A financial planning tool runs 500 Monte Carlo simulations each month using normal distributions tied to your chosen rate of return. The worst outcome shown is the bottom 10th percentile, while projected savings grows in a straight line.
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Hi. Hey, Nancy. Thank you very much for the class. I have a question about Monte Carlo. Is that appropriate now?
Absolutely.
Okay. I already have given a pessimistic and optimistic numbers. So if I do a Monte Carlo on the pessimistic number, let's say that I will change only very investment returns. And let's say that my pessimistic number is 4%. What are the limits between which that 4% is varied every year. Does that make sense?
Yes. Yes. Nancy, do you mind if I share? I'll do a little demonstration. Be my guest. All right. So I'll cover a few things. The first is how Monte Carlo works. Right. So Nancy gave a great explanation at this onset. Just to summarize, instead of applying the same rates of return year over year, we use a random normal distribution to get variance and apply it on a monthly basis. We do this a thousand times and then chart it based on interquartile ranges or the probability of them ending up in certain areas. So normal distributions follow a bell curve. Bell curves have their center point around the average. Now, whenever you use a statistical model, there are two inputs and I'm sorry for being a little deep here, but I think it's important to understand how it works. So the average is based on the rate of return that is currently selected. So if you're using pessimistic and for all of your accounts you have 4%, we'll use 4% for this new or average. It also does depend account from account. Right? So we don't just blend everything together. Each account has its special bell curve that's generated. The second input in order to make a bell curve is the standard deviation. The standard deviation is something that I actually love. How we implemented, we take that based on your rates of return and map it to a standard deviation. That basically creates a scheme where if you have a 0% rate of return, there will be no standard deviation, which means no variance. This makes sense because that would mean you're modeling a cash count. Cash doesn't change its nominal value over time. Right. So there is no variance applied. It will stay the same number throughout the plan. Let's say if a bond fund, historically Speaking, bond funds return 2, 3%. What will happen in the Monte Carlo is it'll recognize that it's a low rate of return and it will vary it by a smaller amount. You'll have a smaller standard deviation. On the other hand, if you have an account with a 7, 8, 9,% rate of return, there will be a quite sizable standard deviation. Now, to Answer your question. Is there a limit? The answer is no, since.
Right, I understand. Because it's a Gaussian. It's a standard deviation.
Exactly.
It's not a year.
Yep. So, I mean, I've checked the back end and I've run the simulation. I've seen it. It's very rare, but if you have like a 9% account, you can have a year of negative 70% returns. But it happens like, once in, like, 10,000. Right. It doesn't happen very often, but it's possible. Right. And the same is true on the other end of the spectrum. Yeah. Now, Steven did ask a great question earlier on in the chat, and that is chance of success. Is this based on Monte Carlo? Yes, chance of success is based on Monte Carlo. It doesn't look at your legacy goal. All it is is, we'll run those thousand. Or actually, in this case, it's 500 on this site. On this page, we'll run 500 iterations and whatever percentage are above $0 in liquid net worth at longevity is this percentage right here. And the other important thing to note here is the poor outcome. The poor outcome on the plant health chart, that's Monte Carlo driven, while projected savings, the top lighter green line, that's linear. Poor outcome is basically the bottom 10th percentile of results. So we run those 500 for this page. 500 iterations and the bottom 10.
Right.
Whatever that exact iteration is, we'll chart that one. So that's what that shows as a side point. If anyone is like, hey, oh, my poor outcome is greater than my projected savings. What's going on? It's probably because you have max spending on. So the projected savings, which is linear, will try to get to $0 or whatever your legacy goal is, while Monte Garlo will just still do its thing.
