In this article, we offer a robust analytical framework that can help endowments and foundations think about spend-rate planning, in terms of key risks they face such as short-term drawdown risk and long-term erosion of capital.

Investment committees for endowments and foundations have a wide range of responsibilities, but ultimately their job boils down to a single task: Ensure that the portfolio can deliver funds to the organization in the short term, without unintentionally spending down principal over the long term. Formally, this is often referred to as “capital sufficiency” planning and more informally, it is often called spend-rate planning.

In the past, spend-rate planning was a fairly straightforward task for investment committees. The “5% rule” was instituted in 1981 by the IRS; this rule requires private foundations to distribute at least 5% of portfolio assets each year, and over time this rule has been voluntarily adopted by nonprofits of all types. As shown in the chart below, in the mid-1990s nominal yields from a conservative fixed income portfolio (as represented by the 10-year U.S. Treasury yield) were enough to meet or exceed a 5% spend rate. Since then, nominal yields have declined notably and currently fall well short of that 5% threshold. What’s more, in the current market environment, fixed income returns alone are barely able to keep pace with inflation. Note that the 5% rule was intended to be a high hurdle. One of our firm’s strategic advisors was on the Reagan-era tax policy team that implemented the 5% rule; they sought to ensure that private foundations did not become favorable havens for tax-free growth. Despite the high interest rates at that time, they knew that the rule would not make it easy for foundations to exist in perpetuity.

Therefore, many investment committees today take on considerably more risk in their portfolios than they did a generation ago, in an effort to maintain historical spending levels. As a result, they face a host of new fiduciary challenges as they seek to balance increased risk in their portfolio, the immediate spending needs of their organizations, and their obligation to protect underlying portfolio capital over the long term.

We believe committees need a comprehensive framework to help them understand and balance these risks. Portfolio “optimization” techniques only go so far; these techniques provide quantitative answers, but lack the judgment needed to appropriately consider current market conditions, liquidity constraints, and most importantly, the key risks of a severe short-term drawdown or of long-term principal erosion. In this paper, we will discuss our framework for spend-rate analysis, and how we help our endowment and foundation clients translate this analysis into decisions for their portfolios.


Twenty-five years ago, a conservative fixed income portfolio could reasonably meet a 5% spend rate and could often outpace inflation as well. Today, fixed income returns alone are barely enough to keep pace with inflation, so institutions have had to take on considerably greater risk in an effort to maintain historical spend rates.

SOURCE: Bloomberg. Real yield figures calculated using monthly Core CPI figures from the U.S. Bureau of Labor Statistics.


When it comes to managing institutional portfolios, most CIOs, committees and advisors adopt one of two philosophical approaches. The first approach is to determine an acceptable level of risk—often termed a “risk budget”—and then seek to maximize potential return within that risk constraint. Alternately, they can determine a target or required rate of return, and then adjust risk up or down to meet that return goal. Each of these philosophies is somewhat rooted in Modern Portfolio Theory (MPT), which introduced the now-intuitive idea that investments should be measured by how well they compensate investors for risk, as measured by standard deviation (i.e., expected dispersion from mean returns).

As we stated in “Confronting the Unknown,” our 2018 asset allocation publication, standard deviation is “a helpful shortcut for thinking about risk, but it is not a fully effective proxy.” Specifically, standard deviation does not account for illiquidity in the portfolio (which investors need to consider if there is any chance they will need access to their capital over time), it does not account for the risk of insufficient growth (a primary consideration for any investor with a long-term timeframe) and perhaps most importantly it does not account for “outlier” drawdown risk in the short term or the risk of principal erosion over a longer period. In other words, it does not effectively measure the actual probability that investors will achieve their stated goals.

