firecalc and cFIREsim both lie?

[lauren_knows]

How hard would it be to add a buffer to the over spend.  meaning i probably wont start spending over my starting annual withdrawl plus inflation until i know i'm safe.  say the first 5-10 years then maybe.  then i will probably be comfortable spending up to a higher amount on good market years.

Trying to protect against sequence of returns risk, eh?  How do you envision this buffer working, exactly?  Right now, there is a Spending Ceiling, but that isn't time dependent, so it can't exactly do what you want.

[boarder42]

1.So the first x many years of gains I don't increase spending by anything more than inflation. Where x is a variable in years the market went up. Say you put in 5. And market goes uuuddddddduu. Where u is up more than inflation and d is down each represents a year. So at the end of that I have hit my 5 up years now I'm willing to go to my spending ceiling.

2.  If that's too complicated (not knocking your skills just don't know how your formula works). Say I don't want to increase my ceiling for x years regardless of gains. So I will just increase for inflation til I hit my x years mark then after that I will use my ceiling.

[DoubleDown]

Isn't trying to simulate all these n-th degree variables and complexities in a long-term ER simulation ultimately a fool's errand? Honest question here, not just poking holes.

I mean, I really value cFIREsim, so I hope bo_knows does not take this as me stating we should give up on the value it delivers. But trying to simulate such complex situations using historical returns seems akin to introducing a small error in an equation that gets amplified so much that it makes the outcome meaningless (such as when you have a 5% error margin, and then multiply and multiply it so many times that the resulting window of error exceeds the result itself).

It seems to me that about every year, we're all going to have to re-evaluate where we are financially and make determinations and tweaks along the way. Trying to accurately model a 30, 40, 50, 60-year spending plan based on hypothetical conditions and past market returns seems impossible, too open to invalid assumptions. Because ultimately, what you get out of FIRecalc or cFIREsim is a "confidence factor" in how likely your portfolio will survive, and I think it would be misleading to believe any spending plan is 93% (or whatever) likely to succeed.  But prove me wrong!

[sol]

If I'm reading those results right, the proposed spending plan (adjusted by half of CPI + portfolio return) increases your SWR by about 0.4% for equivalent risks.  Meaning 4% SWR still fails 7% of the time but the new adjusted SWR can start at 4.4% and only fail times.  For perspective, you can get the same increase in your expected SWR by tilting your portfolio more heavily towards small caps, so the difference is not huge.

Conversely, setting z=1 so that you adjust this years withdrawal by last years CPI + portfolio return straight up seems to be a huge improvement but I'm pretty sure it's bugged.  It's telling me this plan NEVER fails regardless of initial withdrawal rate, which can't be right.  Any suggestions on what's going on there bo?

Low values of z are also giving me funny numbers, where the portfolio rapidly plummets to zero for z values of 10% instead of 50%.  I think it still needs a little tweaking.

[warfreak2]

Conversely, setting z=1 so that you adjust this years withdrawal by last years CPI + portfolio return straight up seems to be a huge improvement but I'm pretty sure it's bugged.  It's telling me this plan NEVER fails regardless of initial withdrawal rate, which can't be right.  Any suggestions on what's going on there bo?

The simple strategy of always simply withdrawing 4% of your portfolio never fails, either, because there's always the other 96% left so you can't go broke. Adjusting equally to the market return (i.e. z=1) is mathematically equivalent to this. So as long as CPI never brings your 4% up to 100% (which would take over a century), you're always withdrawing less than 100% of the portfolio value. has a similar problem to this (see below).

The problem is still the lack of an (inflation-adjusted) minimum withdrawal - retirement plans also fail if they don't give you enough income to live on, but your analysis considers "going broke" as the only failure mode.

[sol]

The simple strategy of always simply withdrawing 4% of your portfolio never fails, either, because there's always the other 96% left so you can't go broke. Adjusting equally to the market return (i.e. z=1) is mathematically equivalent to this. So as long as CPI never brings your 4% up to 100% (which would take over a century), you're always withdrawing less than 100% of the portfolio value.

