Tuesday, June 26, 2018

Snapshots of TDF performance miss the big picture

 Kevin Megargel, senior defined contribution investment strategist and senior manager in Vanguard Institutional Investor Group

If you came to my house, you’d see lots of freshly framed wedding photos. The pictures are very meaningful to my wife and me, reminding us of everything that happened on our wedding day just a couple of months ago. But they depict what happened on one day, during a special occasion. There’s much more to our life together than they can show, even if each one is worth a thousand words.

Plan sponsors and consultants who analyze target-date funds (TDFs­) by focusing on short time frames face the same limitation of snapshots. Drawing conclusions about a TDF’s overall performance based on point-in-time analysis is a little like trying to understand the entirety of my life by looking at a single wedding photo. It’s impossible. When plan sponsors tell us they have isolated and analyzed a TDF’s asset allocation or performance during a defined time period and are now making decisions for participants based on that time period, we become concerned that they don’t have the whole picture.

Judge a TDF holistically
TDFs are long-term investments, intended to grow over (usually) decades and ultimately to provide retirement income. To create an effective TDF, we leverage everything we know about investments, plan design, and investor behavior. We integrate years of knowledge to help ensure a participant’s savings work hard on their behalf, building a nest egg for the long term.

To judge how well a TDF delivers upon its promise, it’s essential to review its results over its entire life span. Point-in-time analysis is interesting and we do it ourselves, but we also think it’s essential to stand back and take a wider angle, holistic view of the TDF in its entirety.

Keep short-term comparisons in perspective
Analyzing a TDF in short time frames may not fully capture the participant experience. For example, if you zoom in on the early stage of a Vanguard TDF’s glide path, you’ll see the Vanguard TDF maintains a 90% equity stake during the investor’s initial accumulation phase. We selected this allocation for many reasons. One is because, from our experience, we’ve seen that that the 90% allocation to equities captures a large portion of the equity market risk premium, while the 10% bond allocation offers a modest degree of diversification, an attribute we believe is desirable for early career investors.

Now compare this early stage of our glide path with a hypothetical TDF that starts with a 100% investment in equities. If you were comparing the performance of a Vanguard TDF to a competitor’s TDF at this early stage, you might see lower returns in the Vanguard product. But that wouldn’t be the whole picture.

Over time the Vanguard allocation—our glide path—gradually reduces equity exposure. By the time a Vanguard investor reaches retirement (age 65), they will own a 50% stock/50% bond mix. In contrast, some funds take a more aggressive step-down approach, abruptly reducing equity from 100% to 40%, which leaves investors with a 60% bond/40% stock mix at retirement— a more conservative asset allocation than Vanguard’s.

At that point, Vanguard might appear to be the better performing fund, depending on what’s happening in the markets. But it is important to remember that differences analyzed at specific points in time can appear meaningful and still make no appreciable difference in long-term outcomes for participants.

Vanguard glide path and hypothetical glide path adjust asset allocations over the TDF life cycle

Source: Vanguard, 2018.

Vanguard’s Glide Path Solutions™ reveals a TDF’s whole picture
To help plan sponsors see beyond the short-term, Vanguard has developed an interactive technology platform, utilized in our engagements with clients when assessing TDFs—Vanguard Glide Path Solutions™.

Vanguard GPS™ provides plan sponsors with a deeper understanding of TDFs. It facilitates and validates plan sponsors’ TDF decisions, provides transparency into Vanguard TDF construction, and supports TDF customization. By using this platform, plan sponsors can analyze risk-return trade-offs and tie the trade-offs to potential outcomes throughout the life cycle for the typical participant on dimensions such as wealth, drawdown, and probability of maintaining a positive balance.

This is one of many techniques Vanguard uses to help ensure TDFs are designed to be a holistic, comprehensive culmination of participants’ years of investing. So when evaluating TDFs, plan sponsors and consultants would do well to look beyond a single point in time and analyze a TDF in its entirety. Now that’s the whole picture.


  • 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. Past performance is no guarantee of future returns.
  • Diversification does not ensure a profit or protect against a loss.
  • Investments in bonds are subject to interest rate, credit, and inflation risk.
  • 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.
  • IMPORTANT: The projections or 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. 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 Models® 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.
  • The Vanguard Lifecycle 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.

About Kevin Megargel

Kevin Megargel, CFA, CFP®, is a senior defined contribution investment strategist and senior manager in Vanguard Institutional Investor Group. In this role, Mr. Megargel is responsible for assisting plan sponsors with plan lineup investment analysis and custom portfolio construction. He also leads a team of defined contribution investment strategists. Before this position, Mr. Megargel was an investment analyst in Vanguard Portfolio Review Department, which is responsible for overseeing, evaluating, and developing Vanguard funds. Mr. Megargel earned a B.S. in finance and accounting from the University of Maryland and an M.B.A. from Villanova University. He is a member of the CFA Institute and CFA Society of Philadelphia.