Portfolio construction

November 27, 2023

Historical returns since 1926 have averaged 10.5% for U.S. equities and 5.4% for U.S. bonds.^{1}

But at any given point in time, the average annual return from these asset classes—even when looking at a 10-year period—can be well below those figures. That was the case during the Great Depression, the mid-1960s through the 1970s, and from 1999 to 2009. The worst 10-year annualized returns bottomed out at about –5% for equities and 0% for bonds.

For investors seeking a certain level of return to meet their spending needs, such extended down periods can significantly reduce their odds of success.

Asset returns are hard to forecast over the short term, but tend to be more predictable over longer timeframes.

“I’d make the analogy that it’s impossible to know what the exact temperature will be tomorrow, but a given range can be expected based on the season,” said Victor Zhu, CFA, CAIA, a Vanguard senior investment strategist. “There are also ‘seasons,’ or expected ranges, for stock returns based on market valuations.”

In fact, our research shows that realized returns over a 10-year period are directly proportional to the valuations of assets at the beginning of the period. For example, when stock valuations are high, the chances are greater that stocks will perform below their historical average over the following decade. And for bonds, low 10-year trailing Treasury yields are an even stronger predictor that their returns over the coming decade may well turn out to be below their historical average.

“We’ve found that, while return forecasts will never be precise, using them to adjust a portfolio’s asset mix as they change over time can increase the probability of meeting a return target compared with maintaining a static portfolio,” said Ziqi Tan, CFA, a Vanguard investment strategist.

The figure below illustrates our approach. The right-hand side shows how the asset mix of a time-varying 60% stock/40% bond portfolio would have changed over time based on the evolution of 10-year projections for asset and sub-asset classes generated by our proprietary Vanguard Capital Markets Model®. The asset mix of a static 60/40 portfolio is shown on the left-hand side for comparison.

For the period shown in the figure, our projections led to an allocation to bonds that was higher than 40% early on, and then a shift to an allocation of greater than 60% stocks as their expected return from depressed levels increased, especially at the onset of the pandemic.

In this example, our time-varying portfolio would have produced a higher expected annualized total return than the static 60/40 portfolio in every year shown, with the outperformance per year ranging from 1 to 12 basis points (one basis point equals one one-hundredth of a percentage point).

That said, it is important to keep in mind that deviating from a static asset allocation involves model risk—the outperformance of a time-varying portfolio depends on the validity of the medium-term forecasts it’s based on, and that validity isn’t ultimately known until after the end of the forecast period.

**Notes: **Time-varying portfolio allocations were determined by the Vanguard Asset Allocation Model. The assets under consideration were U.S. equity, real estate investment trusts, developed market ex-U.S. equity, emerging market equity, U.S. aggregate bond, short-term and long-term Treasuries, U.S. credit bonds, and global ex-U.S. aggregate bonds. The policy benchmark is a 60% stock/40% bond portfolio in which U.S. equity equals 60% of total equity, developed market ex-U.S. equity takes 30% of total equity, and U.S. aggregate bonds equal 70% of total bond allocation across the portfolios. Vanguard Capital Markets Model® 10-year projections as of December 2017, June 2018, December 2018, June 2019, December 2019, June 2020, December 2020, June 2021, December 2021, June 2022, and December 2022 were used.

**Source: **Vanguard.

**IMPORTANT: The projections and other information generated by the Vanguard Capital Markets Model (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 VCMM are derived from 10,000 simulations for each modeled asset class. Results from the model may vary with each use and over time.**

While periodic portfolio adjustments can increase the expected likelihood of meeting a return target, other levers, including the desired return-target level and an investor’s risk profile, matter as well.

The table below summarizes the expected likelihood of success for a range of return-target levels and risk profiles as of December 2022. Not surprisingly, the lower the return target is, the greater the chance of success in meeting it. And regarding risk, the more an investor is willing to take on, the greater the chance of success.

It’s important, therefore, for investors to keep in mind that success in achieving a high return target will likely depend on accepting considerable investment risk.

**Notes:** The probability of meeting the return target is calculated based on 10,000 VCMM 10-year simulations as of December 2022.

**Source: **Vanguard.

“Many retirees rely on their investment portfolios to generate a stable stream of income (for example, 4% of their portfolio annually) in order to fulfill their spending needs,” said Brett Dutton, CFA, FSA, head of investment strategy and analysis at Vanguard. “And a nonprofit organization might have a capital project in view requiring a portfolio return over the medium term of 5%. These investors may, therefore, be less interested in trying to maximize return than in maximizing the probability that they will succeed in generating the return they have targeted.”

Our analysis shows that periodic portfolio adjustments to account for changes in market outlook can increase the odds of such investors achieving their return targets, even during extended periods of low returns.

- Default factor is a key differentiator for bond funds (article, issued November 2023)
- Actively managing negative convexity in muni bonds (article, issued September 2023)
- Munis, Fed policy, and negative convexity (article, issued August 2023)

All investing is subject to risk, including possible loss of principal. Be aware that fluctuations in the financial markets and other factors may cause declines in the value of your account. 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. Past performance is no guarantee of future results.

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.

CFA® is a registered trademark owned by CFA Institute.

CAIA® is a registered certification mark owned and administered by the Chartered Alternative Investment Analyst Association.

Contributors

Victor Zhu, CFA, CAIA

Ziqi Tan

Brett Dutton, CFA, FSA