Effective risk analysis, then, requires us to balance competing goals in a portfolio, and to use a combination of quantitative analysis and subjective judgment to guide future decisions. In this discussion, we focus on two primary risks for endowments and foundations—short-term drawdown risk and long-term erosion of principal. Tolerance for short-term drawdowns is a factor that all investors need to carefully consider, both in terms of how such drawdowns will impact immediate real-world concerns (endowments considering real estate expansion, foundations paying staff members or funding ongoing programs, etc.) and how such drawdowns may create psychological aftershocks that lead to poor investment decisions. Institutions also need to consider legal liabilities and stakeholder responses to outsized portfolio losses. Long-term principal erosion is of particular concern for endowments and foundations; many of these portfolios were created to provide funding into perpetuity, and the erosion of capital represents a clear and present danger to a perpetuity mandate. Even if an institution’s portfolio is being managed in a spend-down or “sunset” model, the organization needs to ensure that asset levels do not fall off more rapidly than intended.

We use what we consider to be a very helpful framework to consolidate these competing short-term and long-term objectives, and visualize how changes in asset allocation, spending rate or underlying market assumptions affect the range of potential outcomes for our clients.* These visualizations, which we call “shoestring charts” (a reference to both the appearance of the graphs as well as an acknowledgment of the shoestring budgets that many of our clients face), depict a reasonable expectation of outcomes for portfolios, given a set of assumptions about the portfolio’s asset allocation, the expected annual spend rate, as well as baseline assumptions about asset-class returns, inflation and other factors.

A basic shoestring chart (as shown below) reveals expected outcomes according to two metrics:

The vertical axis measures short-term drawdown risk, in terms of an expected “1 in 20” drawdown event (i.e., the worst one-year decline we should expect over a 20-year period, based on historical data).

The horizontal axis measures the long-term risk of principal erosion, expressed as a percentage likelihood that portfolio assets will equal or exceed their current value in 10 years.

Each “shoestring” curve represents the expected outcomes for various allocation targets, assuming a given spend rate. We can produce these charts with or without taking inflation into account, but generally we depict data in real terms (i.e., after inflation) so clients can understand how investment and spending decisions today could impact the portfolio’s ability to support the organization in the future. This measure also matches the classic “spend rate+inflation” return hurdle that many organizations target in their investment policies.

The analysis behind these charts, despite their straightforward appearance, is deep and comprehensive. We use a variety of return, risk and correlation assumptions developed by our asset allocation research team to drive Monte Carlo simulations that forecast results over a 10-year time period. In the example chart below, we used a static strategic policy allocation between a global equity benchmark and an intermediate aggregate bond index, but our models for clients can incorporate a full array of asset classes in their diversified portfolios, including alternative investments.


We use shoestring charts to help clients understand key short-term and long-term risks associated with their asset allocation and spend-rate decisions. The “shoestring curve” below depicts these risks for a hypothetical portfolio, assuming various asset allocation targets. As allocation shifts toward equities, the organization’s likelihood of preserving capital (by growing at a sufficient rate to outpace spending and inflation) increases, but its “worst-case” scenario for a short-term drawdown also increases. (All simulations are based on data as of Dec. 31, 2018.)

SOURCE: Brown Advisory

*We would like to acknowledge the work of Peng Wang and John Spinney on this topic over the past several years which helped inspire our own thinking, with particular reference to their article “Combining Science and Judgment to Balance Short-Term and Long-Term Goals” in the Fall 2017 edition of the Journal of Portfolio Management.


Now that we have explained the basic idea behind these charts, we can talk about how endowments and foundations can use this analysis to see the impact of potential decisions they may be considering.

To support a target spend rate into perpetuity, organizations have three primary levers that they can pull to modify results over time: They can 1) change their asset allocation, 2) change their spend rate, or 3) seek higher returns within a given asset allocation through active manager and security selection.

1. Adjust the portfolio’s asset allocation (“moving along the curve”). The example chart on page 4 models the median outcomes for various asset allocation targets, from conservative to aggressive. As we see in that chart, adding equity exposure increases the likelihood of preserving capital over time after spending and inflation, but greatly increases the potential worst-case scenario for a one-year portfolio drawdown.

