Asset Allocation Models

Little did we know when we started running financial simulation models back in 1994 that in less than 10 years the financial markets would provide us with investment return extremes that regular spreadsheet models never considered. The key to these models was the use of investment return probability distributions. Since the long-term expected return of equities is near 10% and the standard deviation is near +/- 16%, one would expect that 96% of the time returns should fall between -22% and + 42%. While the S&P 500 came close to the top of that extreme in 1997, it also managed to dip slightly below the low end in 2002. Technology Mutual Fund returns managed to fall outside of both extremes.

The goal of these simulation models was to seek the asset allocation which produced the highest total return with the lowest likelihood of producing a negative return by simulating uncertainty about the future using @Risk software. Although this analysis was used to show how corporations would be better off having some of their spare cash invested in the stock market and a broad mix of bonds rather than all of it invested in money market funds and short-term bonds, there were lessons we learned that can be applied to the individual investor. Our investigation showed us what would happen to an investment portfolio over a 5 year period in the 1 in 50 chance that negative investment returns during part of that time period were worse than even the “Worst Case Scenario” of a typical analysis. What we found was an asset allocation properly balanced between stocks and bonds would produce better returns and significantly reduce the overall risk of a portfolio versus a the more conservative approach.

Using the “building blocks” approach, expected return characteristics for each asset class are developed, based on its historical pattern of returns relative to the risk free rate. On the other hand, we believe that there is strong logic to support estimations of future standard deviations and cross-correlations by basing those estimates upon their long term histories. Individual asset class return assumptions are thus driven by the current risk free rate, as well as the projected risk free rate of return over the investment time horizon. For example, our model’s recent “starting block” for constructing expected returns used a 2-3% risk free rate, determined as of the beginning of 2008. By comparison, the long term historical risk-free rate has been 6-7%.

In summary, our investment asset class assumption sets have a strong forecasting bias in favor of assumed central tendency, adjusted for the current cyclical stage at the time a portfolio model is created. The result is an internally consistent set of assumptions that run in a real-world modeling context to produce forecasted outcomes that are not only robust, but also practical for an average client decision-maker to understand and consider.

For more details on our Asset Allocation Process, read how we team with FiduciaryVest to use @Risk for Asset Allocation Modeling.

The traditional approach to asset allocation has been to use Efficient Frontier models that seek to find the optimal portfolio mix that has the lowest possible level of risk (standard deviation) for its level of return (mean). When all asset class returns are assumed to follow the normal distribution, an efficient frontier model will yield risk vs. return results that are consistent with our simulation model over a one-year time frame. While this approach provides useful results, it still leaves many questions unanswered. This is where tools like @Risk and Monte Carlo simulation modeling come into play.

An obvious advantage to using @Risk and simulation modeling in preference to the simpler optimization techniques of the efficient frontier model is that we can realistically incorporate the effect of time horizons longer than one year. This is important because: (1) If volatile assets (e.g., stocks) are included in the model, then one-year modeling output has little chance of realism; (2) Consequently, users of modeling output that includes stocks and other volatile assets are forced to consider time horizons of at least 3-5 years; (3) @Risk’s probabilistic modeling output demonstrates to model users how multi-year time horizons increase the beneficial diversification effects of adding asset classes to the portfolio mix.