A deeper look inside the investment returns of some of the most prestigious endowments in the world.
Every fall brings with it excitement and some surprise in a much-watched annual contest. No, we’re not talking about the World Series but rather endowment-reporting season when most schools report their fiscal year investment returns.
In this section of our Research we will focus on analyses of the top endowment funds using MPI Stylus, which has become a solution for investors assessing complex funds and those with limited data disclosures (e.g., hedge funds). The project below is an attempt to bring more transparency to the opaque world of some of the most largest and successful investors in the world. “MPI360” quantitative reports provide insight into these top endowments that cannot be achieved using other methods and suggests reasons for the range of their performance outcomes. Please contact us for a report on an endowment that is not on the list.
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Disclosures from endowments are minimal, typically consisting of annual performance figures, updates to policy portfolios (target allocations to specified asset classes and/or factors) and general commentary. Endowments often have conservative media policies as the interests of the institution in growing and preserving such primary funding sources don’t align with full transparency of investment activities. Even many institutional consultants struggle to explain top-tier endowment results. Although many papers and books have been written on the endowment model for investing by gurus like Yale CIO David Swensen, those seeking to understand tactical allocation shifts, strategy preferences, and manager selection are given little to work with on an ongoing basis. The endowment world remains shrouded in opacity and most takeaways amount to little more than supposition.
When only annual performance figures are reported, a decade’s worth of performance is represented by 10 data points. Traditional static and rolling window methods of regression analysis struggle to find credible insights from such infrequent data. MPI’s Dynamic Style Analysis, however, is uniquely able to work with such limited data. DSA improves upon Sharpe’s original RBSA approach and, using factors indicative of the asset classes reported by endowments, provides significant insights into their behavior. DSA is able to explain changes in an endowment’s performance over time and to highlight differences across endowments using a common analytical framework.
The takeaways from our Endowment research pieces are not much different from the ones our clients derive on daily basis when analyzing individual mutual funds, hedge funds, PE and VC funds as well as portfolios of such funds:
- Validation of the strategy. Was it possible to achieve these kinds of results using reported allocations? Any significant unexplained returns in a particular year?
- Provide a common asset class “denominator.” Every endowment is unique in the way they define asset classes, privates and alts in particular making it almost impossible to compare them to each other.
- Picking up trends in allocation that are otherwise hidden (e.g., Harvard’s restructuring after GFC, Brown’s Tech exposure build-up, Duke’s investment in Coinbase, etc.)
- Risk/Efficiency. Largest endowments are long-term investors, and many don’t have constraints common to pensions. Little is known from outside about their actual risks. Like other investors they should be judged based on risk and performance efficiency.
- Attribution of results. Mostly hidden from the public… in bad years in particular. When performance is great (e.g., 2021FY) they have no problem boasting about each sleeve’s contribution. Not so much when performance is lagging.
- Rankings. We would like to move rankings away from pure performance to include risk and efficiency. We find some smaller endowments are more efficient than larger and more prominent ones.
Please read more about our methodology.
|Name||Endowment||MPI360 Report||Reported||AUM ($B)||2022 return||2021 return||3Y return||5Y return||10Y return||Risk||Private equity %||Sharpe Ratio||Risk 10Y||Assets||Fiscal Year-end||3Q2022 Estimate||4Q2022 Estimate||1Q2023 Estimate||2Q2023 Estimate||FY2023 Estimate|
|Ivy Average||Ivy Average||23.3||-2.4||41.8||13.7||11.9||10.8||16.7||0.70||15.3||6.826419||June||-3.30||2.51||4.02||3.9||7.13|
|70-30 Global Bmk||70-30 Global Bmk||30/06/2022||-14.0||26.4||4.6||5.6||6.8||10.9||0.60||10.9||7.000000||June||-6.35||8.20||6.34||4.8||12.86|
|Fiscal Year 2021|
In-depth Endowment Research
It is generally known that endowments invest in risky assets, but quantifying such risks has remained challenging due to a lack of information about returns. We set out to address this challenge and developed a new basis for estimating endowment risks.
In stark contrast to FY 2016, this past year was a strong one for most endowments. In fact, nearly all the Ivy League endowments, Harvard being the only exception, beat the 60-40 portfolio, a commonly cited benchmark that endowments measure their performance against.
The returns of endowments can be attributed to two fundamental components: asset allocation and security selection. Asset allocation is what a factor model is generally able to explain, shown in terms of factor exposures.
We look at the largest endowments and find striking similarities in their asset class exposures. At the same time, some endowments stand out both in terms of allocations and FY2016 performance.
An 1873 meeting that brought Harvard, Yale and Princeton together to codify the rules of American football also debuted a sports conference later known as the “Ivy League — eight elite institutions whose heritage, dating from pre-Revolutionary times, became formative influences shaping American character and culture. These schools also pioneered endowment investment management, thus helping to secure the nation’s educational legacy for posterity.