An important point here is that the benefit of taking on additional equity risk rapidly diminishes past a certain point, according to this analysis. This model (results will vary depending on asset allocation, economic assumptions and other variables) posits a 43% chance that a 70/30 portfolio under these market and spending conditions will preserve capital over ten years. Increasing equity exposure all the way to 90% only increases the likelihood of preserving capital to 46%.

This “upper limit” on the probability of preserving capital makes sense logically—with a 5% spend rate and inflation around 2%, the portfolio would need to clear a 7% return threshold annually to preserve capital after inflation. That is far from a guaranteed result in today’s market environment. Although this fact may be humbling for institutions and professional investors alike, it is nonetheless prudent to build plans with risks like this in mind.

2. Adjust the organization’s spend rate (“shifting the curve”). This option is depicted in the chart below. The second orange curve depicts a new set of median outcomes, assuming that spending has been reduced from 5% to 4%. The model suggests that such a change would increase the organization’s probability of preserving capital by roughly 10% for portfolios with moderate-to-aggressive allocations.

An organization’s spending rate is almost always the most meaningful variable affecting its long-term portfolio outcomes. We often counsel clients to review various methods of reducing annual portfolio distributions when possible, to provide more breathing room for long-term results. However, many of our clients have little or no flexibility in this regard. Many nonprofit institutions depend heavily on portfolio distributions to support their ongoing programs, and private foundations simply can’t reduce distributions below a 5% annual minimum without risking their tax-exempt status.

Still, there are usually options for organizations to consider regarding their spending. In cases when markets are up notably over a period of time, clients are often able to reduce that year’s spending by a small percentage amount without actually reducing their absolute outlay in dollar terms. Smoothing techniques, such as using a three-year (or preferably a 12-quarter) average portfolio valuation to determine spending, can also help create a natural mechanism that holds back spending in bull markets and ensures a cushion for when markets turn sour. If and when an organization makes the choice to reduce its long-term spend rate, it can phase that reduction in over several years to minimize any shock to the system.


A decision to change an organization’s spend rate is depicted in our framework by a new shoestring curve; in the example below, we see that reducing spending from 5% to 4% materially increases the organization’s likelihood of preserving capital over time.

SOURCE: Bloomberg. Return, volatility and correlation estimates for equity and fixed income allocations are based on the MSCI ACWI Index and the Bloomberg Barclays Aggregate Bond Index respectively. All analysis is based on data as of Dec. 31, 2018.

3. Seek outperformance (“bending the curve”). Finally, institutions can try to earn incremental returns within a given asset allocation through active investment selection. Note that modeling “potential outperformance” is a far more subjective exercise than modeling factual choices like asset allocation or spending rate. A helpful starting point is to simply assume that active risk is symmetrical. In other words, if you think you can outperform by 1% per year in an asset class, you should at least consider what might happen if you underperform by 1% due to active investment choices that do not work out. By modeling both upside and downside return assumptions, we can begin to see the potential positive and negative impact of active investment decisions on short-term and long-term outcomes.

The chart below shows how this option might play out for our hypothetical organization. We have used research from our Investment Solutions Group (ISG) that estimates potential alpha in various asset classes. Note that certain asset classes not modeled, such as private equity (available only to Qualified Purchasers or Accredited Investors) offer greater potential alpha than others. We used this data to model an “outperformance” scenario (i.e., baseline returns are adjusted to match ISG’s potential alpha targets in each asset class) and an “underperformance” scenario (i.e., baseline returns are adjusted downward by an equal amount). For the sake of illustration, we assumed that active investment risk was limited to the equity portion of the portfolio.


By taking on active risk in the equity portion of this hypothetical portfolio, it is possible to increase the likelihood that the portfolio can outpace inflation + spending over time (as displayed in the orange, “outperformance” curve). Of course, the opposite is true if the active investment choices don’t work out as planned (as displayed in the gray, “underperformance” curve). The difference between the upside and downside scenarios is greater for asset allocations with more equity risk. The data is another reminder that active management decisions need to be backed up by rock-solid research and manager due diligence. (See body text above for descriptions of how these scenarios were modeled.)

