Perspectives : Investment | August 09, 2023

Meaningful vs. marketing: Assessing changes in glide-path design

Read time: 8 minutes

As target-date funds (TDFs) continue to grow and become a critical component of the retirement savings landscape, the need for a systematic, empirical approach to building, revalidating, and evolving glide paths is vital.

We see providers make marginal adjustments to their TDF series glide path or sub-asset allocation that may seem innovative and impactful on the surface, but we find that the actual effect of these changes on investors’ retirement outcomes is often negligible at best. We believe that some of the changes we’ve seen across the industry will not result in meaningful impact with respect to achieving long-term retirement success.

Any change Vanguard makes to its glide path represents our belief in that change having a consistent, positive impact on investor outcomes. In this article, we outline how we assess potential changes to our glide path and sub-asset allocation to separate what’s meaningful from what’s simply marketing.

How we define success for a TDF?

Before diving in, though, it is important to understand the intent and objective of a TDF as well as the typical end investor.

TDFs are designed to help address a particular challenge facing many retirement investors: constructing a professionally built portfolio that has an appropriate mix of higher- and lower-risk assets given the investor’s time horizon, retirement goals, and other considerations. We select the optimal glide path by assessing the trade-offs between the expected lifetime spending that a glide path will fund and the uncertainty of market risk toward that spending. Simply put, the goal of a TDF is to help give investors the best chance of building up enough wealth during their working years so that they have the income needed to maintain their lifestyle in retirement.

Additionally, we believe it is critical to understand the characteristics and behaviors of the typical end investor. In the case of a TDF, the end investor is usually someone who has been defaulted into an investment plan or who has a default mindset. These two inputs function as a starting point that allows us to evaluate potential changes to the Vanguard glide path.

Assessing impact on investor outcomes: Quantitative and qualitative perspectives


With the objective and typical end investor of a TDF in mind, we then move to examining potential changes through both quantitative and qualitative lenses. The quantitative metrics allow us to better determine whether a change to the glide path provides meaningful improvements to investor outcomes and consistent net-of-fees value. The qualitative metrics let us go a step further and consider the characteristics and preferences of the end investor.

Questions to consider for a glide-path change:

1. What is the impact on long-term investment outcomes?

2. Does it add consistent value net of fees, and what is the measure of value?

3. Is the position sized appropriately to have a meaningful impact?

4. Will investors understand the change?

Looking first through a quantitative lens, the Vanguard Life-Cycle Investing Model (VLCM) provides a robust framework for assessing any potential changes or additions to the glide path. The VLCM generates two goals-based metrics—certainty fee equivalent (CFE) and probability of success—which together provide relative and client-centric benchmarks. Figure 1 shows how the use of the VLCM enables cost-benefit analysis of glide-path customization by inputting an investor’s specific characteristics and retirement goals to evaluate and choose the optimal glide path for that investor.

Using VLCM calculations, we quantify the benefits of an optimized glide path through the CFE or “equivalence payment.” This is the annual fee (in basis points) that an investor is willing to pay relative to one glide path over another. The higher the equivalence fee, the greater the benefit of striking the right risk-return balance that aligns with the investor’s goal. We use this metric to provide an additional frame of comparison between our TDFs and other potential glide-path options.

Figure 1
Source: Vanguard.

Next, we calculate a distribution of outcomes related to the projected wealth and spending of an investor by modeling returns under different market scenarios. We also layer expectations regarding the investor’s earnings and replacement ratio to determine their retirement spending expectations. We use this information to calculate the probability of success, which is the likelihood that the TDF will match or exceed the investor’s expected spending goals in retirement. We also use the VLCM framework to assess the impact on probability of success of certain investor characteristics relative to sub-asset-class decisions.

Overall, as Figure 2 shows, sub-asset allocation changes, such as adding commodities or increasing credit exposure, tend to have a much lower impact on the probability of success than changes in participant characteristics, such as increasing the saving rate, lowering the replacement ratio (spending less), or delaying retirement. We look for changes that exhibit a high CFE benefit (which we define as >10 bps) and a meaningful improvement to the probability of success relative to the existing glide path and underlying sub-asset allocation.

