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2017 Ivy Endowment Performance Puzzle: Was Equity Allocation Strategy Irrelevant?

Ivy League endowments number three (Cornell) and four (Penn) released their 2017 returns Friday, producing an impressive 12.5 percent and 14.3 percent return, respectively. When compared with Dartmouth and Harvard endowment returns, which we analyzed last week as part of MPI’s annual Measuring the Ivy endowment performance series, an interesting trend emerges. Regardless of their […]

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Measuring the Ivy 2017: Dartmouth vs. Harvard, Similar Exposures Yield Significantly Different Results

With the first two Ivy League endowments turning in their fiscal year 2017 returns, we’re providing a snapshot to compare how Harvard and Dartmouth did relative to each other using our patented Dynamic Style Analysis (DSA) model. DSA is an enhanced (returns-based) quantitative analysis model that provides a more transparent view of opaque or complex […]

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Target-Date Fund Research Series, Part III: Assessing Allocation Changes in TDF Glide Paths, Strategic or Tactical?

When an investor is considering a target date fund, the sales pitch is that they can “set it and forget it”, that their asset allocation will proceed merrily along a painstakingly selected glide path until it reaches the desired retirement (or post-retirement) date. Fund managers however, cannot maintain the same capital market assumptions and associated […]

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Target-Date Fund Research Series, Part II: Differences in TDF Holdings vs. Exposures

As we previously discussed in Part I, returns-based style analysis, in particular MPI’s DSA model, generally does an excellent job of estimating the current equity exposures of Target Date Funds.  In some cases, however, DSA estimates are significantly different from consolidated holdings information – for six fund families out of the current TDF universe, in […]

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Target Date Fund Research Series, Part I: Advantages of a Quant Approach to Glide Path Analysis

As target date funds (”TDFs”) become increasingly entrenched in investors’ retirement portfolios, plan sponsors and advisors do not miss the irony that these products, meant to simplify the investors’ decision process to the point of simply choosing the fund with the closest date to their intended retirement, can add a world of complexity to their […]

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Isolating the Monkey Effect. Part 3 of MPI’s Series on Smart Beta ETFs

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. There are numerous arguments in circulation saying that smart beta in general isn’t […]

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Parsing the Dynamics of Global Tactical Asset Allocation (GTAA) Funds

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, possibly driving withdrawals from the strategy.  Considering the question of whether investors are bailing too soon, MPI was asked by Institutional Investor to look at some of the funds that […]

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Forecasting Bridgewater All Weather Performance in November’s Bond Storm

November’s government bond sell-off resulted in one of the sharpest increases in Treasury yields in recent history and an uptick in fixed income volatility. While this may be particularly bad news for traditional fixed income funds, risk parity funds should, in theory anyway and to the extent that other asset classes have held their ground, […]

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Does Risk Parity Maximize Risk-adjusted Returns?

While it is well known that risk parity strategies typically allocate more weight or apply leverage to asset classes with lower risk, it is not well understood how higher volatility affects the Sharpe ratios exhibited by the assets that get over- or under- weighted.  We find that in practice the strategy increases an asset’s weight […]

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Have Endowments Adopted The Yale Model?

Using MPI’s Common Style to Understand the Endowment Landscape   Dispersion of 2016FY Results With limited data and only general information about their actual allocations, it can be difficult to identify the causes of the wide dispersion in the returns of endowments in 2016. Note the large spread between the highest and lowest performing endowments […]

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