Valuations in focus

Bonds remain in favor in time-varying model portfolio

August 19, 2024

Vanguard’s time-varying asset allocation (TVAA) portfolio continued to have a much lower risk profile than its benchmark at the end of the second quarter. However, it has a marginally increased equity weighting compared with the first quarter due to improved valuations of U.S. value and non-U.S. stocks.

The equity allocation increased by 3 percentage points to 39% for the quarter ended June 30, 2024. The underperformance of U.S. value and ex-U.S. developed markets stocks has resulted in improved valuations. Our allocations to these categories, as well as to emerging markets equities, have increased by 1 percentage point each.

With this shift, our TVAA equity allocation is now 21 percentage points less than that of a market-capitalization-weighted benchmark portfolio with 60% equities and 40% bonds. “The time-varying portfolio is a reflection of an interest rate environment that remains positive for fixed income, while high valuations have left U.S. stocks’ equity risk premiums muted,” said Harshdeep Ahluwalia, Vanguard head of asset allocation for the Americas.

During the quarter, TVAA allocations to international bonds, U.S. intermediate credit bonds, and long-term U.S. Treasury bonds were reduced by 1 percentage point apiece because of the improvement in the outlook for U.S. value and non-U.S. equities.

Our TVAA is geared toward investors who are comfortable with model forecast risk, a type of active risk in which investors embrace our disciplined model for navigating changing market and economic environments.

A bar chart showing allocations to equities and fixed income in a benchmark 60/40 portfolio and Vanguard’s time-varying asset allocation. In the 60/40 portfolio, our 60% equity weighting is broken down to 36% U.S. and 24% international, and our 40% fixed income weighting is broken down to 28% U.S. and 12% international. In our time-varying asset allocation, equities account for 39% of the portfolio and fixed income 61%. The equity breakdown is 13% U.S. value factor, 5% U.S. growth factor, 5% U.S. small factor, 8% emerging markets equity, and 8% developed markets ex-U.S. equity. The fixed income breakdown is 2% U.S. aggregate bonds, 2% U.S. short-term Treasury bonds, 4% U.S. long-term Treasury bonds, 23% U.S. intermediate credit bonds, and 30% international bonds.

Notes: Time-varying portfolio allocations were determined by the Vanguard Asset Allocation Model (VAAM). The assets under consideration were U.S. and non-U.S. equities and fixed income, as well as real estate investment trusts (REITs), U.S. high-yield corporate bonds, and emerging markets equities, which were used to illustrate time-varying allocation not only within equities versus fixed income but also within sub-asset classes. See the notes that accompany VCMM forecasts for additional details on asset class indexes. A minimum home-bias constraint of 60% was applied for U.S. equities, and 70% was applied for U.S. fixed income. The allocation to non-U.S. equities would have been higher had there been no home-bias constraint, given the asset class’s higher expected return. VCMM 10-year projections as of June 30, 2024, were used. The sum of individual sub-asset class allocations may not total 100% because of rounding. 

Source: Vanguard calculations, as of June 30, 2024.

Notes: Vanguard calculations are based on portfolios optimized by the VAAM, using return projections from the VCMM. Sharpe ratio is a measure of return above the risk-free rate that adjusts for volatility. A higher Sharpe ratio indicates a higher expected risk-adjusted return. Expected maximum drawdown is the median peak-to-trough drop in the portfolio’s value in 10,000 VCMM simulations. The probability of underperforming the benchmark is in any given year. 

Source: Vanguard calculations, as of June 30, 2024.

IMPORTANT: The projections and other information generated by the VCMM regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. Distribution of return outcomes from the VCMM are derived from 10,000 simulations for each modeled asset class. Simulations as of June 30, 2024. Results from the model may vary with each use and over time.

Notes: All investing is subject to risk, including the possible loss of the money you invest. 

There is no guarantee that any particular asset allocation or mix of funds will meet your investment objectives or provide you with a given level of income.

Investments in bonds are subject to interest rate, credit, and inflation risk.

IMPORTANT: The projections and other information generated by the Vanguard Capital Markets Model regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. VCMM results will vary with each use and over time.

The VCMM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the VCMM. More important, the VCMM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based.

The Vanguard Capital Markets Model® is a proprietary financial simulation tool developed and maintained by Vanguard's primary investment research and advice teams. The model forecasts distributions of future returns for a wide array of broad asset classes. Those asset classes include U.S. and international equity markets, several maturities of the U.S. Treasury and corporate fixed income markets, international fixed income markets, U.S. money markets, commodities, and certain alternative investment strategies. The theoretical and empirical foundation for the Vanguard Capital Markets Model is that the returns of various asset classes reflect the compensation investors require for bearing different types of systematic risk (beta). At the core of the model are estimates of the dynamic statistical relationship between risk factors and asset returns, obtained from statistical analysis based on available monthly financial and economic data from as early as 1960. Using a system of estimated equations, the model then applies a Monte Carlo simulation method to project the estimated interrelationships among risk factors and asset classes as well as uncertainty and randomness over time. The model generates a large set of simulated outcomes for each asset class over several time horizons. Forecasts are obtained by computing measures of central tendency in these simulations. Results produced by the tool will vary with each use and over time.

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