We all know the problem with the 4% rule, is that you will likely die very rich, or at least many times the money you started with.
Not a problem if you had plenty with the 4% withdrawal, or if you love you heirs (kids, charity…) but I’m selfish enough that I’d like to get “maximum use” out of my money. I’d rather give with a warm hand if withdrawals allow.
It’s possibly even worse if you have a longer retirement and smaller SWR
Guyton and Klinger proposed to use guardrails using (what I think are) very complicated rules. Y you get to increase your withdrawals when the portfolio reaches a certain level, or reduce your withdrawals if the portfolio drops be low a certain threshold. It’s difficult to understand, feels empirical and clunky, but at least they tried to help you get more use out of your money while alive.
I’ve been very interested in this article from Michael Kitces (and Derek Tharp) that also claims the Guyton-Klinger guardrails are dangerous in addition to being opaque and clunky and that using Risk-Based guardrails is a better choice. Here’s another article where they cover risk-based guardrails.
The basic recipe from the article is:
Essentially, to implement probability-of-success-driven guardrails, advisors would need to solve for an initial target probability of success (e.g., 90%), and then decide on upper and lower guardrails that would warrant spending changes (e.g., increase spending at 99% probability of success; decrease spending at 70% probability of success).
and notes:
The process of setting guardrails with Monte Carlo analyses would involve extensive trial-and-error or ‘guess-and-check’ work, and so is a bit cumbersome. Ideally, technology should be able to automate this guardrails process for advisors so that the advantages of risk-based guardrails can be captured without bringing in the disadvantages of withdrawal-rate guardrails.
For those interested in learning more about why the risk-based strategy works may want to refer to this video from Derek Tharp and this one from Aubrey Williams.
I haven’t gone too far down the rabbit hole about how well they work, but as much as the Guyton-Klinger guardrails feel kludgy, this method feels elegant to me.
Full disclosure, I think Amortization Based Withdrawal methods (ABW, VPW, TPAW…) are even better at maximizing your use of money if you can stomach the larger swings (if a large part of your income is discretionary)
I’ve not used Income Lab but it’s supposed to automatically calculate risk-based guardrails for you, possibly using Monte Carlo analysis for the risk evaluation (I don’t know)
Aubrey Williams has an interesting video and associated spreadsheet in which he uses firecalc for the technology assist of this trial-and-error.
This essentially replaces the Monte -Carlo mentioned in Kitces’ article with using historical data for risk evaluation. I don’t know if the idea is his originally or not, but i like it!
I don’t quite as much like the idea of plugging things multiple times in firecalc, I’m sure as he mentions you get used to it and can do it reliable in 6 minutes (I’ve gotten somewhat good at it) but it can still be prone to dumb errors.
So because I (don’t) need another project, I decided to make my own self-contained calculator (which will be another sheet on my “Big Spreadsheet of Everything”)
This is early stages, probably not quite “most excellent” yet, in particular it doesn’t account for any future income (SS, pensions) or spending, just pure portfolio withdrawals.
But I’d love to get feedback, advice or even help, in developing a tool that many may find useful and hopefully give back to this community.
I recommend watching Aubrey’s video on how to used firecalc to better understand what this calculator does
I’ve never shared files like this before so hopefully you can access from this link: (I will keep updating it to the latest version)
https://drive.google.com/file/d/1ENp-BO … sp=sharing
you will need to enable macros, the search for the portfolio/withdrawal pair that matches a specific risk target uses macros.
