Monte Carlo analysis is an excellent tool for your retirement planning strategy. It can help you understand how much you can safely spend throughout retirement, prepare for uncertainty, and define your overall retirement plan.
As with all predictive modeling, there’s no such thing as absolute certainty. Monte Carlo analysis comes with caveats, so it’s best to use it less as a crystal ball and more as a probability model to help you make informed decisions.
Key Takeaways
- Monte Carlo simulations determine the probability of outcomes among randomness and uncertainty.
- Monte Carlo analysis aims to visualize the simulation data under different scenarios.
- There are caveats and guardrails to set before taking Monte Carlo analysis at face value.
Monte Carlo Simulation: An Origin Story
As the name suggests, the Monte Carlo method originated with the gaming industry, just not in the way you think.
Mathematician Stanislaw Ulam first developed it while recovering from surgery and playing solitaire. He wanted to determine the probability of success given the random dealing of cards, and Monte Carlo was a code name he used for the simulations.
Since then, physicists, scientists, project managers, and financial planners have all used Monte Carlo simulations.
The Basics of a Monte Carlo Simulation
In the world of financial planning, the simulation can run off of a set of values used to determine the returns or growth of a particular stock. Using historical or statistical data, it runs thousands of simulations that account for variables like inflation and bear or bull markets.
The end result is a bell curve showing the probability of success. The center of the curve represents the scenarios most likely to happen—these are your “best bets.” The tails on either side represent extreme scenarios like markets crashing or booming.
The chart below shows you the likelihood of returns based on the NASDAQ 100. Most stock accounts will provide a daily return of 0.5-2%, which translated to 180%+ annually.
How Monte Carlo Analysis Works
Having the simulation data isn’t enough to make sound investment decisions. The next step is to analyze it by adjusting for different scenarios. A common question Monte Carlo analysis can answer is the probability of success—having money for the duration of your retirement—based on your annual spending needs.
The analysis can assume several variables, like your portfolio allocation, age, projected rates of return, desired income, and risk tolerance. Then you’ll receive charts to visualize your retirement plan and add more context to the data.
Looking at this graph, our minds instinctually gravitate toward the 93% probability of success option. You can also do some quick math and realize there’s still a 7% chance that your retirement plan will fail, i.e., you’ll run out of money.
On the surface, 7% might feel like a lot, causing a desire to push the graph to 100% and eliminate any risk of failure. But doing so comes with a costly tradeoff of reducing your current lifestyle by being too restrictive.
Taking Monte Carlo To The Next Level
The issue with this hyper-focused analysis is that it assumes constant volatility and consistent spending—neither is realistic.
But, there are extra probability tricks to make your retirement plan more dynamic. You’re about to read some technical, statistical terms, but they’ll help you understand how to inject Monte Carlo analysis with even more randomness that’s typical in real life.
- Beefing up fat tails. Fat tails are the small ends of the bell curve. Typically, Monte Carlo simulations don’t generate enough fat at either end, meaning you could underestimate the bad scenarios and overestimate the probability of success. By making the tails fatter, you can more accurately account for these extremes that markets have at times exhibited.
- Random volatility. Basic Monte Carlo simulations assume constant volatility, but the markets (and life in general) don’t operate that way. A more thorough simulation accounts for the ebbs and flows of such volatility.
- Correlation between equity and volatility. There’s a clear connection between equity and volatility. When markets are up, volatility is low. When markets are down, the increased volatility can make your returns uncertain. As planners, we’re particularly concerned with low equity and high volatility scenarios.
All of this adds up to infusing a Monte Carlo simulation with more randomness by using a stochastic volatility model. Where a basic simulation will account for random variables, a simulation with a stochastic volatility model assumes that the simulation itself is also random. This assumption makes the data more true to real-world conditions.
Applying Monte Carlo Analysis to My Retirement Plan
Using a sophisticated simulation model allows you to analyze your retirement plan in much more detail.
At Metanoia, we use Monte Carlo analysis to provide financial modeling, portfolio projections, optimize your portfolio allocation, and help you determine your ideal range of retirement spending. Remember, the analysis is supposed to give you context for the data provided by the simulation so you can make informed decisions about your retirement plan.
To start, we use a stochastic volatility model with our Monte Carlo simulations to ensure we’re protecting your retirement nest egg when the market is down.
On top of measuring the probability of success of a retirement plan, we also provide detail on the actual dollars you can spend. But since that topic can get a bit in the weeds, we’ll dedicate it to another post.
Putting It Altogether
There is so much data to analyze and different scenarios to play out when creating a robust retirement plan that it really pays to have a partner who can walk you through it.
At Metanoia, we leverage the best models to help you construct a retirement plan that fits your desired lifestyle and can adjust to market changes. We’ll run simulations before and after retirement to make sure your plan continues to meet your needs and goals.
Book a free consultation to set up or review your retirement strategy today.
