AllocationMaster: About

Setting investment objectives and asset allocation strategy is a dynamic process because future investment returns and inflation rates will fluctuate from year-to-year. As a consequence, when the client's investment objectives are stated as a target return objective, or as a target level of future asset value, the actual value will likely differ from the target value in any one year. Helping the client to cope with these normal fluctuations is an important part of the investment consulting process.

Monte Carlo Simulation is used within AllocationMaster to create a model of future capital market behavior. This is done for the purpose of subjecting the asset allocation plan to different market conditions that might actually be experienced. In this way, Monte Carlo Simulation is used to replicate financial market processes to evaluate the merits of alternative asset allocation strategies.

Monte Carlo Simulation is appropriate when the relationships under alternative investment strategies are sufficiently complex, uncertain and dynamic. Other analytical solutions that do not use simulation may not be able to adequately accommodate this type of problem. Analytical methods can be applied to problems that can be described by simple relationships.

Monte Carlo Simulation solves its problems experimentally rather than analytically. The same Financial Forecast process is repeated many times using alternative rates of investment return. From the results of these experiments are ranges of key financial parameters. This approach can be used to reveal the resultant financial consequences of the asset allocation plan in "operation."

Monte Carlo Simulation makes it relatively easy to test the financial consequences of alternative asset allocation strategies. Meeting the long-term investment objectives of the client can be efficiently evaluated and refined. The exhaustive nature of the simulation process should lend additional assurance that the asset allocation strategy is sound because it has been tested under many different contexts.

AllocationMaster uses Monte Carlo Simulation to project future rates of investment return and asset values, one year at a time, for the entire planning period (up to 75 years). The resulting sequence from a single AllocationMaster simulation is a series of investment returns, one for each year of the projection. The simulated investment return is derived through the use of a random number generator. The annual returns making up the sequence are in random order to reflect the unpredictable nature in which investment values fluctuate. An example of a simulated return sequence might be: Year 1-12.13%, Year 2-5.01%, Year 3-4.98%, etc.

The Monte Carlo Simulation procedure is repeated many times. Each repetition is referred to as a "trial." In AllocationMaster, 200 trials are performed for each year in the sequence. If the planning horizon is 20 years, then 4,000 (200 trials x 20 years) simulated investment returns are used! Some trials produce relatively large asset value projections that represent "best-case" experiences. Other trials will result in adverse "worst-case" experiences. The variation in simulated results will be more pronounced for riskier portfolios and longer time horizons.

The Monte Carlo Simulation feature in AllocationMaster makes it possible to understand the complex and dynamic nature of the client's portfolio. The results will demonstrate the likely volatility of investment returns and fund values. In addition, the probability of meeting the investment objectives of the plan can be evaluated for the Present Asset Mix and the Proposed Asset Mix.

Using Monte Carlo Simulation in AllocationMaster assists in modeling and quantifying the dynamic and uncertain nature of the asset allocation strategy. To the extent that the Monte Carlo Simulation procedure reasonably represents the investment conditions to be eventually experienced, it will produce a good estimate of the range of possible future outcomes under a particular set of client investment objectives.