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The grades for all the Ivy League endowments are in – and they are rather disappointing. Save for Brown, all Ivies underperformed the 9.9% return of a domestic 60-40 portfolio in fiscal year 2019. The Ivy average in FY 2019 was 6.7%, significantly underperforming the 60-40 and reversing two years in which they outperformed the traditional domestic benchmark.
Using our analytical tools and publicly available endowment annual performance data, we project FY2019 performance of large and small endowments, as well as the Ivy League average and Yale
We sought to examine the relationships between endowment size, pedigree and exposure to private assets and what impact that may have on portfolio risk using advanced quantitative methods and a cutting edge methodology to better model the true behavior and risk profile of private market assets.
In this post, our research team examines why investors should proceed with caution when selecting top-ranked funds.
While the health of the bull market, the raging fee wars and the ongoing active vs. passive debate continue to capture the money management industry’s attention, something fascinating has quietly taken place on fund analysts’ radars.
The endowment model, and active management in general, has come under increased scrutiny, while indexed, or passive, products have grown in popularity and number. Regardless of where you stand on that debate, it’s hard to deny that the Ivies approach to asset allocation has been very good.
Similar to 2017 performance, this past fiscal year was a strong one for most Ivy League endowments. Fiscal year 2018 is noteworthy, however, for being the first year that long horizon (10-year) returns from all Ivy endowments lagged behind the 60-40 portfolio.
Returns across the Ivy League are largely seen as being driven by exposure to private equity and venture capital.
At the midway point of fiscal year reporting for the Ivy League endowments, our research team analyzes what we know so far to identify the key drivers of returns.
This document provides an introduction to MPI portfolio stress testing methodology as well as a step-by-step overview of how to conduct fund- and portfolio-level stress tests within the MPI Stylus Pro application.