An online search of “capital market assumptions” (CMAs) yields a large number of hits, with the top results dominated by the latest projections from larger asset management firms and investment consultants.
In their simplest form, CMAs are comprised of the mean-variance optimization statistics that are at the heart of modern portfolio theory, which describe expectations for the returns, volatilities, and correlations of the assets to be employed.
Asset owners use those statistics in models that lead to actions outside of the investment portfolio — such as funding and benefit decisions for pension plans, or spending policy and budget choices for foundations and endowments — and which provide a framework for asset allocation policies.
An expression of beliefs
The formation of the assumptions proceeds differently based upon the size and expertise of an organization. Smaller entities typically look to an investment advisory firm, institutional consultant, or OCIO to provide CMAs, which may be put into use with relatively little examination. The largest organizations usually have the in-house expertise that allows them to build the assumptions from the ground up, using their own capabilities and tapping a variety of outside sources.
Whether home-grown or off-the-shelf or something in between, the CMAs are an expression of beliefs. As such, in draft form they should serve as a vehicle for a discussion of those beliefs, the proposed choices, and the implications of them — well before the CMAs are adopted and implemented.
Questions
Here are a few of the questions that should be asked of those who have arrived at the draft assumptions:
Are the numbers primarily forward-looking or backward-looking? Expectations are largely anchored by past experience, but future return profiles are a function of current conditions, subsequent developments, and the forecasted time horizon. The location on the forward/backward spectrum will result in different projections, sometimes quite different, regarding returns, volatilities, and correlations. Another consideration: Larger disconnects between history and forecast are more difficult to communicate to stakeholders. (Procyclical behavior is evident. See, as one example of research on the topic, “The Return Expectations of Public Pension Funds,” by Aleksandar Andonov and Joshua Rauh, which argues that “institutional investors rely on past performance in setting future return expectations, and that these expectations affect their target asset allocation policy.”)
Are the essential elements of the forecasts presented in a granular but clear fashion? CMA documents can range from the sparse to the bloated. What is most helpful for those trying to understand the choices made is to identify the component factors for each asset class and to convey them in a straightforward manner. A bare-bones presentation of the bottom line doesn’t provide enough context, while a comprehensive approach — which may provide deep detail on economic factors — is often too much information for users.
How are the uncertainties around the assumptions expressed? It is quite common for there to be too much attention to the statistics and not enough exposition of the range of possibilities. What stress tests and scenario analyses were done? Did they go beyond the standard approach of modeling some of the notable crises of the past to consider more drastic events and outcomes? Contemplating those out-there situations is the first step to being able to adapt to varying conditions going forward.
Related to that, are there developments that could cause the expectations to be wrong in substantive ways? Since deviations from plans in a positive direction aren’t really a concern; the focus is on downside surprises. While every period is fraught with uncertainty, is this one more unusual than normal? The participants in a webinar from Portfolio Management Research on “novel risks” made the case that it is. On the economic front, we are in a “yield- and return-starved world,” with a current inflation rate never before seen by many investors and sizable central bank balance sheets. Then there are pandemic, cybersecurity, and digital asset risks, all pretty new. And the big one — climate change — carries industry, regulatory, economic transition, and geopolitical risks. Put it all together and you have a situation where our existing models and analyses are lacking, and overreliance on historical information could be particularly dangerous.
Are the expectations and perceived requirements for returns affecting the objective evaluation of prospective returns in any way? The decline in expected returns, especially over the last decade, has been met by a variety of tactics. Most prominently, portfolios are riskier and less liquid. Also, there has been an increasing use of leverage, not just within alternative asset exposures, but on portfolios themselves. The shortfall from declines in expected returns can also be closed by nudging the assumptions in favorable directions, kicking the can down the road in the hope that things break in a positive way. Both internal analyses and those from an outside advisor can be subject to the dangerous tendency to make those kind of adjustments.
What are the range of expectations in a given asset class that are used by others? While the goal shouldn’t be to follow what others are doing, you can get important perspective by seeing the spread of forecasts from those who publish CMAs. One easy way to do that is by viewing the surveys of CMAs published by Horizon Actuarial Services. (As noted in the methodology sections of the reports, one issue to contend with is the lack of consistent definitions of some of the asset classes.)
How do you come up with your private equity assumptions? Take a look at this graphic from Horizon’s 2021 survey (these are ten-year CMAs):
The individual asset classes have their own indicators; for example, each red triangle represents the expectations for private equity by a surveyed firm. As you can see, they are all over the place. On the horizontal scale, there are standard deviation assumptions of less than 10% out to 35%. The former must believe that the smoothed numbers reported by general partners represent the true volatility of PE, while the latter scoffs at such an approach. In terms of returns, there’s a clear outlier, miles above everyone else. Is that based on historical returns or something else?
What about venture capital? Venture isn’t mentioned in the Horizon report (so the assumption is that it is in the private equity bucket). But it presents a great example for a debate on how much current conditions should affect forward expectations. The extraordinary results of late from a number of venture investors, especially university endowments, would bias many toward keeping the forward assumptions robust, and maybe even moving them higher. But the last time venture had a cycle like this, the subsequent ten years were miserable. What do you do?
How about private credit? Not even recognized as an asset class by most asset owners just a few years ago, private credit has surged in popularity. There are a number of different strategies under that umbrella, historical information is sparse, and the size of assets employed has grown dramatically. How do you come up with a forecast?
Do the CMAs include alpha expectations? There are firms that publish CMAs which include expectations of alpha within some categories (typically alternatives); you normally have to check the footnotes to find that out. They argue that they are able to produce that kind of outperformance, so it should be included, but that’s far from a given (and alpha tends to erode over time). In any case, it muddies the waters. Such an approach shouldn’t be considered a best practice; you should forecast and use beta returns for planning purposes, and let anything above that be a pleasant surprise.
The end result
Ultimately, if you are using a mean-variance optimization (or another approach), you will need to arrive at a set of numbers to use. But the most valuable part of the endeavor may be the scrum leading up to that adoption, with its consideration of the questions above, in addition to others.
A good process should document the uncertainties and weaknesses involved with the final conclusions — there will always be many of them — along with the range of possibilities that were explored for individual issues and why specific choices were made.
Examining potential CMAs and adopting final ones can be a check-the-box exercise or it can be an enlightening journey that prepares you for what’s to come. Which description comes closer to characterizing your approach?
Published: January 20, 2022
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