If that's what it's doing, then it's not at all what I was hoping.  The idea isn't to withdraw a percentage of your portfolio equal to some fraction of the CPI+returns, it is to withdraw last year's withdrawal amount adjusted by some fraction of the CPI+returns.  So in years when the market is down 30% and CPI is zero, your withdrawal amount shrinks 15% from last year.  Your strategy would be impossible to implement in negative return years, right?  How would you withdraw negative 15% of your portfolio?

The problem is still the lack of an (inflation-adjusted) minimum withdrawal

cFIREsim already has a means of addressing this concern.  You can always specify a floor of minimum spending.  I'm not interested in those cases, I'm trying to demonstrate that voluntarily reducing withdrawals in down years dramatially increases your success rate for equivalent SWRs.

[warfreak2]

Your strategy would be impossible to implement in negative return years, right?  How would you withdraw negative 15% of your portfolio?

I was describing your strategy with z=1, so if the market dropped 30% with 0% inflation you would withdraw 30% less than last year.

Aside from the inflation adjustment, your withdrawal as a percentage of your portfolio is unaffected by the market return:

• Suppose you start with $1MM and withdraw $40k. Next year the market drops 30%, and so you withdraw 0.7*$40k = $28k on a portfolio of 0.7*$960k = $672k. $28k/$672k = 4.17% of your portfolio.
• Suppose instead the market rose 30% with inflation of 0%. Then next year you would withdraw 1.3*$40k = $52k on a portfolio of 1.3*$960k = $1248k. $52k/$1248k = 4.17% of your portfolio.
  
• Algebraically, if the market return is r, and inflation is 0, then you're withdrawing $40k*(1+r) on a portfolio of $960k*(1+r). ($40k*(1+r))/($960k*(1+r)) = 4.17% of your portfolio.

Looks like I did make a mistake though, you aren't still withdrawing 4%, so it's not a guaranteed "success" - in fact (inflation aside) the strategy should be a guaranteed failure after 25 years, since that's what would happen with constant market returns of 0%, and we've established that the market returns don't affect the percentage of portfolio withdrawn.

If you adjust the market return by subtracting 4% (which would be sensible, since returns below 4% aren't really "good years" when you have to get 4% just to break even) then it's fully equivalent to a constant-percentage-of-portfolio withdrawal.

cFIREsim already has a means of addressing this concern.  You can always specify a floor of minimum spending.  I'm not interested in those cases, I'm trying to demonstrate that voluntarily reducing withdrawals in down years dramatically increases your success rate for equivalent SWRs.

The thing is, by not setting a minimum inflation-adjusted withdrawal, all you're really demonstrating is that you can dramatically increase your success rate by not necessarily withdrawing enough to buy food and heat. Guaranteeing a minimum inflation-adjusted withdrawal is part of what success means, so you can't ignore it when calculating a success rate. Otherwise withdrawing $0 every year would be the best strategy.

[arebelspy]

Isn't trying to simulate all these n-th degree variables and complexities in a long-term ER simulation ultimately a fool's errand? Honest question here, not just poking holes.

I mean, I really value cFIREsim, so I hope bo_knows does not take this as me stating we should give up on the value it delivers. But trying to simulate such complex situations using historical returns seems akin to introducing a small error in an equation that gets amplified so much that it makes the outcome meaningless (such as when you have a 5% error margin, and then multiply and multiply it so many times that the resulting window of error exceeds the result itself).

It seems to me that about every year, we're all going to have to re-evaluate where we are financially and make determinations and tweaks along the way. Trying to accurately model a 30, 40, 50, 60-year spending plan based on hypothetical conditions and past market returns seems impossible, too open to invalid assumptions. Because ultimately, what you get out of FIRecalc or cFIREsim is a "confidence factor" in how likely your portfolio will survive, and I think it would be misleading to believe any spending plan is 93% (or whatever) likely to succeed.  But prove me wrong!

Yes.  Everyone here agrees it's not going to be accurate, and will require reevaluation.  I don't think anyone is going to come up with a plan and blindly stick to it for 40 years because of what some online calculator told them decades before.

On the other hand, tweaks like this allow someone to get a rough plan in mind for how they will deal with the trying times.  Knowing historically that if you cut X% from your budget you'd have a 93% success rate doesn't mean you're counting on it to work exactly 93% of the time, but it does allow you to compare it to other plans and see what has traditionally been more, or less, successful, and create a rough plan to stick with that you're comfortable with and can continually reevaluate from.

