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Today we’re going behind the scenes on something that drives almost every modern financial plan: the assumptions inside a Monte Carlo analysis.
If you’ve ever seen a plan that says you have—say—an 80% or 90% probability of success, here’s the question most people never ask: where do those percentages come from? Because they’re not magic. They come from a small set of inputs that shape every simulation the software runs.
Today I’m going to break this down into three parts. First, what Monte Carlo analysis actually needs in order to run. Second, the most common ways advisors come up with those inputs. And third, how we do it at Plancorp—and why we prefer our approach for long‑term investors.
This is a topic that I love talking about with other advisors, so I’m really excited to share some of my thoughts with you.
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What is a Monte Carlo Analysis and What Assumptions Does it Require?
Monte Carlo analysis is one of the most common tools used in financial planning. If you’re unfamiliar with it, it’s a model that takes assumptions about your life—spending, saving, retirement date, taxes, your portfolio mix—and then it runs thousands of market scenarios to estimate the odds that your plan holds up.
Most of those inputs I mentioned are things you can control, or at least estimate pretty well.
But market returns? And the order those returns arrive in? That’s the unknowable part. So a Monte Carlo analysis provides a framework for how various market environments could impact your financial plan.
To do this, we have to feed Monte Carlo (at a minimum) three core inputs:
- Expected return for each asset class
- Volatility, or how bumpy the ride tends to be
- Correlation, or how different asset classes tend to move together
Expected return is the long-term geometric average return you’ll earn over the time horizon for which the model is being run—geometric average is the technical term for compounded return. Volatility—as measured by standard deviation—tells the model how wide the range of outcomes should be for any single year within a simulation. And correlation tells the model how much different asset classes move together.
Collectively, those inputs are what we call capital market assumptions.
And one quick clarification: capital market assumptions aren’t predictions. They’re not a claim about what the market will do next year. They’re a set of inputs that define the range of what the market can do over time—so the plan can test itself against many possible futures.
So now we get to the heart of the episode, which is how do people choose what to assume for those key inputs of a Monte Carlo analysis, those capital market assumptions?
Common Approaches to Setting Capital Market Assumptions
In the real world, most approaches fall into one of four categories:
Approach One: Default or vendor‑supplied assumptions
Many advisors use what comes built into their planning software. It’s convenient, standardized, and easy to implement.
Approach Two: Forward‑looking building blocks
Some build assumptions from the ground up using current inputs like bond yields, equity valuations, expected inflation, and risk premia. It’s tailored, but it requires more estimates.
Approach Three: Institutional capital market assumptions
Some advisors adopt assumptions from major institutions—firms like Vanguard, J.P. Morgan, and BlackRock—who publish long‑term outlooks and model expected returns based on their frameworks. It’s like outsourcing the capital market view to a research team.
Approach Four: Historical averages
Others look backward and use historical returns, volatility, and correlations over a chosen time period—30, 50, or more years—and use that as the foundation.
This is the broad bucket that Plancorp falls into. But just like the other three approaches, there is a lot of nuance between the various ways historical averages can be utilized.
How Plancorp Sets Capital Market Assumptions
Without going through the different varieties of using historical averages as the basis for capital market returns, I’ll just focus on exactly what we do, which is anchoring our capital market assumptions to long-term real returns.
And we do that for three reasons.
First: it aligns well with the real time horizon of financial planning.
Most financial plans aren’t 5‑year plans. They’re multi‑decade plans. Even if you’re in your 60s or 70s, there’s often a spouse, a longer retirement, or a legacy goal that extends the horizon. When you look at rolling 30‑year real returns, the long-term experience tends to cluster closer to the long-term average than people assume. That’s the time scale we care about.
Second: fewer moving parts means fewer ways to be wrong.
A lot of forecasting approaches require multiple estimates—expected inflation, valuation changes, risk premia, mean reversion assumptions, and more. And the more knobs you turn, the more chances you have to introduce error.
One of my favorite advantages of using real returns is that it removes one extra forecast. Monte Carlo tools need an inflation assumption, too. If we build the framework in real terms, we reduce one more layer of guesswork.
Third: we try to be precise where precision actually exists.
When we work with clients, we can update what’s knowable: spending changes, savings changes, tax changes, earnings changes, retirement timing changes. Those inputs can be refined as life unfolds.
But trying to “fine tune” the market inputs—returns, volatility, correlations—based on what feels true about the next 10 years can create a false sense of accuracy. The whole reason we run a thousand scenarios is because we don’t know whether the next decade will be unusually good or unusually bad.
Comparing Approaches
Now, to be fair, the other approaches have strengths, which is why I love talking to other allocators about Capital Market Assumptions.
Advisors utilizing the building blocks method are often quite thoughtful and disciplined, but they’re still heavily focused on current market conditions and what happens over the next ten years or so—not the multi-decade planning horizon most people likely have.
And here’s the big issue I have with that…Let’s say valuations are high and that leads your analysis to predict the next ten years of returns to be lower. First of all, there is a chance that you’ll be wrong. But more importantly, this is why we’re using a Monte Carlo analysis in the first place. If you’re running a thousand scenarios, many of them are going to have iterations where the first ten years experience lower than average returns on their path to historically average real returns over multiple decades.
So this is why I’m not a huge fan of the building block approach or adopting the assumptions of major institutions—because in both cases you’re overweighting current market conditions in hopes of being correct for the next ten years rather than focusing on the multi-decade experience that is highly likely to be near the long-term average.
Now, next week I have the person at Vanguard who oversees their capital market assumption models, and I actually love using those return assumptions to set expectations about a range of outcomes over the next ten years, but the consistency with which real returns hover around their long-term averages over 20- and 30-year periods prevents me from going further.
Lastly, I’m going to kind of call out the vendor defaults. And I’m happy for a vendor from any of the big players in the financial planning software space come on the show to defend themselves, but I’ve dug deep in multiple conversations with them to better understand how they’re getting the numbers and the answers leave me with more questions than answers.
If you can’t be transparent and clearly explain how the assumptions are built, I don’t know how anyone could be comfortable using them as the driver of their financial plans. So if you or your advisor are using the “default settings,” I’d say that is an area of concern to address.
In Closing
Our preference is to keep the market assumptions simple, durable, and grounded in long-run evidence—then focus our energy on the parts of the plan that actually improve outcomes: savings rate, spending flexibility, taxes, portfolio discipline, and staying invested.
Because successful planning isn’t about perfectly predicting the next 10 years. It’s about building a plan that can survive many versions of the future—and sticking to it.
That brings us to the end of today’s deep dive into the assumptions behind Monte Carlo planning—and why we set ours the way we do at Plancorp.
Thanks for reading, and until next time, to long-term investing.
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Disclosure: This content, which contains security-related opinions and/or information, is provided for informational purposes only and should not be relied upon in any manner as professional advice, or an endorsement of any practices, products or services. There can be no guarantees or assurances that the views expressed here will be applicable for any particular facts or circumstances, and should not be relied upon in any manner. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment.
The commentary in this “post” (including any related blog, podcasts, videos, and social media) reflects the personal opinions, viewpoints, and analyses of the Plancorp LLC employees providing such comments, and should not be regarded the views of Plancorp LLC. or its respective affiliates or as a description of advisory services provided by Plancorp LLC or performance returns of any Plancorp LLC client.
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