The FiduciaryVest team of Joe DiNunno and Gregg Buckalew began building asset allocation forecasting models in 1994. We typically build financial models using @RISK for four types of investment projects for clients: Defined Benefit Pension Plans, Hospital Balance Sheets, Foundations/Endowments, and customized Target Date Funds for Defined Contribution Plans (401k, 403b, etc.).
Before starting a project, FiduciaryVest establishes each client’s preferences by discussing the following considerations with them:
- The time horizon for achieving the client’s defined investment objectives (typically 5 years, sometimes longer.
- A prioritized set of investment outcome objectives within that time horizon.
- A profile of the client’s risk tolerance, defined as exposure to “unacceptable outcomes” over the specified time horizon.
For forecasting situations that require an asset/liability analysis, financial models usually include projected cash flows such as: pension plan liabilities, hospital expenses, or annual foundation distributions. By adding liabilities to the modeling equation, we are able to determine, for example: (a) the probability of a foundation being able to meet its distribution requirements without draining its capital, (b) the probability of a defined benefit plan being able to meet its pension obligations while keeping cash contributions stable, or (c) whether the combination of a hospital’s cash flow with its portfolio income will be enough to meet its financial obligations over the next five years. (Our assignments often include working with a client’s actuary or hospital financial consultant).
The Building Blocks Approach
The usefulness of any forecast depends heavily on the realism of assumptions used to produce it. Rather than relying solely on historical returns-based assumptions for long range future behavior of the various asset classes , we favor of a “building blocks” approach. The foundation of all building blocks is the market-determined current risk-free interest rate, for the forward period that the client determines is its investing time horizon.
Beginning with that risk-free rate, successive ‘premiums’ are added to reflect each investment category’s historical rational risks. This method produces expected return, and range of returns, for each asset class based on its historical ‘behavior’ relative to the risk-free rate, which makes the model’s entire set of assumptions internally consistent and logical. For the remaining two assumptions needed in forecasting (the variability of each category’s expected return, i.e., its standard deviation, and its cross correlation with each of the other categories’ returns), FiduciaryVest believes there is strong evidence to support an assumption that long-term history is the most reliable predictor for those two factors.
Individual asset-class return assumptions are thus driven by the current risk-free market-driven interest yield. For example, as of August 2011 the model’s “foundation block” for constructing the expected returns of all asset classes was a 1 to 2% risk-free rate. By comparison, the long-term historical risk-free rate has been 5 to 6%. As a result, our 5-year expected return for Large Cap Equities modeled in August 2011 was only about 8%, compared to its long-term historical average of a little more than 11%.
Lessons from the 2008 Financial Market Debacle
The financial crisis of 2008 convinced us that it was time to look closer at our use of the normal distribution for modeling the dispersion of investment returns for equities. While the normal distribution assigns about 3 chances in 1000 for the stock market to experience a 3 standard deviation decline, such as the market experienced in 2008, the reality is that a move of this magnitude occurs about once out of every 50 time periods. Accordingly, we adjusted the return distribution of equities in our model so that it now produces ‘Fat Tails’ (outlier events that occur more frequently in reality than would be predicted by the normal distribution’.
Another observation from the market upheaval in 2008 was the fact that correlations between most asset classes were temporarily much higher than almost any period in history. In contrast, a few asset classes used by FiduciaryVest that historically have had a low correlation with equities actually produced positive returns for 2008, a result that makes sense. Our models now use higher correlations between some asset classes than we had previously used.
Practical Considerations
Incorporating multi-year time horizons and ‘Fat Tail’ probability distributions in our asset allocation models, allows FiduciaryVest to help clients answer questions that cannot be addressed using static or optimized financial models. Some of those questions are:
- What is the probability of a negative return over a specific time period like 1 year, 3 years, 5 years, or 10 years?
- What is the probability of the client’s portfolio reaching (or not reaching) its targeted return over a give time period?
- What is the Value-at-Risk (or more importantly the conditional Value-at-Risk) for an investment portfolio?
- What is the probability of meeting a client’s financial obligations (i.e. benefits payments or spending distributions, or cash flow expenses) without tapping into portfolio principal?