MPI Transparency Lab
A free resource from MPI providing unique insight into the world’s largest and most opaque investments
Driven by our MPI Stylus Pro technology, these MPI-360 transparency reports shine a light on portfolio return, risk, and effective asset-class exposures in a way unavailable anywhere else. Whether you are an institutional investor, asset manager, wealth advisor, researcher, or journalist, you can read below to learn why we believe transparency into these impactful portfolios is important and how we go about pulling back the curtains on their behavior. Endowments and Pensions sections above provide analytical information on specific funds unavailable anywhere else, including detailed MPI reports on specific funds with free registration.
And, of course, you can contact us to speak with MPI and learn why MPI is the provider of choice for anyone seeking to understand complex fund and portfolio behavior for which limited data is available.
- 1. Why Transparency?
- 2. Why MPI?
- 3. Methodology
- 4. MPI-360 Reports
- 5. FAQ
1. Why Transparency?
College and charitable endowments, pension funds, hedge funds, sovereign wealth funds…even as frustrated stakeholders call for greater transparency, the world’s largest and most important investments tend to remain shrouded in mystery. Yet the larger these funds get, the more they may not only be affected by markets, but also exert an influence on markets, both public and private. It’s fair to wonder what exactly is inside these tax-sheltered or tax-advantaged investing machines.
MPI remains committed to helping investment professionals, stakeholders, and the public form a clearer picture of these investment giants and their behavior through our endowment transparency and pension transparency reports.
College endowments are famously opaque, providing so little information that their once-a-fiscal-year reports have become a regular autumn spectacle. But seldom do these reports contain much real information about how endowments achieve results, how they decide which risks to take, or how outsiders can independently verify their results.
Disclosures from endowments are minimal, typically consisting of annual performance figures, updates to policy portfolios and general commentary. Endowments often have highly restrictive media policies. Even many institutional consultants struggle to explain top-tier endowment results. Although much has been written on the endowment model for investing by gurus like Yale CIO David Swensen, those seeking to understand tactical allocation shifts, strategy preferences, and manager selection are given little to work with on an ongoing basis.
U.S. pension funds have been plagued by chronic underfunding, decreased disclosure, and even allegations of criminal behavior. Stakeholders don’t have the option of moving their money elsewhere. MPI believes that stakeholders in public, union, and other pensions should be able to:
- Validate pension disclosures and reconcile them with actual returns.
- Identify the risks pension funds take.
- Assess whether pension fund risk exposure has resulted in commensurate returns.
- Determine whether current levels of risk are appropriate for a fund’s stated risk tolerance.
2. Why MPI?
MPI is a leader and innovator in helping large investors understand complex portfolio dynamics and the drivers of return and risk.
That’s why government regulators, top institutional investors, and investment management organizations look to MPI as part of their fund due diligence and surveillance. And it’s why investigative reporters use us to help uncover red flags (like in our analysis of a feeder fund to Bernie Madoff’s strategies.)
The same technologies that drive our products and services were recognized with best academic paper prizes. Here are some of the unique benefits MPI brings to the industry:
- Strategy validation. Was it possible to achieve reported results using reported allocations? Can one trust the returns or allocations? Are there hidden risks?
- A common denominator for comparing opaque investments. Every endowment or pension fund is unique in how it defines asset classes and categories of alternative investments, making apples-to-apples comparisons very difficult. Our tools can standardize allocation types across endowments, allowing a meaningful picture for comparison.
- Netting exposures. Effective equity and fixed income exposures can be netted by shorting, leveraging, or the use of derivatives. Risk managers and CIOs use our factor analysis to uncover hidden exposures that truly drive returns – often unexpectedly.
- Uncovering hidden allocation trends. Harvard’s restructuring after the global financial crisis, Brown’s big bet on tech, Duke’s dive into crypto, and now, endless public statements about fossil fuel divestment. Our tools allow a sharper picture of flows in and out of allocation categories, helping reveal which funds’ behavior matches their PR.
- Risk and Efficiency. Endowment and pension press often focuses on returns and ignores the risk and performance efficiency analysis every investment needs. Large endowments are long-horizon investors that lack the constraints of regulated investments. And underfunded pensions could be in danger of chasing returns through high-risk investments. MPI helps uncover an investment’s risk/return efficiency picture even with only a few public data points.
- Performance attribution. Endowments had no problem boasting about asset class contributions to returns in FY2021; after all, it was a record year. But in down years, managers may be less incentivized to explain their results to stakeholders. Our tools help stakeholders form year-to-year performance attribution pictures to keep a close eye on ongoing behavior.
- Meaningful investment rankings. Most public lists focus on pure performance from year to year. But MPI’s inclusion of risk and efficiency metrics helps tell more meaningful stories, shining a light, for example, on smaller endowments and pensions that are more efficient than their larger and more prominent peers.
When only annual performance figures are reported, a decade’s worth of performance is represented by 10 data points. Traditional static and rolling window methods of regression analysis struggle to find credible insights from such infrequent data. MPI’s Dynamic Style Analysis (DSA), however, is uniquely able to work with such limited data.
DSA improves upon Sharpe’s original returns-based style analysis (RBSA) approach and, using factors indicative of the asset classes deployed by pensions and endowments, provides significant insights into their behavior. With our DSA, MPI is able to explain changes in an institution’s performance over time and to highlight differences across institutional portfolios using a common analytical framework.
MPI’s Dynamic Style Analysis (DSA) Methodology
- Uses publicly available annual returns and public and private market indices/factors.
- Captures changing portfolio exposures in ways other methodologies can’t.
- Cross-validates selected factors and model parameters through rigorous machine-learning algorithms.
- Computes estimated return contributions by asset both for the most recent fiscal year and the entire history.
- Computes risk measures, drawdowns, stress tests and hypothetical shocks using quarterly public proxies and the most recent factor exposures.
For a much deeper dive into the MPI approach and our proprietary methodology, see this CAIA-published paper.
4. MPI-360 Reports
The takeaways from pension and endowment transparency reports found in this Hub are not much different from the ones that MPI clients derive on a daily basis when using our tools to analyze and compare individual mutual funds, hedge funds, PE and VC funds as well as portfolios of funds.
Individual MPI fund reports…
- Uncover trends in asset allocation and exposures.
- Explain more recent and historical results and compare them to benchmarks and peers.
- Provide estimates of risks, drawdowns and efficiency
- Calculate historical stress-tests and hypothetical scenarios
5. Frequently Asked Questions
As a result, exposures obtained through factor analysis of returns may not match reported asset allocation but could provide more accurate information for both risk managers and CIOs as the effective exposures rather than the reported ones are, in fact, driving returns.
By definition, “Selection” is a portion of return that is not explained by our factor model. Selection might include fees, missing or imprecise factor effects, rapid changes in allocation that cannot be captured by annual data, massive write-offs or restructuring, individual success stories such as the Coinbase investment by Duke, etc. Fortunately, multibillion dollar portfolios are not easily turned over quickly, especially when significant portions are in illiquid private investments, and this makes our analyses possible and more accurate. Nevertheless, many try to see manager selection skill behind some endowment success stories. In most cases, such success is driven by a common factor we can identify. For example, we determined overweight of technology as the primary source of the Brown endowment’s winning streak.
As mentioned above, asset class exposures obtained through our analyses may differ from stated allocations but, if used to replicate an institution’s results out-of-sample, would deliver very close returns.
We look at the largest endowments and find striking similarities in their asset class exposures. At the same time, some endowments stand out both in terms of allocations and FY2016 performance....
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. ...
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. ...