Forecasting Methods
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 methods which are commonly used in financial planning to help you evaluate the uncertainty and risk around expected portfolio growth and portfolio survival based on withdrawals and assess your financial plan.
A Historical simulation is a method that relies upon returns from historical data to simulate potential portfolio performance and plan outcomes over time.
A Monte Carlo simulation is a method that relies upon a specified probability distribution to simulate potential portfolio performance and plan outcomes over time. It provides a wider variety of potential scenarios than those provided by a linear or historic projection.
Overview of Monte Carlo
A Monte Carlo simulation is a technique based upon statistical analysis that simulates a range of possible outcomes for 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. The results of Monte Carlo simulations are expressed as a percentage of scenarios where there was money remaining at the end of retirement. For instance, a Monte Carlo score of 90 means that in 90% of the test simulations there was money remaining at the end of the plan, while 10% of the simulations ran out of money.
Boldin's Monte Carlo Simulation
Boldin requires you to input an average rate of return based on the performance history of your asset allocation or rely upon our portfolio models when entering your retirement and other accounts in the platform. Your rates of return serve as the basis of our Monte Carlo analysis.
Boldin conducts a Monte Carlo simulation with 1,000 iterations calculating a normal distribution curve (i.e. bell curve) of potential outcomes using your average rate of return and a reasonable standard deviation associated with your rate of return based on history to create variance.
The number of iterations where funds never run out divided by the total number of iterations forms your Retirement Chance of Success which indicates the probability that your portfolio will sustain withdrawals and you will be able to meet your financial goals and obligations.
Additional detail
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.
Percentiles
Note: The Monte Carlo Analysis page only displays the Average forecast.
Boldin's Monte Carlo simulation produces 1,000 distinct curves, each representing a different financial outcome over time. To interpret this wide range of possibilities, we utilize interquartile ranges, which illustrate the probability of achieving a specific financial position. This approach not only indicates the likelihood of your plan's success but also quantifies potential remaining assets under various scenarios.
Key percentiles are:
50th percentile (median): This represents the middle outcome, where half of the simulations resulted in a portfolio value above this amount, and half below.
10th percentile: This indicates a "bad but not catastrophic" scenario, as 90% of simulations yielded more than this amount, while only 10% yielded less.
90th percentile: This signifies a very optimistic scenario, with only 10% of simulations ending with a portfolio value exceeding this amount.
The span between the 10th and 90th percentiles effectively conveys the potential range of outcomes, highlighting the inherent uncertainty and volatility within your plan.
Higher return rates correlate with increased deviation, signifying greater risk and variability. When your plan incorporates higher growth rates, you'll observe greater variability, illustrated by the blue shaded area. This indicates the heightened volatility intrinsic to an aggressively allocated portfolio. Conversely, lower growth rates in your plan result in less variability, or a smaller blue shaded area, reflecting the reduced volatility inherent in a more conservatively allocated portfolio.
What you’re left with is a spread of outcomes that starts out relatively tight and predictable, but grows increasingly scattered the further into the future you look. This widening range reflects the reality we live in: as much as we might wish for a crystal ball, uncertainty naturally compounds over time. The longer the time horizon, the more variables can shift — markets, inflation, health, policy — making it harder to pin down exact outcomes. Monte Carlo analysis embraces this uncertainty, not to confuse or overwhelm, but to equip us to plan more realistically, with guardrails that account for both best-case and worst-case scenarios.
How can you use Monte Carlo to assess your plan and make informed decisions?
Given the inherent risks and unpredictability of investing, precise long-term financial forecasting is impossible. However, the Monte Carlo simulation serves as a valuable tool for understanding these risks and stress-testing financial plans.
This simulation allows you to test various strategies—such as altering your withdrawal rate, adjusting asset allocation, or changing your retirement age—to observe their impact on your probability of success. This process helps pinpoint the most resilient plan across a diverse range of potential future scenarios and prepares you for less favorable market conditions.
Ultimately, Monte Carlo simulations provide crucial insights, empowering you to make informed decisions regarding savings, retirement timing, and investment strategies. This understanding is essential for refining your financial plan and adapting it as conditions or goals evolve.
High Chance of Success
A consistent Monte Carlo success rate, particularly 90% or higher, suggests your financial plan allows for increased spending beyond current projections. This flexibility could lead to greater peace of mind, opportunities for higher expenses or charitable contributions, or a reduction in investment risk and its associated returns.
Low Chance of Success
If perhaps 20% or more of your Monte Carlo simulations indicate a poor outcome, it suggests your plan could be improved. Consider strategies such as increasing savings, reducing expenses, or adjusting your investment approach.
What other methods can you use to assess your plan and make informed decisions?
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.