Monte Carlo Simulation
Here are some key takeaways:
1. Monte Carlo simulation explained: It’s a retirement planning tool that runs thousands of scenarios using inputs like withdrawal needs, investment returns, and inflation rates to assess retirement viability and provide a probability of success.
2. Aiming for 100% success isn’t always necessary: A success rate in the 80-90% range may be acceptable, allowing for more spending in the present or early retirement; while still maintaining a high likelihood of not running out of money.
3. Interpretation is crucial: Monte Carlo simulations shouldn’t be viewed as pass/fail tests but rather as a compass or sliding scale. The results require proper interpretation, ideally by a financial planner who understands the tool’s assumptions and limitations.
4. Customization and flexibility are important: Different inflation rates should be applied to various expenses (e.g., healthcare costs inflating faster than general inflation). The ability to adjust inputs and see how changes affect the outcome is valuable for planning.
5. Results should inform action: If a simulation shows a low probability of success, it doesn’t mean failure is inevitable but indicates that changes may be necessary. These changes typically involve adjusting spending rather than manipulating other inputs to artificially improve results.
Monte Carlo Simulation – Links
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JP Morgan Guide to Retirement – download
Monte Carlo Simulation – Transcript
Case, can we talk about what a Monte Carlo simulation is?
I mean, this is like a “major jargon alert” for planners and investment advisors!
We throw it around sometimes with clients – and we shouldn’t.
But can we break this down into simple terms what a Monte Carlo simulation actually is?
Yeah, I was hoping on this video, I wouldn’t have to talk for 3 or 4 minutes in the, in the very beginning to set you guys up for an answer, but that’s all right.
So Monte Carlo simulation is a is a retirement planning tool that a lot of financial advisors and financial planners will use to assess retirement viability.
Basically, what it does is it runs a 1000 different scenarios. Using inputs like uh withdrawal needs from a retirement plan, anticipated investment returns.
Anticipated inflation rates, it kind of bakes everything in in there and then runs, like I said, a lot of different scenarios, and it spits out a probability or some people use a “thumbs up” or a “green light” versus a red light or yellow light.
Or, every firm kind of has their different spin on, on how they present to the clients, but basically it’s if you have a 100% success rate, then your plan looks good in all of these different simulations that the Monte Carlo ran.
So that means that if everything goes according to plan, you will not run out of money in retirement.
So, so should we be, should we be aiming for 100% every time?
Well, I think that not necessarily. I think my takeaway is, of course, it depends on the type of person.
I think if we’re applying. Different, if we go back to grade school, we can think about if you study really hard and you put in all the work, you’re going to get 100%.
But if you do just enough, remember “C’s get degrees!”
I know that we don’t want to encourage that type of behavior. But if you get somewhere in, like the 80 to 85% range, I think that that might be OK, because 100% means you’re definitely not going to run out of money in retirement – according to the inputs that you’re using.
And if you get somewhere, if you have a little bit of slack, maybe high 80s, low 90s, that means that maybe you spend some more money today, you spend some more money upfront in retirement, doing some things that you, you always dreamed of, or you always, always wanted to do, so I don’t necessarily think that everyone should be aiming for 100%.
But I want to get Brendan’s take on this.
Yeah, I mean, I think at a high level like Monte Carlo is just “introducing chaos” to the plan.
In the sense of it takes us from a “two dimensional world” where we’re assuming straight line, straight line returns on investments on things like inflation, and it introduces variance in the sense of like if you’re going to average a 6% rate of return on your investments over the lifetime of retirement plan and you’re going to take out a certain amount each year and inflation is going to increase what you need each year by a certain amount.
It’s great and you can do that in a straight line and probably assess just based on what the percentage need from the portfolio it’s whether it feels, you know, good or not. You need some experience to determine that.
But to take it a step further is sure, we’re going to average 6%, but we’re going to have returns that are all over the map – because we’re investing in the stock market or the stock and the bond market in some capacity. So you can start to see what happens if the order of the returns isn’t necessarily a straight line.
And how that can impact the plan when you’re pulling money from it in particular.
So, when you do that, and you get a score, what it’s telling you is the percentage of times out of the 1000 trials or 10,000 trials, like whatever the whatever the sample size is that you didn’t run out of money.
And so as Case said, if it’s 100%, you are probably leaving some money on the table. And if you want to leave like a ton of money to the next generation, then maybe that’s your MO.
And that’s cool.
But you could run it at different levels depending on how risky you want to be, like, if running out.
“Running out” means different things to different people.
You could run a plan that’s fifty-fifty. And if you have a lot of pension and Social Security income that’s kind of outside of the plan — and that’s going to cover all of your fixed expenses — or close to it. Then maybe you want to “run it hot” and see how much more you can pull from your portfolio.
Because the worst case is, “uh, I’m fine. I’ve got that other income.”
Kind of like the “Tommy Boy” quote, “we still got that meat lover’s pizza in the trunk,” no big deal, I think.
Yeah, so like it, it depends.
I think these tools are all over the place, and they’re all a little bit different and they’re “kind of black boxy;”
So I think you have to use them responsibly.
And I think that — unsurprisingly — I think having a financial planner who knows what’s baked into it; and can interpret the results; and communicate them is probably more helpful than just like punching numbers in on a website and getting a percentage that doesn’t mean thing.
Or a “green light” or something like —
–it says, “honey, it says we’re good, so we’re out, we’re going to retire.”
I mean, it’s better than not having any information! But I think you can probably gain like some “false sense of certainty” by plugging your stuff into a tool that says “go for it.”
When it’s going to be “garbage in / garbage out” like anything that uses data. You’ve got to be sure about what’s going in. And then what’s being assumed — under the hood — to know how much you can trust the results.
And then even the results are open for interpretation, I think.
Yeah, to go back to my grade school analogy, I don’t think they should ever be viewed as like a pass / fail.
It’s kind of like a sliding scale, or maybe, even graded on a curve, I don’t know, um.
It’s more like a compass though. It’s pointing you in a direction.
It’s not telling you pass or fail. But if we put it into a “percentage” term, then everybody wants to get an “A”
Because that’s our default is like, “sure, I’d probably want it to be a higher percentage” or 100% if I could.
Because that’s how everything else is scored.
But that’s not what this is.
Yeah, I think, along the lines of a pass / fail …I think one of the ways that a Monte Carlo score can be interpreted is if it does spit out a “plan failure” that I know — and we’ve talked about it before, how a “plan failure” doesn’t necessarily mean that you’re doomed.
It just means that you got to change something.
Maybe you have to cut some expenses back.
Or work another year and that is going to change the score.
And I think Bren, like you said, understanding the inputs and the outputs is your best bet.
So then you can understand the different levers that can be pulled.
And I want to share some numbers.
This is from JP Morgan’s “Guide To Retirement.”
I’m going to talk about average healthcare costs in retirement and the different inflation rates.
So historical inflation rates are just over 2%. But we want to inflate healthcare costs at least 5% per year.
So from JP Morgan’s info, the average Medicare cost for someone turning 65 in 2024 is $542 a month, or about $6500 per year.
By that time, by the time the person is 95, the average Medicare cost is going to be just under $1500 a month or about $17,800 per year.
So it’s going to triple in about 30 years.
Yes. And I think healthcare costs, like I said, are inflating at a different rate than overall inflation.
So you have to be able to break out healthcare costs from base expenses and, and use those two pieces of information differently.
So yeah, just, just speaks to being able to customize the plan and being able to, to pull the different levers inside of, inside of that plan.
And you can, ummm.. We have all messed around with these Monte Carlo calculators — where it’s like, “well, let’s add another year of work,” or “let’s come up with a different mix of how our assets are going to be invested” and you can manipulate the numbers to really — any — outcome that you want.
I think the important things are if you do wind up in a situation that’s trending towards less than a 50% positive outcome… you need to be aware.
It doesn’t mean that that’s going to happen! But you need to be aware of how things look.
I think it’s probably important context too, that’s going to be case-by-case to add to that conversation if you’re running a plan with those sort of margins.
How quickly a change would be needed.
And a change, and like “define change.”
Change isn’t going to mean “we change the investments around.”
The change is going to be cutting spending.
That IS the change — if you want to course correct. If you’ve run at 50%. And results that you get in real time, have you headed down one of the other 50% where the plan depletes, um.
Like that’s important. And I think it’s important to realize like, “how are you going to recognize it?” in the moment?
And how soon are you going to be able to make changes?
And how “cut-able” are you know, 10% of your expenses?
Like, how quickly could you cut those?
And, are you willing to do that?
And I mean, those are the things that you NEED to understand that aren’t just in the percentage outcome — or whatever sliding scale, the results are there, like that’s, I think that’s the color that you need to have to responsibly use the feedback that you get from some of these tools.
Certainly helpful.
You just got to know what their, what part they play in the, in the whole picture.
Yeah, Bren, I think the compass analogy is great. It points in the right direction and you’re, you’ll probably want to run multiple different iterations of the plan changing and tweaking inputs to see, to play with the inputs at different expense levels, to see like that’s probably the one that you have the most control over.
So like when your plan doesn’t work — I don’t think it’s smart to go into the Monte Carlo tool and just “beef up the returns” until it does, or “lower your life expectancy” until it works, or like “lower inflation” or “taxes” or any of the other assumptions that are going into there.
The thing you should probably play around with is the income thresholds.
And then do some reflection on whether that’s realistic or not.
Like you can make the retirement plan work — if you just say you’re going to spend less.
But then if you don’t ACTUALLY spend less, you’re headed down a different track.
So, like you have to DO what you tell the machine you’re going to say you do.
It’s just giving you some extra color by adding variances to the returns is really all the Monte Carlos was adding above and beyond. Taking retirement income planning inputs.
And building assumptions on them: you still have to be responsible with it – I think is maybe the important point.
Yeah, I think it’s, it’s important for people to not just try and get 100%.
Or not to manipulate the data, just to get a good score.
You know, you should input, you want the real, you want the real stuff, you want the real information coming back to you.
So you need to put the real information going in.
And there’s no, there’s no judgment. It’s like it’s not like the kids who get D’s in class are going to get scolded by the teacher and have to stay after and do extra credit.
You need to know, you need to know where you’re going. It is why you’re working with a financial planner who’s using these tools in the first place — to get that feedback. So you can take control of your life and take control of your expenses and make the necessary adjustments to get yourself back on track.
So, I guess the big takeaway is don’t get lured into a false sense of security by these tools!
And use the tools
Garbage in / garbage out!
Yes, garbage in, garbage out and use the tools as a way to get feedback — to take stock of your life and your retirement.
