Asset Allocation Tool
All institutions are in a constant state of portfolio risk. While the specific sources of risk vary based on a portfolio’s allocations, instruments and managers, it is widely accepted that asset allocation is a significantly greater source of portfolio variability. For that reason, we chose to focus on asset allocation as the variable we attempt to model.
Asset definitions are not universal, so the first step in modeling the variability of asset allocation is to control for the nominal differences in asset definitions across institutions. To do so, we reviewed dozens of institutional investment policy statements to understand the entire universe of asset classes, illustrated below by three real (though anonymized) institutions.
|Marketable Alternatives & Credit||12%|
|Fixed Income & Cash||10%|
|Broad US Equity||33%|
|Broad International Equity||19%|
The Venn diagram below shows the similarities and differences of asset class definitions across all three institutions. Our definitions of asset classes may not be all-encompassing or omniscient, but they are segmented thoughtfully enough to successfully model an institutional portfolio to uncover issues therein.
Long/Short Equity is its own asset class in this particular IPS, which is uncommon. Upon closer examination, the components of this asset class are more similar to the more common conception of Hedge Funds. Within our tool, Long/Short is available as a subcategory under Hedge Funds.
Marketable Alternatives and Credit appears to be a reference to high yield government bonds and is not seen in any of the other IPSs we’ve evaluated. Within our tool, High Yield Bonds are available as a subcategory under Fixed Income.
Resources are given their own classification, which we have seen in other IPSs. Within our tool, Natural Resources are available as a subcategory under Real Asset.
Cash is listed as opposed to Cash Equivalents. Across the IPSs we have seen, the tie that binds appears to be the classification of T-bills as Cash. Every allocation to Cash or Cash Equivalents within the tool is assigned as T-bills.
The IPS definitions are very generalized. Within our tool, asset subcategories exist to help make more granular determinations.
Real Estate is listed as an asset class, which we view as a subset of Real Assets.
Public Equity, Private Equity, Fixed Income, Real Assets, Cash all appear in one form another.
Cash is distinguished from Fixed Income; this distinction was kept.
Hedge Funds have their own category. Many IPSs refuse to list hedge funds and often fallaciously categorize these allocations as Real Return, however, we believe Hedge Funds deserve their own asset class.
Real Return is distinguished from Real Estate. Upon further investigation, some of Institution C’s Real Return items are real assets like MLPs or Timber, but others are more similar to hedge fund allocations. In cases of conflicting definitions, we have to make an assumption about the breakdown of the choices.
Broad US Equity is distinguished from Broad International Equity; this distinction was kept.
The following are relevant features of IPSs that are not present in either of the institutions used in our examples, but are covered as categories or subcategories within our tool:
Private Credit (or similar) is an independent line item. The prevalence of this asset class is growing rapidly and its return profile is distinct from both Public Fixed Income and Private Equity.
Venture Capital and Leveraged Buyouts are distinguished from Private Equity in some IPSs. We don’t believe this distinction is important enough for us to make.
Emerging Market Equity is distinguished from International Equity. Though none of the three institutions shown make this distinction, many others do. We view these assets as distinct enough that they should be separated.
The following tables and graphs present the performance figures of the three anonymized institutional portfolios compared to the predicted performance simulated by our framework. All IPS replications are reproduced in a reasonably accurate manner, though the framework will rarely ever reach 100% accuracy due to variances related to instrument, manager selection, data availability, our desire for universalizability, and changes in asset allocation that institutions make across time.
While we don’t think three institution examples prove a rule, we believe that this level of precision is sufficient
for gaining a picture of a portfolio with enough resolution to diagnose issues.
Once you have determined the desired asset allocation, the next step in the Wizard of Odds tool is to define the desired performance characteristics of the portfolio. While there are many variables to consider, we have chosen target return, volatility and max drawdown as key measurable factors that define how well a portfolio performs over time.
In the Wizard of Odds, you define these parameters as well as the percentile desired to see how closely the portfolio will perform against these criteria through Monte Carlo simulation, identifying the potential distribution of outcomes over the next 10 years. This functionality provides the ability to see how likely you are to achieve the portfolio's desired levels and how much this changes with different underlying asset allocations. We can also provide a more detailed summary report showing possible enhancements to the portfolio that may increase the likelihood of achieving the desired goals.
With the ability to design a detailed asset allocation and apply thoughtful portfolio performance criteria, the Wizard of Odds takes this framework and distills it into an interactive tool for helping investors uncover potential concerns within their portfolios.