*Update (August 2, 2018): The ERN blog was nominated for the 10th Annual Plutus Awards in two categories: Best Financial Independence/Early Retirement Blog and Best Series: Blog, Podcast, or Video (specifically, the Safe Withdrawal Rate Series). Thanks to the growing community of all the amazing readers here who nominated my small blog!*

Welcome! You probably landed on this page because you clicked someone’s link to my **Safe Withdrawal Rate Series**. Thanks for stopping by! This series has now grown to 30+ parts and if you are looking for a **less technical** summary before jumping into the nitty-gritty details, I recommend you check out the new “landing page” to my series first:

**The Safe Withdrawal Rate Series – A Guide for First-Time Readers**

But if you got an appetite for the technical details after that I suggest you check out all the other parts as well! Also, I 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)**

**But without further ado, here’s Part 1 of the Series:**

We just calculated over 6.5 million safe withdrawal rates. Well, not by hand, of course, but by writing a computer program that loops over all possible combinations of retirement dates, and other model parameters. Not a big surprise here, but it took a lot of work to put this together. We can’t possibly fit all results into one single post, so we publish our results in multiple parts. Today, we briefly introduce our research and some baseline results. Stay tuned for more to come in the next few weeks/months:

- Part 2: Some more research on
**capital preservation vs. capital depletion** - Part 3: Safe withdrawal rates in different
**equity valuation**regimes - Part 4: The impact of
**Social Security benefits** - Part 5: Changing the
**Cost-of-Living Adjustment**(COLA) assumptions - Part 6: A case study: 2000-2016
- Part 7: A
**DIY withdrawal rate toolbox**(via Google Sheets) - Part 8: A
**Technical Appendix** - Part 9:
**Dynamic**withdrawal rates (Guyton-Klinger) - Part 10: Debunking Guyton-Klinger some more
- Part 11: Six criteria to grade
**dynamic withdrawal rules** - Part 12: Six reasons to be suspicious about the “
**Cash Cushion**“ - Part 13: Dynamic Stock-Bond Allocation through
**Prime Harvesting** - Part 14:
**Sequence of Return Risk** - Part 15: More Thoughts on
**Sequence of Return Risk** - Part 16: Early Retirement in a
**low return environment**(The Bogle scenario!) - Part 17: Why we should call the 4% Rule the
**“4% Rule of Thumb”** - Part 18:
**Flexibility**and the Mechanics of**CAPE-Based Rules** - Part 19:
**Equity Glidepaths**in Retirement - Part 20: More thoughts on
**Equity Glidepaths** - Part 21:
**Mortgages**and Early Retirement don’t mix! - Part 22: Can the
**“Simple Math”**make retirement more difficult? - Part 23:
**Flexibility**and**Side Hustles!** - Part 24:
**Flexibility Myths**vs. Reality - Part 25: More
**Flexibility Myths** - Part 26: Ten things the “Makers” of the 4% Rule don’t want you to know
- Part 27: Why is
**Retirement Harder**than Saving for Retirement? - Part 28: An
**updated Google Sheet**DIY Withdrawal Rate Toolbox - Part 29: The
**Yield Illusion:**How Can a High-Dividend Portfolio Exacerbate Sequence Risk? - The Yield Illusion Follow-Up (SWR Series Part 30)
- The Yield Illusion (or Delusion?): Another Follow-Up! (SWR Series Part 31)

The plan to work on this research came after one of those moments when we realized that if you want something done right and exactly applicable to our own situation, we just have to do it ourselves. We wanted to do a lot more robustness analysis than we had seen anywhere in the blogging world.

### Nonconformist among the nonconformists

Intriguingly, very few early retirement planners or bloggers question the validity of the 4% safe withdrawal rate rule. When you retire in your 30s or even 40s you are by nature nonconformist. You question the consensus, the people with the McMansions and the full-size SUVs in the driveway. People who are otherwise extremely suspicious about everything consensus suddenly eat up the 4% rule without much questioning or checking under the hood:

- People take the Trinity Study at face value and extrapolate the 30-year windows from Trinity to 50+ years for the early retirement crowd (bad, bad, bad idea, see Part 2 of our series!!!),
- It’s probably not a good idea to use a withdrawal rate calibrated to the average retiree since 1926 when today’s equity valuation is much less attractive than the average since 1926, see Part 3 of this series.
- Social Security will also save your behind come age 67 (uuhhhm, good luck with that, see Part 4 of this series!).
- Folks wave their hands about how one can just slow consumption growth (it’s not that easy, see Part 5 of this series!),
- and wave their hands about how the 4% rule did just fine in 2001 and 2008 (believe me, it didn’t; see Part 6 of this series!)

