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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.
Investors have a tendency to downplay interest rate sensitivity as a factor influencing equity products, with the assumption being that its effect must be negligible at most. One of a handful of exceptions to that assumption, however, is concern over the rate sensitivity of low volatility “smart beta” funds.
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.
Morningstar’s 2017 Target Date Landscape Report indicates that approximately one quarter of TDF series shifted the target equity allocation of at least one vintage by 15% or more over the last 5 years and nearly half by at least 5%.
Four of the other five fund families with holdings vs. returns-based discrepancies are of a similar nature in that they have investments in derivatives, leveraged funds or absolute return funds, which affect the holdings tally. In each of these cases, DSA provides a much closer estimate to the intended systematic exposure.
We demonstrate the advantages of using returns-based analysis in determining the effective glide-paths of Target-Date Funds vs. the stated ones
Continuing our exploration into the smart beta segment (Part 1, Part 2), in this third post we introduce a simple “IQ Test” that can help investors and managers measure the “smartness” of the increasing number of non-cap-weight rules-based products on the market.
Global Tactical Asset Allocation (GTAA) funds, which seek to take advantage of changing market conditions while maintaining a globally diversified portfolio, have suffered recent underperformance. MPI was asked by Institutional Investor to look at some of the funds that have received the most interest from investors.
We use Bridgewater All Weather, one of the largest hedge funds, to illustrate how to quantitative techniques could provide investors with a more dynamic understanding of the potential fund behavior intra-month using only monthly fund data.