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 »

This place has extreme wealth inequality, yet everybody is happy!

We are taking a short break from our Safe Withdrawal Rate Series (see the latest post here) to look into some pretty fascinating data we came across the other day. There’s a small place on earth with rampant wealth inequality. If you had just one single dollar in your name you’d be worth more than the entire bottom 27% of the wealth distribution combined. The bottom half of the population owns only about 8.6% of all wealth, while the richest 10% own 40% of all wealth, and the richest 20% own about 62% of all wealth.

Despite the wealth inequality, there is surprising harmony. There’s no call for building walls. And no call for redistributing the “ill-gotten” profits of “evil capitalists” either. There is no envy! Folks in the lowest wealth bracket would regularly compliment their richer counterparts and say “Geez, you are rich. Good for you!”

Where on earth is this place? 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.
Read More »

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.

swr-part9-chart1
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 OnlyGold.com. 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!

Read More »

The Ultimate Guide to Safe Withdrawal Rates – Part 6: A 2000-2016 case study (or: Welcome to the Potemkin Retirement Village)

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)

If you’ve been following our series on withdrawal rates (part 1 here) you have noticed that we’re quite skeptical about the 4% rule. That would be especially true for early retirees with a much longer horizon than the standard 30 years. Though, by reading through some of the research from the heavy hitters in the retirement research world, even the foundation of the 4% rule over 30 years seems to be crumbling a little bit:

  • Wade Pfau has been warning that due to high equity valuation and low bond yields the Trinity Study success rates are likely overrated. His argument is similar to ours in Part 3 of this series: we live in a low return world now and comparisons with past average returns could overstate the success probability of the 4% rule. He uses a slightly different methodology (Monte Carlo simulations) but reaches similar results.
  • Even Michael Kitces, arguably one of the great defenders of the 4% rule, has (inadvertently?) demonstrated that the 4% rule over 30 years isn’t all that sound. In the discussion after the famous “ratcheting post,” some readers (including yours truly) pointed out that we can’t replicate the success of the 4% rule with 1965/66 starting dates. Nothing to worry about, Kitces replied, all you needed to do is to use a very short-term bond (1-year T-bills) for the bond allocation, and you sail smoothly during the 1970s. Who would put 40% of the portfolio into 1-year Treasury bills (essentially CD interest rate) rather than trying to harvest the term premium of longer-term bonds? Very easy: someone with 20/20 perfect hindsight who knew that longer duration 10Y bonds will get hammered in the 70s and sink the 4% rule even over a 30-year horizon.

And I just became a little bit more skeptical about the 4% rule even over a 30-year horizon! But there is (at least) one prominent 4% SWR firewall still standing. In countless blog posts, discussions, forums etc. I have heard this quote (or variations of it):

“The 4% rule worked just fine during the Tech Bubble and Global Financial Crisis”

Let’s shine some light on that claim.Read More »

The Ultimate Guide to Safe Withdrawal Rates – Part 5: Cost-of-Living Adjustments

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)

Welcome back to the Safe Withdrawal Rate Series. Last week we wrote about how Social Security can impact the SWR estimates. Even under the most optimistic assumption (no changes to the Social Security benefits formula), we didn’t think that the 4% withdrawal rate is safe.

But how about tinkering with the inflation adjustments, also called Cost-of-Living adjustments (COLA)? I often hear that one way to save the 4% rule in periods when the stock market doesn’t cooperate is to not do inflation adjustments for a few years. Or simply utilize the fact that we all potentially spend less (in real terms) as we age! How much can we push the initial withdrawal rate in that case?

swr-part5-chart1
With a declining real withdrawal rate, we can afford higher initial withdrawals!

Read More »

Guest Post on Budgets are Sexy: My Top 7 Disagreements With Personal Finance Experts

J. Money, the personal finance blogger who runs Budgets are Sexy and RockstarFinance asked yours truly to write a guest post! Wow, what an honor! And, it turns out, this is actually my first guest post ever (not counting the “Christopher Guest Post” on the Physician on FIRE blog two months ago because that’s actually an interview). What did I write about? Initially, I proposed to go on an all-expenses-paid trip to Tahiti to review some luxury resorts and report back, uhm, some time later this year. But J$ had another brilliant idea: write about my favorite finance pet peeves. And it got published today:

My Top 7 Disagreements With Personal Finance Experts

Of course, to read it all, you’ll have to head over to Budgets are Sexy. But please see below for supplemental material, i.e., some of our blog posts on the seven topics:

  1. Safe Withdrawal Rate:
  2. Robo-Advisers:
  3. Emergency Funds:
  4. Bonds to diversify equity risk:
  5. Bond vs. Stock Risk:
  6. Cash as bear market insurance:
  7. Tax loss harvesting: