A Review of Monte Carlo Simulation

Categories: Invest, Plan

Conventional retirement projections in most retirement financial planning software packages and calculators looks at everything the moment you enter it, such as your goals, assets, benefits, and expenses coupled with assumptions for taxes, rates-of-return and inflation. Since this is just a calculation it doesn’t measure probability of all of the many variables that could possibly help or hinder your goal achievement. Therefore financial plans require ongoing updates.

Within the last decade most financial planning software programs have added Monte Carol Simulation as one way to visualize and examine the effect of unpredictable financial market volatility and other factors on your retirement plan.

Monte Carlo Simulation introduces random uncertainty into the assumptions and then runs the model a large number of times. Observing results from all these changing results can offer a view of trends, patterns and potential ranges of future outcomes illustrated by the randomly changing simulation conditions. While Monte Carlo Simulation cannot and does not predict your financial future, it may help illustrate for you some of the many different possible hypothetical outcomes.

Ten thousand full financial plan calculations are performed utilizing the volatile annual rates of return in our software program (eFinPLAN.com). The result is ten thousand new hypothetical financial plan results illustrating possible future financial market environments. By the use of random rates from a statistically appropriate collection of annual returns and repetition of the process thousands of times, the resulting collection can be viewed as a representative set of potential future results. The tendencies within the group of Monte Carlo Simulation results—the highs, lows and averages—offer insight into potential plan performance that may occur under various combinations of broad market conditions.

Most Monte Carlo Simulation reports, and those of eFinPLAN provide a:

  • Bold Line
  • Percentage of Monte Carlo Results Above Zero at Selected Ages
  • Monte Carlo Simulation Minimum, Average and Maximum Dollar Results

The Bold Line

The bold line in the Monte Carlo Simulation Results graph tracks the value of assets over the length of the illustration if all rates of return are held stable at the assumed rates of return. The estimate uses annual expected portfolio rates of return and inflation rates to model the growth and use of assets as indicated under Assumptions.

Percentage of Monte Carlo Results Above Zero at Selected Ages

These results represent the percentage of Monte Carlo simulation outcomes that show positive retirement asset value remaining at different ages. A percentage above 70 at last life expectancy is an indication that the underlying retirement plan offers a substantial probability of success even under volatile market conditions. Additional ages shown give the percentage of simulation outcomes with positive asset amounts at various ages.

Monte Carlo Simulation Minimum, Average and Maximum Dollar Results

These values indicate the best, worst and average dollar results at the end of the five thousand Monte Carlo Simulations. These show the range of results (high and low) and the average of all Monte Carlo results. All values are based on results at the life expectancy of the last to die.

Conclusion

Monte Carlo Simulation is a great tool; however, the most important thing to remember that is that financial planning is a process, and part art and part science. Regularly monitor your plan while seeking help from trusted professional advisors. Simulation results demonstrate effects of volatility on rate of return assumptions for education and discussion purposes only. The projections or other information generated by the plan regarding the likelihood of various investment outcomes are hypothetical in nature; they do not reflect actual investment results and are not guarantees of future results. Each Monte Carlo Simulation is unique; results vary with each use and over time.

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