Monte Carlo
Developed by John von Neumann and Stanislaw Ulam in the 1940s, Monte Carlo models utilize statistics and math to predict the outcome of unknown future events. Known as a “probabilistic model,” Monte Carlo scenarios can incorporate an element of uncertainty or randomness into their predictions. The models will return different results every time, but – when taken on a large scale – they can determine the probability of specific outcomes.
For example, the distance between your home and office remains constant, but your commute can vary depending on factors such as traffic, weather, accidents, errands, or breakdowns. You could use a Monte Carlo model to simulate 1,000 trips to and from work. The results would show you a consistent median travel time, but also high and low probabilities.
For your investment plan, Monte Carlo tests will factor in decades of economic variables to test your investment plan. The Retirement Success Graph app runs up to 10,000 different variations to determine if your investment plan is likely to meet your financial goals. By continually changing the variables of investment returns, inflation, expense variations, market variables, and many more, the app can provide a high level of statistical accuracy for your retirement plan and help guide you on your path to Financial Independence and Retiring Early.