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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.
“The smart beta label still represents a small, new, heterogeneous, and most likely misunderstood, group of exchange-traded funds in the fixed income space,” says MPI’s Megan Woods in this article on Smart Beta bond funds by Institutional Investor‘s Julie Segal.
“Markov Processes International… uses a model to infer what returns would have been from the endowments’ asset allocations. This led to two key findings… ” John Authers cites MPI’s 2017 Ivy League Endowment returns analysis in his weekly Financial Times Smart Money column.
“This method allows firms to effectively deduce strategies at other firms and avoid potential counterparty risks, without being forced to wade through information…” The article “Criminal minds and increased surveillance” highlights MPI’s technology for non-intrusive Oversight and Surveillance.
“Michael Markov, C.E.O. of MPI, a quantitative research firm, said calculations using daily prices of AXA Rosenberg’s mutual fund portfolios suggest that by early 2009, there was “an apparent aberration” in the funds.” The New York Times’ Jeff Sommer features MPI’s analysis in a story “The Tremors From a Coding Error”.
“…(MPI) was hired by a fund two years ago to look into Fairfield Sentry’s returns and found that it was “statistically impossible to replicate them.” New York Times article “In Fraud Case, Middlemen in Spotlight” discusses how MPI found warning signs in Madoff’s returns.
“To folk who want to invest in hedge funds, as well as those who want to invest like hedge funds, Markov Processes has a lot to offer…” The Economist article “In the garden of good and evil” discusses MPI’s expertise in quantitative analysis and replication of hedge funds