR S&P 500 ETF Trust (SPY) as a proxy for U.S. large-capitalization stocks.
2. SPY/VUG/VTV – SACEMS with VUG and VTV added.
3. VUG/VTV – SACEMS with VUG and VTV but without SPY to limit the overall tilt toward U.S. large-capitalization stocks.
4. VUG – SACEMS with VUG substituted for SPY to assess the benefit of growth alone versus value-growth switching.
5. QQQ – SACEMS with Invesco QQQ Trust (QQQ) substituted for SPY to compare VUG versus QQQ as large-capitalization growth stock proxies.

We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) based on monthly measurements and annual Sharpe ratio (with excess annual return calculated using average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year) as key performance statistics. Using monthly dividend-adjusted returns for baseline SACEMS assets, VUG, VTV and QQQ during February 2006 through February 2025, we find that:

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