Strategic Allocation

Having iteratively compared the Simple Asset Class ETF Momentum Strategy (SACEMS) with a fixed lookback interval to SACEMS with a VIX-based variable lookback interval over several years, we are dropping the former and adopting the latter as the tracked baseline. We are making one other change to eliminate a legacy data workaround. Specifically: Keep Reading

One concern about simple momentum strategies is data snooping bias impounded in selection of a lookback interval to measure asset momentum. To circumvent this concern, we consider the following argument:

• The CBOE Volatility Index (VIX) broadly indicates the level of financial markets distress and thereby the tendency of investors to act complacently (when VIX is low) or to act in panic (when VIX is high).
• Complacency translates to resistance in changing market outlook (long memory and lookback intervals), while panic translates to rapid changes of mind (short memory and short lookback intervals).
• The inverse of VIX is therefore indicative of the actual aggregate current lookback interval affecting investor actions.

We test this argument by:

• Setting a range for VIX using monthly historical closes from January 1990 through December 2006, before the sample period used for most tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
• Applying buffer factors to the bottom (0.9) and top (1.1) of this actual inverse VIX range to recognize that it could break above or below the historical range in the future.
• Segmenting the buffer-extended inverse VIX range into 12 equal increments and mapping these increments by rounding into momentum lookback intervals of 1 month (lowest segment) to 12 months (highest segment).
• Use these increments to translate each future end-of-month inverse VIX level into a SACEMS lookback interval for that month.

We test the top one (Top 1), the equal-weighted top two (EW Top 2) and the equal-weighted top three (EW Top 3) SACEMS portfolios. We focus on compound annual growth rate (CAGR), maximum drawdown based on monthly measurements, annual returns and Sharpe ratio as key performance statistics. To calculate excess annual returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Benchmarks are these same statistics for tracked (baseline) SACEMS. Using monthly levels of VIX since inception in January 1990 and monthly dividend-adjusted prices of SACEMS assets since February 2006 (initial availability of a commodities ETF), all through August 2026, we find that: Keep Reading

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 August 2026.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for September 2026. Past returns for the top assets are closely bunched, so SACEMS rankings could change by the close. SACEVS allocations are unlikely to change by the close.

The four iShares Core Asset Allocation exchange-traded funds (ETF) offer exposures to U.S. stocks, global stocks and bonds semiannually rebalanced to fixed weights, as follows.

1. iShares Core Conservative Allocation (AOK) – 30% stocks and 70% bonds (30-70).
2. iShares Core Moderate Allocation (AOM) – 40% stocks and 60% bonds (40-60).
3. iShares Core Growth Allocation (AOR) – 60% stocks and 40% bonds (60-40).
4. iShares Core Aggressive Allocation (AOA) – 80% stocks and 20% bonds (80-20).

Each fund holds a portfolio of seven iShares Core stocks and bonds ETFs, thereby compounding management costs and fees. Do these funds of funds offer attractive performance? To investigate, we compare performance statistics for these funds with those for comparably weighted and rebalanced combinations of SPDR S&P 500 Trust (SPY) and iShares 20+ Year Treasury Bond (TLT), or SPY and iShares iBoxx $ Investment Grade Corporate Bond (LQD). We start tests at the end of December 2008 (about a month after inception of the asset allocation ETFs). We ignore semiannual rebalancing frictions for the SPY-TLT and SPY-LQD comparison strategies. Using semiannual dividend-adjusted prices for all specified funds during December 2008 through June 2026, we find that: Keep Reading

Has the strong shift in investor flows from active funds to passive funds unexpectedly contributed to a decline in performance of the former? In her June 2026 paper entitled “Passive Flows, Active Woes: Passive Investing and the Decline of Active Mutual Fund Alpha”, Hannah Unterberg studies whether the secular shift from active to passive investing (see the chart below) has depressed active fund performance because outflows force liquidation of active fund holdings. Using holdings and returns for a broad sample of active and passive U.S. equity mutual funds and exchange-traded funds during January 1984 through December 2024, she finds that:

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Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as retirement portfolio specification advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding exchange-traded fund (ETF) selections and weights:

For a hypothetical U.S. investor with moderate risk tolerance at each of ages 30, 40, 50, 60, 70 and 80, please provide your unique view on the three ETFs, with annually rebalanced fixed weights, that the investor should hold in a retirement portfolio over the next 10 years. Do not provide any explanation.

We then compare and contrast results from AI panel members. Using responses to the prompt as posed in early June 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as safe haven selection advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following prompt regarding 16 potential safe haven exchange-traded funds (ETF) from the list in “Best Safe Haven ETF?”:

Using all training and real-time data available to you, please provide your unique view of 16 ETFs that are potential safe havens during U.S. equity market crashes by ranking them from best safe haven to worst safe haven: XLU, TLT, IEF, SHY, BIL, LQD, AGG, TIP, VTIP, VNQ, GLD, SLV, DBC, USO, UUP, GBTC. Do not provide any explanations.

We then compare and contrast results from AI panel members. Using responses to the prompt as posed in early June 2026, we find that: Keep Reading

Is the evolving set of artificial intelligence (AI) platforms based on large language models interesting as asset class allocation advisors? Are they monolithic, or diverse? As a simple exploration, we pose to each of Grok, ChatGPT, Claude, Perplexity and Gemini the following two prompts regarding the 12 asset class exchange-traded funds (ETF) considered in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS):

1. Using all training and real-time data available to you, please provide your unique view on whether investing in each of the following ETFs is favorable, neutral or unfavorable for the balance of 2026: SPY, IWM, QQQ, EFA, EEM, TLT, LQD, BIL, EMB, VNQ, GLD and DBC. Do not provide any explanations.
2. Which three of your ETFs with favorable views work best together as a diversified set?

We then compare and contrast results from AI panel members. Using responses to the two prompts as posed in late May 2026, we find that: Keep Reading

Can artificial intelligence (AI) agents based on a large language model (LLM) carry most of the load in strategic asset allocation? In their April 2026 paper entitled “The Self-Driving Portfolio: Agentic Architecture for Institutional Asset Management”, Andrew Ang, Nazym Azimbayev and Andrey Kim present a 6-step strategic asset allocation system in which:

1. A macro agent identifies the economic regime (expansion, late-cycle, recession or recovery).
2. Asset class agents each assigned one class run in parallel to estimate respective expected returns, expected volatilities and confidence levels.
3. A covariance agent generates an asset class covariance matrix.
4. Portfolio construction agents each independently employ Step 2 and 3 outputs to proposed a portfolio based on an assigned method (such as equal weight, inverse volatility, mean-variance optimization or risk parity), including:
   • A researcher agent to propose novel portfolio construction methods.
   • An adversarial agent to uncover unconventional allocation ideas.
5. Multiple agents review all proposed portfolios and vote on them.
6. A chief investment officer agent scores, selects and combines surviving proposed portfolios using an ensemble of seven combination methods. This agent then summarizes a final recommendation/reasoning/dissenting views.

They include a meta-agent that compares forecasted and realized returns and rewrites agent scripts to improve future performance. They specify each agent in this system via a description, a set of scripts, a collection of skills and a structured output. An Investment Policy Statement (specifying asset class universe, objective, tracking error) constrains the AI agents. Overall, this system compresses days or weeks of human work into minutes. Based on prior research and experience with LLM-based AI agents, they observe that: Keep Reading