Monte Carlo Simulation: Planning for Uncertainty to Better Understand Your Financial Future (Part 1 of 2)
By Brittany Mollica
You won’t be surprised to hear that we usually try to avoid casino analogies when we’re talking about investments. Still, we must acknowledge the element of chance when we place our money in the markets.
Investing is inherently unpredictable. The odds that you’ll see the exact same investment performance in your portfolio two years in a row are slim. The odds that you’ll see that same return for your entire life? Pretty much impossible.
However, when we run long-term financial planning projections for our clients, we select a flat, conservative rate of return to model yearly investment growth. We call this a “deterministic model,” and it’s much more feasible than trying to predict your actual future investment returns. Although a steady return makes for a useful projection, it’s not very realistic. The good news is we have a tool that supplements the usual deterministic model and gives us a more realistic picture: the Monte Carlo simulation.
Before we get into the details, some background: the Monte Carlo simulation is a mathematical method of using randomized data to understand a probability of outcomes. It was first developed in the 1940s by Stanislaw Ulam, a scientist who worked on nuclear weapons projects at the Los Alamos National Laboratory. Ulam’s work was top-secret, so he and mathematician John von Neumann needed a code name for his new mathematical method. They decided to call it Monte Carlo (not to be confused with a Monte Cristo, which is a fantastic fried sandwich) after the famed casino in Monaco where Ulam’s uncle used to borrow money from relatives to gamble.
The “Monte Carlo” moniker is apropos, as gambling is based on unpredictable outcomes and can yield a wide range of results. Nowadays the Monte Carlo method is utilized in a variety of fields, from science and engineering to finance and operations, and it has proven to be a useful tool in financial planning.
In the financial planning context, we use Monte Carlo to determine the probability that your plan will result in “success”. To do this, we:
Use your financial plan to create a new projection, running 1,000 different trials with randomized investment returns and inflation levels for each year. The investment and inflation numbers are based on historical data, so the trials create a realistic range of financial outcomes for each year.
Measure each of the 1,000 trials using a simple test: do your assets drop below $0? If you have at least $1 left over in a trial, then it’s considered a “success”. If $0, then it’s a “failure”.
Review the “probability of success”, which is simply the percentage of trials that “pass”, or end with at least $1 left over.
We do not expect a Monte Carlo simulation to spur you to change your planning strategy overnight. But we believe the simulation can instill greater confidence in your financial plan because it accounts for a wider range of possibilities, allowing you to view your financial plan through 1,000 different lenses instead of just one. We hope this ultimately helps you make better-informed decisions.
In our next post, we will discuss how to interpret your results from a Monte Carlo simulation and how to use this information to make wise decisions with confidence.
Meanwhile, if you aren’t confident in your financial plan and you’d like to better understand the possibilities of your financial future, please reach out to us!
Sources:
https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-88-9067
https://www.ibm.com/cloud/learn/monte-carlo-simulation
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