Most previous studies are so rigid ("withdraw 4%, adjust it up for inflation every year blindly") that it's nice to tweak with realistic parameters ("I'd cut my 20% travel budget if the market was down") and see how you'd have fared so you can come up with a plan you're comfortable with, and see what that does to a success rate and the "number" you have to hit.

[Spudd]

I think the confusion is in "withdraw x% of portfolio" vs "withdraw x% of initial portfolio". I think the latter is what the intention was. Clearly the former will never fail. But it could result in some very lean years!

[lauren_knows]

Isn't trying to simulate all these n-th degree variables and complexities in a long-term ER simulation ultimately a fool's errand? Honest question here, not just poking holes.

I mean, I really value cFIREsim, so I hope bo_knows does not take this as me stating we should give up on the value it delivers. But trying to simulate such complex situations using historical returns seems akin to introducing a small error in an equation that gets amplified so much that it makes the outcome meaningless (such as when you have a 5% error margin, and then multiply and multiply it so many times that the resulting window of error exceeds the result itself).

It seems to me that about every year, we're all going to have to re-evaluate where we are financially and make determinations and tweaks along the way. Trying to accurately model a 30, 40, 50, 60-year spending plan based on hypothetical conditions and past market returns seems impossible, too open to invalid assumptions. Because ultimately, what you get out of FIRecalc or cFIREsim is a "confidence factor" in how likely your portfolio will survive, and I think it would be misleading to believe any spending plan is 93% (or whatever) likely to succeed.  But prove me wrong!

I take no offense.  Although other people have answered, I'll state my peace on the whole thing.  No, I do not have any delusions that cFIREsim (or ANY simulator/calculator) is of scalpel precision.  However, being able to tweak and adjust a spending plan, so that you can better prepare for the future (even if you don't follow it exactly) isn't really a fools errand in my opinion.  If you weren't playing with the numbers, maybe you wouldn't realize that a big market drop in the first couple years of retirement is cause for great concern when it comes to withdrawals.  Just getting a rough idea of what makes a portfolio survive better, can give you better expectations.  It doesn't need to be perfect  *shrug*.

The thing is, by not setting a minimum inflation-adjusted withdrawal, all you're really demonstrating is that you can dramatically increase your success rate by not necessarily withdrawing enough to buy food and heat. Guaranteeing a minimum inflation-adjusted withdrawal is part of what success means, so you can't ignore it when calculating a success rate. Otherwise withdrawing $0 every year would be the best strategy.

The spending floor/ceilings ARE inflation-adjusted.  Maybe I should label that, eh? :)

The simple strategy of always simply withdrawing 4% of your portfolio never fails, either, because there's always the other 96% left so you can't go broke. Adjusting equally to the market return (i.e. z=1) is mathematically equivalent to this. So as long as CPI never brings your 4% up to 100% (which would take over a century), you're always withdrawing less than 100% of the portfolio value.

If that's what it's doing, then it's not at all what I was hoping.  The idea isn't to withdraw a percentage of your portfolio equal to some fraction of the CPI+returns, it is to withdraw last year's withdrawal amount adjusted by some fraction of the CPI+returns.  So in years when the market is down 30% and CPI is zero, your withdrawal amount shrinks 15% from last year.  Your strategy would be impossible to implement in negative return years, right?  How would you withdraw negative 15% of your portfolio?

Sol, it's working as you think it is... it's not the way warfreak2 describes.  But, he is correct... the straight 4% method never "fails", but it can cut your expenses quick.  That is why you're seeing the z=1 have a 100% success rate.  I mean, define success?  It says that the average withdrawal rate is $57k (impressive, no?), but the standard deviation (volatility) of that withdrawal amount is huge.  Also, look at the lowest values. The lowest every was $15k, and it at least 1 instance, withdrawal rates dropped to $18k in the first 5 years of retirement.

I don't know about you, but if I'm expecting about $40k in income, I'm not going to consider $15k a "success".  You need to set spending floor/ceiling's in there to meet your expectation of income.   If you expect $40k, and can live with $32k, but $28k seems too low, then you know where to put the spending floor.

[lauren_knows]

Low values of z are also giving me funny numbers, where the portfolio rapidly plummets to zero for z values of 10% instead of 50%.  I think it still needs a little tweaking.

