Twenty years ago, Monte Carlo probability based methods found a place in the financial planning profession as advisors began to use these stochastic simulation models to show clients how different retirement strategies might be expected to perform under dynamic stresses of many varied potential economic and market driven futures. The advantage of probability based analysis over straight line deterministic illustrations lay in Monte Carlo’s ability to convey risk and volatility as unpredictable facts of financial life, and offer a technique to contrast and compare the risks and benefits of various planning options.
Deterministic vs. Monte Carlo Financial Planning Models
Deterministic, or constant based, financial planning creates a single forward looking financial illustration based on the assumption that the future is essentially predictable and average each year. Deterministic results represent the most common, or the mean, expected planning result that would occur if the average rate of return occurred each year. Deterministic results are simple, easy to read, and easy to explain. As long as everyone understands the assumptions and limitations, deterministic planning models are valuable tools to examine complex financial interactions. Deterministic models are also the essential fundamental underpinnings of advanced Monte Carlo Simulations.
Monte Carlo Simulation, or Stochastic Analysis, runs hundreds or thousands of plan models utilizing changing return assumptions for each year within each simulation. These simulations create collections of planning outcomes that might occur in hundreds and thousands of changing potential future real world market environments. The annual rates of returns used within the simulations are randomly selected based on expected average return and an expected return volatility.
Chaos Theory and the Law of Large Numbers are statistical concepts that support the Monte Carlo Simulation technique. By applying large numbers of simulations based upon specifically randomized market behavior modeling, a range of probable and potential financial plan projections can help demonstrate, measure, and compare expected future results and plan reactions under conditions of uncertainty.