Two additional factors play a role in our quantitative assessment of a change: the expected size of the position and the implementation costs associated with making and managing the change over time. Oftentimes, we see asset classes or portfolio tilts included in a TDF that are too small to make any meaningful impact on long-term results. If the change is truly impactful, it should be sized accordingly. Additionally, by including real-world projected implementation costs, we can better assess how these costs might erode the projected value that we see in our modeled simulations.

Figure 2:  The impact of select changes on probability of success

Note: This chart shows the impact of each population characteristic changing from low (25th percentile of broad population data) to medium (50th percentile). Vanguard Capital Markets Model® simulations are as of December 2019.

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. For more information, please see the important information section.

Lastly, we also assess the qualitative impact of changes or additions to the glide path. This is to ensure that each change meets the criteria that Vanguard believes are crucial in evaluating investments for use in TDFs—especially for TDFs that are part of a qualified default investment alternative (QDIA). These considerations include the potential for increased complexity, lack of transparency, and reduced liquidity.

A high bar for change: Considerations for asset allocations adjustments
Source: Vanguard.

Meaningful changes for improved outcomes

At Vanguard, we aim to take a thoughtful, measured approach to any changes we make to our TDF glide path. Since the series inception in 2003, we have adjusted our glide path and sub-asset allocation only five times. This is not due to a lack of research. In fact, it is quite the opposite, as we have analyzed virtually every asset class and exposure over the years. Instead, it is a recognition that TDFs, given their typical use as a QDIA, simply require a high bar for change. This is why we rely on multiple metrics that are quantitative, qualitative, and practical to ensure that any changes we make are meaningful and provide actual long-term value for our investors.


  • For more information about Vanguard funds, visit or call 800-662-2739 to obtain a prospectus or, if available, a summary prospectus. Investment objectives, risks, charges, expenses, and other important information about a fund are contained in the prospectus; read and consider it carefully before investing.
  • Investments in Target Retirement Funds are subject to the risks of their underlying funds. The year in the fund name refers to the approximate year (the target date) when an investor in the fund would retire and leave the workforce. The fund will gradually shift its emphasis from more aggressive investments to more conservative ones based on its target date. An investment in a Target Retirement Fund is not guaranteed at any time, including on or after the target date.
  • 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. Diversification does not ensure a profit or protect against a loss.
  • Vanguard is responsible only for selecting the underlying funds and periodically rebalancing the holdings of target-date investments. The asset allocations Vanguard has selected for the Target Retirement Funds are based on our investment experience and are geared to the average investor. Regularly check the asset mix of the option you choose to ensure it is appropriate for your current situation.
  • Investments in bonds are subject to interest rate, credit, and inflation risk.
  • The Vanguard Life-Cycle Investing Model (VLCM) is designed to identify the product design that represents the best investment solution for a theoretical, representative investor who uses the target-date funds to accumulate wealth for retirement. The VLCM generates an optimal custom glide path for a participant population by assessing the trade-offs between the expected (median) wealth accumulation and the uncertainty about that wealth outcome, for thousands of potential glide paths. The VLCM does this by combining two set of inputs: the asset class return projections from the VCMM and the average characteristics of the participant population. Along with the optimal custom glide path, the VLCM generates a wide range of portfolio metrics such as a distribution of potential wealth accumulation outcomes, risk and return distributions for the asset allocation, and probability of ruin, such as the odds of participants depleting their wealth by age 95.
  • The VLCM inherits the distributional forecasting framework of the VCMM and applies to it the calculation of wealth outcomes from any given portfolio.
  • The most impactful drivers of glide path changes within the VLCM tend to be risk aversion, the presence of a defined benefit plan, retirement age, savings rate and starting compensation. The VLCM chooses among glide paths by scoring them according to the utility function described and choosing the one with the highest score. The VLCM does not optimize the levels of spending and contribution rates. Rather, the VLCM optimizes the glide path for a given customizable level of spending, growth rate of contributions and other plan sponsor characteristics.
  • A full dynamic stochastic life-cycle model, including optimization of a savings strategy and dynamic spending in retirement is beyond the scope of this framework.
  • 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.