Monte Carlo Analysis and Your Plan

Linear simulations assume a fixed return each year based on long-term averages. They’re simple, easy to follow, and useful for setting expectations—but they don’t reflect real-world variability.

Non Linear methods

Historical simulation and Monte Carlo simulation are two methods used to simulate potential outcomes of unpredictable situations. They are both commonly used in financial planning to help you evaluate the uncertainty and risk around expected portfolio growth and portfolio survival based on withdrawals, and as a result assess your financial plan.

Historical simulation is a method that applies historical returns to simulate potential outcomes.

A Monte Carlo simulation applies random returns, modeling real uncertainty and showing a range of outcomes instead of a single path. It can be reassuring to know that the plan will be successful in a wider variety of potential scenarios than those provided by a linear or historic projection.

A Monte Carlo simulation is a statistical application which applies random simulations to an uncertain situation. The objective of a Monte Carlo Simulation is to assess the risk inherent in long term predictions and support informed decision making. It was named after the resort town Monaco, known for its casinos, since the element of chance is essential to the method, similar to a game of roulette.

A Monte Carlo simulation provides a range of possible outcomes and the probabilities or likelihood that they will occur. In financial planning it is often used to evaluate the probability that your portfolio will last and you will be able to achieve your financial goals. This allows you to make sound decisions about other elements in your plan such as spending, investment strategy, long term care planning, legacy planning and more.

Boldin’s Retirement Chance of Success Score indicates probability that your portfolio will sustain withdrawals and you will be able to meet your financial goals and obligations. It is based upon our Monte Carlo Analysis.

Your Boldin financial plan accounts for all of your modeled assets, income, and expenses, projected RMDs, withdrawals, gains, taxes and more through your entire lifespan.

In order to assess assess a range of possible outcomes, the Boldin Monte Carlo analysis runs your plan through 1,000 simulations with varied market returns.

In our Monte Carlo Analysis, we apply a normal distribution to your rates of return. We generate 1,000 random simulations and then randomize the values along a normal distribution each month of the simulation. We then divide the number of simulations where funds never run out by the total number of simulations, and reflect that number back to you in your Retirement Chance of Success.

The model infers a standard deviation from the rate of return for each account under the forecast assumption selected. It selects the standard deviation from a lookup chart, in which we’ve chosen values which loosely mirror the standard deviation of different asset classes. For example, a rate of return of 10% will result in a standard deviation associated with growth stocks. You may view the standard deviation in the Rate Inspector for each account.

The blue line and shading in the Monte Carlo Explorer Chart display the various percentile markers from the Monte Carlo analysis. The blue plot line represents the median portfolio value each year.

Each one of the 1,000 Monte Carlo "iterations" has a different curve over time. In order to make sense of this spread of 1,000 iterations, we use interquartile ranges to show the probability of ending up at a certain place. This helps you understand not just if your plan is likely to succeed, but how much you might have left under different scenarios.

The deviation increases with higher return rates to represent increased risk and variability. If you have higher growth rates in your plan, you’ll see greater variability, as represented by the blue shaded area. This reflects the increased volatility that can be inherent in a portfolio with an aggressive allocation. If you have lower growth rates in your plan, you’ll see less variability, or blue shaded area. This reflects the lower volatility that can be inherent in a portfolio with a more conservative allocation.

We also see a spread of results that starts off tight and predictable, and as we project further into the future, becomes more scattered and variable. This is in fact representative of the world we live in. As much as we may wish to have a crystal ball and know what will happen, the further out we plan, the more that can end up happening.

Due to the risk and unpredictability inherent in investing, precise long-term financial forecasting is impossible, and the Monte Carlo simulation can be an important tool to support robust planning.

You can test different strategies-such as varying your withdrawal rate, changing asset allocation, or adjusting your retirement age-to see how they affect your probability of success. This helps identify the most robust plan under a wide variety of possible futures and prepares you for less favorable market conditions.

Monte Carlo simulations empower you to make better decisions about how much to save, when to retire, and how to invest. This insight is crucial for refining your plan and making adjustments as conditions or goals change.

If your Monte Carlo consistently reflects a high degree of success - where perhaps 90% or more of simulations result in success - this indicates that you may have some flexibility in your spending above the expenses projected in your plan. As a result, you might breathe easy, or explore ways to increase expenses, charitable giving, or reduce investment risk (and associated return.)

If your Monte Carlo reflects a poor degree of success - where perhaps 20% or more of simulations result in a poor outcome - this indicates that your plan might benefit from improvement. As a result, you might explore ways to save more, spend less, or change your investment strategy.

The Monte Carlo Analysis is just one of a variety of ways to evaluate your plan. We recommend measuring your progress towards your specific goals against a number of indicators including your Chance of Success, Net Worth, ability to meet your spending and legacy goals, and others.