Monte Carlo: this method uses special mathematical calculations to create random scenarios of investment returns one might encounter over his or her lifetime.

What is Monte Carlo?

Named after the games of chance in the casinos of Monte Carlo in Monaco, Monte Carlo Simulation (MCS) uses random sampling of returns to execute simulations for forecasting financial strategies. During an analysis using MCS, a large number of trials are performed using an expected return and standard deviation that the advisor (or client) has selected to constrain the data. By adjusting key variables (such as asset allocations, retirement spending needs, retirement age, etc.) and applying client priorities, the advisor may create a strategy with a high confidence in achieving goals in a variety of markets.

How do we apply Monte Carlo in the context of Wealthcare planning?

The Wealthcare process is designed to help clients achieve their most important financial goals without needlessly sacrificing their lifestyle and while avoiding unnecessary investment risk. Wealthcare planning is designed to help each client live his or her life in financial security.

The priority assigned to each of a client's goals, along with the investment risk the client is willing to accept, combine to set the stage for the Monte Carlo Simulation analysis. In this analysis, we determine the likelihood the client may successfully achieve his or her objectives by simulating various market performances over the client's lifetime and assessing the impact of these markets on the client's most important financial goals.

This analysis, while complex, is easily understood by the summary numeric results described below:

• The Summary states the number of simulations that achieved an ending value greater than the target value. This number is also stated as the percentage of simulations in which the client's valued goals were achieved or exceeded.


• In addition, the Summary states the number and percentage of simulations that ended below the targeted ending value but did not run out of money during the client's lifetime.


• The Summary also states the number and percentage of simulations in which the plan ran out of money prior to the end of the planning period.

The results of the Monte Carlo Simulation analysis may also be expressed in terms of the Comfort Assessment, indicating whether the client may have confidence that the tested strategies will be comfortably achieved or whether the goals fall into Uncertainty or Sacrifice.

In addition, MCS on Financeware may incorporate mortality risk. Random mortality uses life expectancy tables and randomly introduces lifetimes into the simulations. Each one of the 1,000 trials performed uses a randomly generated lifespan that determines both plan duration and the timing of certain cash flows. The MCS calculation method supports either fixed or random lifespans in determining results.

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