Inside the Engine: The Assumptions Behind Your Monte Carlo Retirement Plan (EP.240)

0:28We all need to make smart decisions with our money. modern financial plan, and that's the assumptions inside a Monte Carlo analysis. Now, if you've ever seen a plan that says you have, I don't know, an 80 % or a 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 that the software runs. And 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.

1:05And third, how we do it at PlanCorp and why we prefer our approach for long-term investors. This is a topic I love talking about with other advisors, so I'm really excited to share some of my thoughts with you. And a quick note, if you want to see what I'm reading and have access to some exclusive content that isn't available on my podcast or my website, You can sign up for my newsletter. There is a link right at the top of the episode description. All right, let's get into today's episode. What is a Monte Carlo analysis and what assumptions does it require? Well, a Monte Carlo analysis is one of the most common tools used in financial planning.

1:41And if you're unfamiliar with it, it's basically just a model that takes assumptions about your life, whether that's spending, savings, retirement date, taxes, your portfolio mix, and then it runs thousands of market scenarios to estimate the odds that your plan holds up. Now, most of those inputs that I mentioned are things you can control or at least estimate pretty well, but the market returns and the order of those returns in which they show up, 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 the Monte Carlo at a minimum three core inputs, and that's expected return, volatility, and correlation.

2:26Expected return is the long-term geometric average you'll earn over the time horizon for which the model is being run, and the geometric average is just the technical term for compounded return. Volatility, which is measured by standard deviation, tells the model how wide the range of outcomes should be for any single year within a simulation, and then correlation tells the model how much different asset classes move together. And then collectively, those inputs are what we call capital market assumptions. Now, one quick clarification, capital market assumptions are not predictions. They're not a claim about what the market will do next year.

3:06They're really just a set of inputs that define the range of what the market could do over time so that the plan can test itself against infinite possible features. 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. Now, in the real world, I think most approaches fall into one of four categories. One approach is just the default or vendor supplied assumptions. So many advisors will just use what comes built into their planning software because it's convenient and feels standardized and easy to implement.

3:44A second approach requires a little bit more work, and it's what I would characterize as forward-looking building blocks. Some will build these assumptions from the ground up using current inputs like bond yields, equity valuations, expected inflation, and risk premia. And so people like this because it's tailored, but it does require more estimates. The third approach is to implement institutional capital market assumptions. So some advisors will adopt the assumptions from major institutions like Vanguard, JP Morgan, and BlackRock, et cetera, who publish long-term outlooks and model expected returns based on their own models.

4:20So it's sort of like outsourcing the capital market view to a research team. And actually next week, I'm interviewing the individual at Vanguard who is responsible for developing these models. So you might want to tune into that. It's a pretty wonky conversation. However, it really gives you a sense of how much thought goes into setting those capital market assumptions. Now, the fourth approach is simply historical averages. People will look backward and use historical returns, volatility, and correlations over some chosen time period and use that as the foundation. This is the broad bucket that Plancourt falls into, but just like the other three approaches that I've outlined, there is a lot of nuance between the various ways that historical averages can be utilized.

5:03And I don't want to go into every single variety of using historical averages, but I will focus exactly on what PlanCorp does, which is anchoring our capital market assumptions to long-term real returns. And I think I can explain why we do that in just three simple reasons. The first is that it aligns very well with the real time horizon of financial planning. I mean, think about it. Most financial plans aren't five years or 10 years. They're multi-decade plans. Even if you're in your 60s or 70s, there's often a spouse, a longer retirement, a legacy goal that extends the horizon past your own lifetime.

5:41And so when you are looking at rolling 30-year real returns, the long-term experience tends to cluster very close to that long-term average, probably more so than people realize. And that's the timescale we care about. The second reason that we really like this approach is that there are fewer moving parts, which means fewer ways to be wrong. A lot of the forecasting approaches that I've outlined 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. And one of my favorite advantages of using real returns rather than nominal returns is that the real returns remove one extra forecast.

6:26Because Monte Carlo tools need an inflation assumption too. So if we build the framework in real terms, we reduce one more layer of guesswork. Now, the third core reason that we use this approach is that we try to be precise where precision actually exists. When we work with clients, we can update what's knowable. Spending changes, savings changes, taxes changes, earnings changes, retirement changes, those inputs can be refined as life unfolds. But trying to fine-tune the market inputs, those returns, those volatilities, those correlations, based on what feels true about the next 10 years, I feel like creates a false sense of accuracy.

7:09The whole reason that we run thousands of scenarios through a Monte Carlo analysis is because we don't know whether the next decade will be unusually good or unusually bad. Now, to be fair, all of the approaches have their strengths. This is part of why I love talking with other allocators about capital market assumptions. It's one of the first things I'll bring up with somebody when I first meet them. Now, I tend to find that the advisors utilizing the building blocks method are often very thoughtful and disciplined. But the reason I don't trend that way is I still feel like it's heavily focused on current marketing conditions and what happens over the next 10 or so years, not the multi-decade planning horizon most people will likely have.

7:50And here's the big issue I have with that. Let's say that valuations are high and that leads your analysis to predict that the next 10 years of returns are going to be lower. So first of all, there is a chance that you'll be wrong. But more importantly, this is why we're using the 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 10 years experience lower than average returns on their path to the 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 some of the major institutions, because in both cases, you're overweighting the current market conditions in hopes of being correct for the next 10 years, rather than focusing on the multi-decade experience that is likely to be near the long-term average.

8:39Now, as I already mentioned, 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 10 years. But again, I'm kind of a broken record here. The consistency with which real returns hover around their long-term averages over 20 or 30-year periods really prevents me from going further. But I also want to call out the last bucket. I haven't touched on it since introducing it, and that's the vendor defaults. And I'm going to call them out a little bit.

9:13I am happy for a vendor from any of the big players in the financial planning software space to come on the show and defend themselves. But I have dug in deep via multiple conversations with them to better understand how they're getting to their numbers. And I got to say, their answers leave me with more questions than answers. So 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 plan. So if you or your advisor are using the default settings, I would say that this is an area of concern to address.

9:47So in closing, our preference is to keep market assumptions simple, durable, and grounded in long run evidence. Then we can focus our energy on the parts of the plan that actually improve outcomes. The savings rate, the spending flexibility, the taxes, the portfolio discipline, this 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 have a plan that you can stick with. That brings us to the end of today's deep dive on the assumptions behind the Monte Carlo planning and why we set ours the way we do at PlanCorp.

10:26Don't forget, if you want exclusive content and links to the things I'm reading and publishing, sign up for my newsletter. The link is right at the top of the episode description. It'll help you stay focused on what matters and filter out the noise. As always, thanks for listening. And until next time, to long-term investing. Thanks for listening to the Long-Term Investor podcast. To access free financial resources and submit questions to be answered on the show, visit thelongterminvestor.com. Peter Lazaroff is an employee of PlanCorp and BrightPlan. All opinions expressed by Peter and any podcast guests are solely their own opinions and do not reflect the opinions of PlanCorp or BrightPlan.

11:10This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of PlanCorp and BrightPlan may maintain positions in the securities discussed in this podcast.