Monte Carlo analysis is a club, not a scalpel. It is an imprecise tool. That’s the good news. We don’t know what the future will hold, and we need an analytical method that recognizes that. The bad news is that it’s harder to talk about with clients. So how does Monte Carlo analysis help financial planning software account for uncertainty, and how do you talk about it with clients?
Embracing Uncertainty
All of finance is really just trying to solve some aspect of the present value formula. Finance can, to a certain degree, be summed up as:
Present Value = Future Value / (1 + Investment Return)^Time
There’s a lot of different pieces going in and out of that equation, but that is the crux of it. And what Monte Carlo analysis does is randomize the investment return. Since we don’t actually know what investment returns will look like going forward, what we really want to do is see what happens with a whole bunch of different return paths through time.
And that is what Monte Carlo analysis does for us. It allows us to run a whole bunch of simulated investment life times, and see what happens. So we had an 84% probability of success, that means that 84% of the simulations run we successful.
The Issues with Straight Line Financial Planning
This is all in contrast to straight line analysis that uses predetermined rates of return. There are a three main problems with this approach: the returns are predetermined, the analysis does not include the affects of the standard deviation of the returns, and the results are essentially binary. Each of these problems can be overcome (to a degree anyway), but the analysis still leaves a lot to be desired.
Making Predictions is Hard (Especially About the Future)
The most obvious issue with straight line analysis is that we simply do not know what returns will look like going forward. We can make guesses based on historical data, but the disclosures are true. Past performance is not indicative of future returns. Trying to pick specific future returns makes understanding a plan very difficult. There are ways to mitigate this, namely using very conservative returns, but that just means that clients either need to save more, take more risk than they actually need, or plan on spending less than they actually can. None of these are attractive options.
Financial Planning is All About Variance
Straight line analysis also ignores the effects of variance. This can have a huge impact in two different ways. The most obvious is that variance reduces total returns. As an example, let’s say you have $100 and are looking at two different two year paths. On the first path you get a 25% return each year, and on the second path you get a 50% return the first year and a 0% return the second year. Even though they have the same average return, the total returns will be different. You’ll end up with a 56% total return on the first path, but only a 50% return on the second path. You can use annualized returns to reduce this problem, but even then, it’s not a complete solution.
But that brings us to the other issue that straight line planning has with the variance of investment returns: timing. Sequence of return risk is a huge issue for financial planning. If you get a run of bad returns as a client is about to retire, that client is going to be in for a bad time. The converse is also true – even if a client is not in great shape, a good run of returns as they are entering retirement is able to make up for a lot. There’s been a lot of good work done quantifying the effects of this issue, and a good place to start is with Wade Pfau.
The thing is, straight line analysis strips all of this out. You will monotonically get the planned return every year. With Monte Carlo analysis this sequence of return risk will be included. There simply is no way around this problem.
The Answer Should Not be Yes or No
Straight line plans will tell you whether, given the assumptions used, the plan worked or not. This is their most attractive feature. Problem is, it’s also their worst feature. Financial planning is not binary. There are no right answers. Everything is about managing tradeoffs, and helping clients understand what the choices they make mean. Straight line analysis removes that from the equation – it looks at one scenario and tells you if it works. And then you can tweak that scenario until then numbers worked out. If the world worked like that, then the roboadvisors really would be able to take over financial planning.
Fortunately for all of us, the world is not deterministic. We are not able to provide “the answer” to clients. It makes talking about planning more difficult, but allows us to have better conversations with our clients about what risk truly means to them and what tradeoffs they want to make. By and large, Monte Carlo analysis makes that conversation easier to have, and also incorporates that uncertainty into the analysis. That is why inStream uses Monte Carlo analysis.
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