Has anybody actually done some *serious* simulations that are truly applicable to the FIRE community? Something comparable to the original Trinity Study, but with more bells-and-whistles and robustness checks applicable to the FIRE community? I don’t like the “hand-me-down” research targeted at my parents’ retirement. So, when you want something done, and done right, you gotta do it yourself! Which is what we did with the 6.5 million safe withdrawal rates.

### What we do to be more relevant for early retirees

- The study is done at a monthly frequency (not just annual like cFIREsim), starting with equity and bond returns in January 1871 and going through September 2016. It would be unrealistic for us to withdraw funds only once per year at the beginning of the year and have – on average – 6 months of cash sitting around in our checking account.
- We look at the sustainable withdrawal rates over 30, 40, 50, and 60-year windows. It’s still a good idea to keep the 30-year window for comparison, though this window length is simply too short for us in the early retirement community.
- We look at different target final values, i.e., calibrate maximum withdrawal rates to deplete the capital (final value=0), preserve the inflation-adjusted initial capital (final value=100% of initial value) and some steps in between (final value=25%, 50%, 75% of inflation-adjusted initial value). This is useful for retirees who are uncomfortable with the idea of running out of money at some future date and/or plan to leave a bequest to their children, grandchildren, and charitable organizations.
- We extrapolate past the current history and append equity and bond returns after September 2016. To this end, we assume long-term average returns for equities going forward (about 6.6% real p.a.). For bonds, we assume a low real return over the first 10 years: only 0% real p.a., which is actually slightly above the 9/30/2016 10Y yield (1.61%) minus the inflation expectation at the time (~2%). After the initial 10 years, bonds too will return their long-term average of 2.6% real per year. We should note that these return assumptions are likely going to generate higher sustainable withdrawal rates due to the absence of return volatility.
- We study how different the safe withdrawal rates and success probabilities were in various equity valuation regimes. Specifically, how do safe withdrawal rates and success probabilities look like for different Shiller CAPE ratio regimes? We did a similar study before using cFIREsim.com, but now we can rely on our own monthly simulations and easily loop over all sorts of other model parameter values.
- We can study the impact of reducing the monthly withdrawals over time. This mimics the assumption that some people consume less as they age. Or we can take into account the impact of lower withdrawals once retirees start collecting Social Security.
- We study how alternative withdrawal strategies, e.g., dynamic withdrawal rules rates based on equity valuation (Shiller CAPE) would have performed during this time.

### Methodology in detail

We use monthly total return data (including dividends/interest) for the S&P500 and 10-year Treasury Bonds from January 1871 to September 2016. We realize that some other researchers use slightly higher yielding corporate bonds. Notice, though, that this higher yield comes at the price of higher correlation with equities and thus less diversification. Our analysis yielded that the exposure in the LQD ETF (iShares investment-grade corporate bonds) has roughly the exposure of 75% government bonds (IEF = 7-10-year US Treasuries) and 25% US equities (VTI = Vanguard US Total Equity Market ETF). So, a 60% equities 40% corporate bond portfolio has about the same return characteristics as a 70% equities, 30% government bond portfolio if you like to translate our portfolio weights into a Stock vs. Corporate Bond portfolio. The Barclays Agg (iShares ticker AGG) is somewhere in between.

Monthly returns and monthly CPI inflation are translated into monthly real returns. We assume that the retiree has withdrawn an initial amount equal to one-twelfth of the targeted withdrawal rate at the market closing price of the previous month. The remainder of the portfolio grows at the real market return during the current month. At the end of the month the retiree withdrawals the next monthly installment and rebalances the portfolio weights to the target equity and bond shares. We assume that the portfolio is subject to a 0.05% drag from fees for low-cost mutual funds.

### Why 6.5 Million Safe Withdrawal rates?

We calculate safe withdrawal rates for **all possible combinations** of 1) starting dates, 2) retirement horizons, 3) equity weights, 4) final asset values and 5) withdrawal patterns:

- 1739 possible retirement start dates between February 1, 1871, and December 1, 2016.
- 4 different retirement horizons: 30, 40, 50, and 60 years
- 21 different equity weights from 0% to 100% in 5% steps (bond weight = 100%-equity weight)
- 5 different final asset value targets: 0%, 25%, 50%, 75% and 100% of real inflation adjusted initial asset value
- 9 different withdrawal patterns. The baseline assumes that withdrawals are adjusted in line with CPI inflation, but we also allow for slower than CPI-growth. We also check how lower withdrawal rates 20 or 30 years after the retirement start date (to account for Social Security income) will impact the maximum sustainable withdrawal rates.

Hence, we calculate 1739 x 4 x 21 x 5 x 9 = 6,573,420 different safe withdrawal rates.

### Base Case Results

Here’s a table, roughly the same structure as they use in the Trinity Study. Major changes:

- we use retirement lengths of 30-60 years and
- withdrawal rates only between 3% and 5% in 25 basis point step. No serious long-term retirement planner with a horizon of 50-60 years would ever even consider a withdrawal rate above 5%, anyway, given that equities return “only” about 6.6% and you have to account for volatility and sequence of return risk.

The success criterion is a final asset value of zero as in the Trinity Study.

A few conclusions from this table:

- The success rates for a 30-year horizon are roughly consistent with the Trinity study.
- Success probabilities stay very high at all horizons when using 75-100% equity shares and withdrawal rates of 3.5% and under.
- Success probabilities deteriorate quite a bit when the retirement horizon goes from 30 to 60 years.
- It may be true that for a 30-year horizon, an equity share of 50-100% gives consistently high success rates if the withdrawal rate is 4% or lower. Essentially the main result of the Trinity Study! But for longer horizons, 100% stocks gives the highest success rate. This goes back to our earlier research that showed that over long horizons bonds can have extended drought periods and only equity-like returns are a guarantee for not running out of money over long horizons. For example, a 4% withdrawal rate has a 95% success probability in a 50%/50% over 30 years, but only 65% over 60 years. The failure probability is 7 times higher over the 60-year horizon!
- A 5% withdrawal rate would have an unacceptably low success rate even after 30 years, and certainly after 60 years. As stated above, no early retiree should get anywhere close to a 5% withdrawal rate.

Another way to look at the data: Plot a time series chart of different safe withdrawal rates over time both for 30-year and 60-year horizons. In the chart below I use an 80% equity weight and 20% bond weight, pretty common among bloggers. Unsurprisingly, the 60-year withdrawal rates are significantly below the 30-year rates. There are only a few occasions where the 30-year SWR drops below 4%, but a 60-year retirement horizon has a few stubbornly long episodes with 3.5-4% withdrawal rates. So, 3.5% is the new 4%! What’s worse, in future posts, we will show that you’d likely have to reduce the 3.5% even further to account for a) today’s high CAPE ratio and b) a higher final asset target!!!

Another way to slice the data; same chart but as a scatter plot instead of time series chart, see below. The 30-year safe withdrawal rate is on the x-axis and 60-year withdrawal rate is on the y-axis. The dots are all under the 45-degree line, no surprise here! On average, the 60-year SWR are more than a full percentage point below the 30-year SWR (below the 45-degree line), but in the region where it really matters, when the SWRs are low, the difference is “only” about 0.5%.

So much for the sneak preview today. We hope you enjoyed this research so far. More topics coming over the next few weeks/months:

- Part 1:
**Introduction** - Part 2: Some more research on
**capital preservation vs. capital depletion** - Part 3: Safe withdrawal rates in different
**equity valuation**regimes - Part 4: The impact of
**Social Security benefits** - Part 5: Changing the
**Cost-of-Living Adjustment**(COLA) assumptions - Part 6: A case study: 2000-2016
- Part 7: A
**DIY withdrawal rate toolbox**(via Google Sheets) - Part 8: A
**Technical Appendix** - Part 9:
**Dynamic**withdrawal rates (Guyton-Klinger) - Part 10: Debunking Guyton-Klinger some more
- Part 11: Six criteria to grade
**dynamic withdrawal rules** - Part 12: Six reasons to be suspicious about the “
**Cash Cushion**“ - Part 13: Dynamic Stock-Bond Allocation through
**Prime Harvesting** - Part 14:
**Sequence of Return Risk** - Part 15: More Thoughts on
**Sequence of Return Risk** - Part 16: Early Retirement in a
**low return environment**(The Bogle scenario!) - Part 17: Why we should call the 4% Rule the
**“4% Rule of Thumb”** - Part 18:
**Flexibility**and the Mechanics of**CAPE-Based Rules** - Part 19:
**Equity Glidepaths**in Retirement - Part 20: More thoughts on
**Equity Glidepaths** - Part 21:
**Mortgages**and Early Retirement don’t mix! - Part 22: Can the
**“Simple Math”**make retirement more difficult? - Part 23:
**Flexibility**and**Side Hustles!** - Part 24:
**Flexibility Myths**vs. Reality - Part 25: More
**Flexibility Myths** - Part 26: Ten things the “Makers” of the 4% Rule don’t want you to know
- Part 27: Why is
**Retirement Harder**than Saving for Retirement? - Part 28: An
**updated Google Sheet**DIY Withdrawal Rate Toolbox - Part 29: The
**Yield Illusion:**How Can a High-Dividend Portfolio Exacerbate Sequence Risk? - The Yield Illusion Follow-Up (SWR Series Part 30)
- The Yield Illusion (or Delusion?): Another Follow-Up! (SWR Series Part 31)

### Thanks for stopping by. Please leave your comments, questions and suggestions below!

This is an outstanding study. In this part 1 you write that you calculated success rates of equity weights by 5% increments from 0% to 100%. Have you posted all of those similar to you table above with 100/75/70/25/0? I’d be very interested to see the whole range.

Thanks! You can just linearly interpolate between the 25% steps. In other words, for 35% equity weight you take 0.6x the 25%figure and 0.4x the 50% figure.

It would take too much space to post the entire table. 🙂

Perfect, thanks so much. Do you have the data tables for various CAPE regimes? I’d love to create the same chart based on CAPE > 30 as current. Or is there some type of interpolation that I could make? Great great work!!

I’d be cautious about the CAPE>30 cutoff. You’d capture exactly the 1929 peak and the dot-com-bubble. That might not be representative of today’s situation.We’re still rolling out the GFC low earnings from 2008/9.

But if you want to create your own tables, check out SWR Part 7, and the Google Sheet. There are the failure probabilities for CAPE>30 in the table on the right.

One issue with the 4% rule, and variants that use a different percentage, is that the retirement payout is specified as a percentage of the value of the portfolio as of the retirement date. Making retirement payouts so dependent on the market valuation of your portfolio on one specific date raises a couple of issues:

First, what happens if your retirement date corresponds to a market top? Your withdrawal percentage needs to be small enough to cope with this scenario.

Second, what happens if the market plunges a few days before you retire? If you stick to your chosen withdrawal rate, your retirement income is less than you expected.

You consider variable withdrawal rates, which is one way to address these issues, but I was wondering if it was possible to address them more directly. The best idea I can come up with to address these issues is as follows. Take your portfolio as of your retirement date, and using market data, produce monthly valuations for the past year. (The reason for computing this as opposed to using actual portfolio valuations is that you’ve presumably been adding money over the course of the year.) This gives you 13 valuations. Compute your distribution as a percentage of this median, rather than as a percentage of the final value.

Using a median addresses the first issue by avoiding using a valuation that is a market top. (There are going to be six dates nearby where the valuation is higher.) It’s not immediately obvious how much this will help, because market cycles tend to last longer than a year, and it’s not immediately obvious how to test this because even if this scheme results in a higher success rate it’s not necessarily an improvement if it (on average) lowers the payout. I’m hoping you will be inspired to figure this out.

Using the median means that the six months from a proposed retirement, you know the market index values for seven of the twelve dates that could end up being chosen as the median, so you can compute a range of possible payout values. As you get closer to the proposed retirement date, the number of possible dates, and thus the range of payout values, will get smaller. So I think this approach does a good job of addressing the second issue, since you should normally be assured several months in advance of your proposed retirement date that your retirement payouts will be acceptable even if the stock market collapses just before you retire.

I haven’t tried that rule. How would you propose using the rule after a few years into retirement? Update according to that rule or still use the withdrawals from the initial calculation (adjusted by CPI)? I would be afraid that even the median is still too close to the market top.

Very interesting comments about the challenge of using a certain SWR at a fixed point in time. As a member of the financial community, this is a frequently at the top of my mind and I don’t think is well addressed/though-out within the community….other than trying to “time the market” by reducing/managing risk excessively a few years out from the retirement age – – a bad solution IMO. Would love to hear Big Ern’s thoughts on how to address this challenge….

There is timing the market in the stock vs. bond vs. cash allocation (=a hard task) vs. timing the withdrawal rate (=not that hard). I think a CAPE-based withdrawal rule (see Part 18) is a pretty reasonable timing method.

Great series! Absolutely love your site and all the effort you put into your articles! Do you know if anyone has ever done a SWR analysis for international markets or for an asset allocation that includes developed and emerging markets?

Great series! Absolutely love your site and all the effort you put into your articles! Do you know if anyone has ever done a SWR analysis for international markets or for an asset allocation that includes developed and emerging markets?

Hi Big ERN

I used portfolio visualiser to test a a more global portfolio using the following available categories – US stock market, global ex us stock market (split evenly between them) and global bonds unhedged. I experimented with allocations from 50/50 to 100/0 (us was always half the stock part) and set it as 2 worst years at start of retirement thereby accounting for sequence of return risk. I assumed crises usually last 2 years. Funnily enough 50/50 seemed to do better than higher equity allocations.(Just as an fyi I did include pension at 65 and the available data due to the bond part was from 1994 onwards). Was wondering if you had any idea why the difference? Thanks.

Sorry forgot to mention that I saved up as if I was going to retire on the 4% safe withdrawal rate and suddenly retired on a crisis as per the above.

Not sure what exactly you calculated. Do you a Google Sheet with the calculations? Thanks!

Thanks for responding. I used https://www.portfoliovisualizer.com , not a Google sheet. They have what they call a monte Carlo tool and a financial goals tool.