SOURCE: Bloomberg. Return, volatility and correlation estimates for equity and fixed income allocations are based on the MSCI ACWI Index and the Bloomberg Barclays Aggregate Bond Index respectively. All analysis is based on data as of Dec. 31, 2018.

We will not explore the active vs. passive investing debate in this article. We generally believe that there is no need to “choose” one or the other, and many of the portfolios we manage for endowment and foundation clients hold both active and passive strategies. Suffice it to say that outperformance will clearly increase a portfolio’s chances of beating inflation and supporting a given spend rate, but investors need to acknowledge the downside risk that their active choices may generate. Introducing active management into a portfolio can certainly make sense when supported by solid research processes and expertise. When private investments are an option, an organization can benefit from the potentially favorable risk/reward mix that such investments can produce over time, but these need to be considered against the illiquidity that accompanies such investments and whether the organization can comfortably lock up portions of its portfolio for extended periods of time.


None of the decision levers we discussed in this article—adjusting asset allocation, adjusting spend rate, incorporating active management—offer a surefire path to success for endowment and foundations. These institutions are pursuing multiple investment objectives. They want to constrain drawdown risk, maximize return to support spending, and (in most cases) preserve capital into perpetuity. These objectives often directly compete with each other, so investment committees continually walk a fine line in order to satisfy their fiduciary obligations and their organization’s operational needs.

No purely quantitative exercise can address this quandary. As advisors, we inject a great deal of analytical rigor into our relationships with endowments and foundations, yet the decisions we make together with our clients also require subjective discussion, a clear understanding of the organization’s culture and priorities, and a willingness to adjust and adapt over time to account for changing market conditions.

We try to encourage clients to invite full participation from board members and staff in the spend-rate discussion, to ensure that program and development perspectives are included as well as the views of the investment or finance leadership. In some cases, we discuss options to diversify the organization’s sources of funding, in order to reduce reliance on funding from the portfolio. We have helped a number of clients build out their planned giving programs and enhance their annual fundraising capabilities using mechanisms such as donor advised funds, IRA rollovers and donations of appreciated assets. While it can be challenging to ramp up new fundraising efforts for any nonprofit, sometimes a conversation about the cold, hard numbers can be just the catalyst an organization needs to embrace those fundraising challenges and begin to put itself on stronger footing for the future.

In other words, this analytical framework cannot replace vision or leadership or thoughtful, fiduciary judgment. But it can be a helpful tool to help leaders and fiduciaries perform their roles effectively and guide their institutions forward. 





The views expressed are those of the author and Brown Advisory as of the date referenced and are subject to change at any time based on market or other conditions. These views are not intended to be and should not be relied upon as investment advice and are not intended to be a forecast of future events or a guarantee of future results. Past performance is not a guarantee of future performance and you may not get back the amount invested.

The information provided in this material is not intended to be and should not be considered to be a recommendation or suggestion to engage in or refrain from a particular course of action or to make or hold a particular investment or pursue a particular investment strategy, including whether or not to buy, sell, or hold a particular security or pursue a particular asset allocation. It should not be assumed that investments in any particular securities, or any particular asset allocation, have been or will be profitable. The information contained herein has been prepared from sources believed reliable but is not guaranteed by us as to its timeliness or accuracy, and is not a complete summary or statement of all available data. This piece is intended solely for our clients and prospective clients, is for informational purposes only, and is not individually tailored for or directed to any particular client or prospective client.

Monte Carlo simulations model future uncertainty. In contrast to models generating average outcomes, Monte Carlo analyses produce outcome ranges based on probability, thus incorporating future uncertainty. Analysis was developed using a custom-built Monte Carlo spreadsheet model, and did not involve usage of a third-party tool.Because these simulations involve a large series of randomized scenarios, results may vary across simulations that use identical assumptions, and over time.

The information in this material is for illustration and discussion purposes only. It is not intended to be, nor should it be construed or used as, investment, tax or legal advice, any recommendation or opinion regarding the appropriateness or suitability of any investment or strategy.

IMPORTANT: The information regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. The simulations are based on assumptions. There can be no assurance that the results shown will be achieved or sustained. The charts present only a range of possible outcomes. Actual results will vary, and such results may be better or worse than the simulated scenarios. Clients should be aware that the potential for loss (or gain) may be greater than demonstrated in the simulations. In addition, other investment strategies and asset allocations not considered herein may have characteristics similar or superior to those being analyzed.


All calculations used projected returns by asset class and not historical or actual returns of any singular investment holding or portfolio. Projected portfolio returns were calculated by weighting individual return assumptions for each asset class. The analysis assumed reinvestment of interest and dividends at net asset value without taxes or fees, and also assumed that the hypothetical portfolio would be rebalanced annually to reflect the initial recommendation. No portfolio rebalancing costs, including taxes, if applicable, are deducted from the hypothetical portfolio value.

Portfolios in this analysis were limited to two asset classes: global equity, and core fixed income. Baseline expected returns, as well as assumptions regarding fluctuations in these returns and correlations between the two asset classes, were calculated using a variety of factors, one of which was the historical returns of the MSCI ACWI Index for global equity, and the Bloomberg Barclays Aggregate Bond Index for core fixed income, as well as other factors Using these estimates as inputs, the Monte Carlo model developed scenarios, each of which represented a randomly generated series of annual returns in each asset class over a ten-year period. The Monte Carlo simulations involved development of 1,000 of these scenarios, across a range of equity/fixed income asset allocation mixtures. Results in each scenario were expressed net of an assumed spend rate (as noted in each simulation summary) and net of inflation (assumed at 2% and held constant over the measured time period).

Results were expressed in terms of two variables: 1) the maximum likely one-year portfolio drawdown at a given asset allocation mixture, and 2) the percentage likelihood that the portfolio’s value would be equal or greater after ten years, once spending and inflation were taken into account.


There is no certainty that the assumptions for the model will accurately estimate asset class return rates going forward. As a consequence, the results of the analysis should be viewed as approximations, and readers should not rely heavily on the apparent precision of the results.

The analysis does not use all asset classes. Other asset classes may provide different returns or outcomes than those used.

Taxes are not taken into account, nor are fees, expenses or early withdrawal penalties.

The analysis models asset classes, not investment products. As a result, the actual experience of an investor in a given investment product (e.g., a mutual fund) may differ from the range generated by the simulation, even if the broad asset allocation of the investment product is similar to the one being modeled. Possible reasons for divergence include, but are not limited to, active management by the manager of the investment product or the costs, fees, and other expenses associated with the investment product. Active management for any particular investment product—the selection of a portfolio of individual securities that differs from the broad asset classes modeled in the analysis—can lead to the investment product having higher or lower returns than the range used in this analysis.

Inflation is assumed at a constant rate in the analysis, so variations in inflation are not taken into account. Various factors endemic to equity markets—extreme market movements, market crises, periods of higher asset-class correlation—are not taken into account, so results actually experienced by investors may be different and more volatile than those modeled.

The MSCI ACWI Index is a market capitalization weighted index designed to provide a broad measure of equity-market performance throughout the world. The MSCI ACWI is maintained by Morgan Stanley Capital International (MSCI), and is comprised of stocks from both developed and emerging markets. All MSCI indexes and products are trademarks and service marks of MSCI or its subsidiaries.

The Bloomberg Barclays U.S. Aggregate Bond Index is an unmanaged, market-value weighted index comprised of taxable U.S. investment grade, fixed rate bond market securities, including government, government agency, corporate, asset-backed, and mortgage-backed securities between one and 10 years.