The end of CAPE Fear? What happens to the Shiller CAPE ratio when we roll out the weak 2008/09 earnings?

In last week’s post on dynamic withdrawal rates, one of the withdrawal rules we actually liked quite a bit was based on the Shiller CAPE ratio. One disadvantage of any such rule: The CAPE is at a high level by historical standards, 29.30 to be precise as of this morning (March 22, 2017). Today’s CAPE-based withdrawal rates will be very stingy, only around 3% per annum.

So, what to do about our CAPE Fear? One reader recently made an interesting observation: The CAPE uses ten-year rolling S&P500 earnings. So, once we roll out the low earnings from the Global Financial Crisis (GFC) in 2008/9, average earnings should move up again and the CAPE should come down. But by how much? Probably not below 20. Still, how much of a decline in the CAPE can we realistically expect: 10%? 20%? We have to start a new Excel Spreadsheet for that. Let’s get cranking!

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The Ultimate Guide to Safe Withdrawal Rates – Part 11: Six Criteria to Grade Withdrawal Rules

After a three week hiatus from our safe withdrawal rate research, welcome back to the next installment! If you liked our work so far make sure you head over to SSRN (Social Science Research Network) and download a pdf version. It’s a free 47-page (!) pdf working paper covering parts 1 through 8:

Our SSRN working paper

But let’s move on to part 11. In our previous posts (Part 9 and Part 10), we wrote about the Guyton-Klinger dynamic withdrawal rule and why we’re not great fans. Add to that our two-month-long bashing of the static 4% rule and people may wonder:

What withdrawal rule do we like?

True, we proposed a lower initial withdrawal rate (3.25-3.50% depending on future Social Security income), but that’s just the starting point. We have written here and elsewhere that this withdrawal rate is not set in stone. How do we go about adjusting the withdrawals in the future? How did different dynamic withdrawal rules perform in the past? How do we even measure how much we like a withdrawal rate rule? Today, we like to take a step back and gather a list of criteria by which we like to evaluate different (dynamic) withdrawal rules. Then simulate a bunch of withdrawal rules and assign grades.Read More »

Happy Birthday, Bull Market!

If you’re waiting for part 11 of the Safe Withdrawal Rate series, please be patient. It’s scheduled for next week and will dive deeper into variable withdrawal rate rules! For this week we have some other pressing business because tomorrow will be the eighth birthday of a very good friend of ours:

The Bull Market that started on March 9, 2009.

Almost eight years ago to the day we saw the trough of the stock market during the Global Financial Crisis when the S&P500 index closed at 676.53 and the Dow Jones Industrial at 6,547.05. The intra-day low on March 6 was even a bit lower – the very ominous 666 points in the S&P500. Everyone pretty much thought the world would end soon!

Screenshot from the on March 9, 2009: The Dow Jones hit a low (6,547.05) on March 9, 2009, and some pundits warned of a fall all the way to 4,000!

How bad was the March 2009 trough?

  • From its previous high in October 2007, the S&P 500 index fell by almost 57% and even with dividends reinvested the drop was a still staggering 55%.
  • This drop was even more severe and at a faster pace than the Dot-Com bust in the early 2000s, which was “only” a 49% drop over 2.5 years!
  • In March 2009, the S&P500 fell all the way back to its September 1996 (!) level, so it wiped out 12 years worth of equity gains.
S&P500 index 1996-2017: What a ride!

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An addition to the ERN family portfolio: Preferred Stocks

Last year in December we noticed that one of our Municipal Bond mutual funds had short-term losses. That’s not a huge surprise after the post-election bond yield surge and hence it was time to harvest those losses. If you’re not familiar with Tax Loss Harvesting, we wrote two earlier posts on the topic, one dealing with the general concept and one dealing with the implementation. In any case, after we sold the underwater tax lots, where do we put the money? For 30 days we can’t invest in the same fund (or different fund with identical benchmark) or we’d run afoul with the IRS wash-sale rule. There was one asset class that we had never owned but had definitely been on our radar screen for a while. Finally, we took the plunge and invested in… drumroll …

Preferred Stocks!Read More »

The Ultimate Guide to Safe Withdrawal Rates – Part 10: Debunking Guyton-Klinger Some More

A quick update on part 7 of this series, the Google Sheet toolkit to simulate your own safe withdrawal rate study: I added two more asset classes: Cash and Gold, with returns going all the way back to 1871. Enjoy!

Last week’s post about the Guyton-Klinger Dynamic Withdrawal Rule only scratched the surface and we ran out of time and space. So, today we like to present some additional and detailed simulation data to present at least four areas where Guyton and Klinger are quite confusing and misleading:

  1. The ambiguity between withdrawal rates and withdrawal amounts. A casual reader might overlook the fact that the withdrawal amounts may very well fall outside a guardrail range. Inexplicably, Guyton and Klinger are very stingy with providing information on withdrawal amounts over time. There aren’t any time series charts of actual withdrawals in their paper.
  2. True, Klinger shows time series charts in this paper, but they are only for the median retiree. Does anyone else see a problem with that? The good old 4% rule did splendidly for the median retiree since 1871 so I haven’t really learned anything by looking at the median. Wade Pfau showed (with a Monte-Carlo study) that the GK rule has a 10% chance of cutting withdrawals by 84% after 30 years. It’s very suspicious that the inventors of the rule don’t show more details about the distribution of withdrawals. You could call this either deception or invoke Hanlon’s Razor and blame it on sloppiness and incompetence, and both options are not very flattering.
  3. The Guyton-Klinger rule (even with a 4% initial withdrawal rate) is very susceptible to equity valuations. Results look much worse if you look at the average past retiree with an elevated CAPE ratio (20-30).
  4. Guyton-Klinger doesn’t afford you to miraculously increase your withdrawal amount without any drawback. The higher the initial withdrawal amount the higher the risk of massive spending cuts in the future.

So, let’s get cranking! We present another case study, the dreaded January 2000 retirement cohort, and also subject the Guyton-Klinger Rule to the whole ERN retirement withdrawal simulation engine to see how all the different retirement cohorts going back to 1871 would have fared. That’s over 1,700 cohorts because we insist on doing our simulations monthly, not annually.
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The Ultimate Guide to Safe Withdrawal Rates – Part 9: Are Guyton-Klinger Rules Overrated?

The number one suggestion from readers for future projects in our Safe Withdrawal Rate Series: look into dynamic withdrawal rates, especially the Guyton-Klinger (GK) withdrawal rate rules. The interest in dynamic rate rules is understandable. Setting one initial withdrawal amount and then stubbornly adjusting it for CPI inflation regardless of what the portfolio does over the next 50-60 years seems wrong (despite the extremely simple and beautiful withdrawal rate arithmetic we pointed out last week).

So, here we go, our take on the dynamic withdrawal rates. Jonathan Guyton and William Klinger proposed a dynamic strategy that starts out just like the good old static withdrawal rate strategies, namely, setting one initial withdrawal amount and adjusting it for inflation. However, once the withdrawal rate (expressed as current withdrawal rate divided by the current portfolio value) wanders off too far from the target, the investor makes adjustments. Also, notice that this works both ways: You increase your withdrawals if the portfolio appreciated by a certain amount relative to your withdrawals and you decrease your withdrawals if the portfolio is lagging behind significantly.  Think of this as guardrails on a road; you let the observed withdrawal rates wander off in either direction, for a while at least, but the guardrails prevent the withdrawal rate from wandering off too far, see chart below. It’s all pretty intuitive stuff, though, as we will see later, the devil is in the details.

Guyton-Klinger Guardrails explained: Make the usual CPI adjustments to the withdrawals as long as the proposed withdrawal rate stays within the guardrails. If the withdrawal rate crosses one the guard rails make the necessary adjustment.

The Wall Street Journal calls this methodology “A Better Way to Tap Your Retirement Savings” because it allows higher (!) withdrawal rates than the traditional 4% rule. As you probably know by now, we’re no fans of the 4% rule and if people claim that we can push the envelope even further by just applying some “magic dynamic” we are very suspicious. Specifically, we believe that the GK methodology has (at least) one flaw and we like to showcase it here.Read More »

The Ultimate Guide to Safe Withdrawal Rates – Part 8: Technical Appendix

Update: We posted the results from parts 1 through 8 as a Social Science Research Network (SSRN) working paper in pdf format:

Safe Withdrawal Rates: A Guide for Early Retirees (SSRN WP#2920322)

Last week we published a Google-Sheet that calculates safe withdrawal rates to exactly match a specified real final asset value target. For 1,700+ retirement cohorts (starting between 1871 and 2015)! How do we compute those safe withdrawal rates in practice? I hope we don’t lose half of our subscribers this week but I thought it would be a great idea to show the mathematics behind our calculations. It’s simple arithmetic that we can easily implement in Excel/GoogleSheets and Octave/Matlab. But despite the simplicity, I haven’t seen anyone else use this methodology. Everybody (Trinity Study, cFIREsim, etc.) seems to be using the brute-force simulation technique of iterating portfolio values while applying withdrawals and returns over time. That’s an inefficient approach and we developed a more elegant technique. Read More »

The Ultimate Guide to Safe Withdrawal Rates – Part 7: A Google Sheets Toolbox

Update: We posted the results from parts 1 through 8 as a Social Science Research Network (SSRN) working paper in pdf format:

Safe Withdrawal Rates: A Guide for Early Retirees (SSRN WP#2920322)

One commenter the other day had a good suggestion: Publish the Excel spreadsheet that we use in our safe withdrawal rate research. Great idea! There is only one problem: we didn’t use Excel to calculate any of the SWRs. We did use Excel to create some tables, but the computation and most charts were all done using GNU Octave, a free number-crunching programming language, similar to Matlab.

But we still liked the idea of creating a tool to run some quick SWR calculations. In Octave, we can calculate a large number of simulations and calculate safe withdrawal rates over a wide range of parameter value assumptions. Millions and millions of SWRs over many different combinations of parameter values (retirement horizons, final asset value target, equity shares, other withdrawal assumptions). That would have been cumbersome, probably even impossible to implement in Excel. But a quick snapshot on how one single set of SWR parameters would have performed over time? That’s actually quite easy to do, even though there are 1,700+ different retirement cohorts between 1871 and 2015.

Update 2/10/2016: I added the gold and cash returns.

  • Gold returns are only completely trustworthy after 1968 when I got the London Fixing time series via Quandl. Before that, I had to rely on annual data from If someone has a better (monthly) time series for 1871-1967 please let me know!
  • For cash returns I use:
    • 3-month T-bill interest rates from the Federal Reserve starting in 1934. Monthly data.
    • I have annual data for going back to 1928 from NYU-Stern. Data gathered via Quandl.
    • For 1871-1927 I use annual data on 1-year T-bill yields from Prof. Rober Shiller. It’s not exactly ideal to splice it this way but it’s the best I can right now. If someone has better data, please let me know!

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