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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Standard Life GARS Fund: MPI’s Factor X-ray

The £27bn Standard Life Global Absolute Return Fund (SLI GARS) has been renowned as a leading absolute return UCITS/mutual fund since its inception in 2008. However, recently its performance reversed from the peak reached in April of 2015. Using SLI GARS’ weekly performance data, we demonstrate how sophisticated factor analysis techniques can provide valuable insights […]

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Ivy League Endowments 2016 Performance Review

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 […]

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Lower Volatility Smart Beta Funds – A Safe Haven in Turbulent Times? Part 2 of a Series on Multifactor Smart Beta ETFs

Smart Beta funds are hot. According to ETF.com, more than half of the 150 funds launched in 2016 implemented smart beta strategies.  For the year to June 30, 2016, ETFGI’s most recent data show that assets in smart beta funds have a five-year annual compound growth rate of 31.3 percent. And, low volatility funds, up […]

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