The rapid plummeting of spending to zero is some new code that I introduced.  Those buggy looking instances are actually the year that a portfolio goes below $0.  I set the code up to automatically change your spending to $0 if your portfolio fails, that's because you get all sorts of weird problems when you start multiplying market gain with negative portfolios, when trying to dynamically change your spending based on these multiplied values.

[DoubleDown]

Okay, I'm definitely in agreement with you all and in favor of the goal, which is to determine how different factors affect the survivability of a portfolio. But to get engineering-geeky, my question has to do with the margin of error. How do we know how precise these outcomes really are?

For example, if changing a variable (like spending "z" or whatever) increased the survivability from, say, 94% to 97%, then that 3% change is only meaningful if the precision is far better than 3% -- like, in the tenths of percentage points. If the margin of error is +/- 10%, then a 3% change is meaningless. When we're talking about long time horizons, my intuition tells me that we're greatly magnifying potential error margins so much that any perceived change in the results is meaningless. As in, we could not say if changing the variable mattered one iota, it may in fact have the opposite effect of what we think, but we don't know because of the large margin for error.

I realize no one (with any smarts) is making high-precision decisions with the simulations, but I'm playing devil's advocate wondering if the entire exercise is essentially impossible because we're dealing with too large of a margin for error?

[arebelspy]

We don't know for sure.  There is way to many variables.

But it's plenty reasonable to conclude that if a strategy of X would have been beneficial to a portfolio in the last 97 periods of history that can be run, it may also be helpful in the future.

Further, does it matter if we don't know "for sure"?  Isn't it still better than the alternative, throwing up our hands?

Your point of "don't rely on its precision too much" is well taken, but beyond that, I don't think saying "we can't know for sure" tells us anything.

[sol]

But to get engineering-geeky, my question has to do with the margin of error. How do we know how precise these outcomes really are?

You're misconstruing the cFIREsim model.  It's not a mathematical model like most engineers are used to seeing, in which things like residual errors can be computed and compared.  cFIREsim has no objective function, no target observations, no discretization scheme.  It's just a list of historical market performance evaluated retrospectively against withdrawal patterns.  It doesn't have any "errors" because it is a discrete list of numbers calculated different ways.

There is certainly a large area of uncertainty because the future may not look like the past.  But I don't think anybody believes the next 100 years in the market will look exactly like the last 100 years.  The idea isn't to be able to predict exactly how your future portfolio will hold up, it's to see how your portfolio would have held up under all available recorded historical periods.  There is no "precision" and no multiplying "margin of error" because it's not that kind of model.

In modeler terms, the problem with cFIREsim is a problem with the conceptual model, not the numerical model.  It is formulated a particular way, using all available past data, but then it numerically treats that data perfectly.  If the future is different from the past, say zombie apocalypse or nuclear holocaust, then of course the cFIREsim results will be utter garbage.  It's not useful to people who think those are likely outcomes, but it is very useful to people who want to use the best available information about the history of the US market to better position themselves for the future of the US market.

[Nords]

Okay, I'm definitely in agreement with you all and in favor of the goal, which is to determine how different factors affect the survivability of a portfolio. But to get engineering-geeky, my question has to do with the margin of error. How do we know how precise these outcomes really are?

Precision is for theorists.  We all know what happens when you try to turn a skewed bell curve (especially one with fat tails) into a log-normal distribution. 

Everybody builds huge statistical structures on top of that assumption and loses sight of the questionable validity of the original premise.

Instead of trying to quantify the precision, apply a better algorithm derived from weapons engineering.  Shoot at the target as soon as you have it in range and can track a decent solution, then depend on mid-course guidance and terminal homing. 

If you're 95% certain that you can hit a target, then would you shoot?  This is where the term "polishing a cannonball" came from.

I used to shoot 50-knot torpedoes at targets that could evade at 30 knots.  We used to do it from ranges as great as 10,000 yards and solutions that were only within about 1000 yards of accuracy, even though the torpedo might run out of fuel at 20,000 yards.  But the torpedo had a pretty good sonar sensor on it, and its terminal homing did a great job of refining our initial solution even if the target deviated from its original track and tried to evade...

In financial terms, you want a portfolio that's relatively insensitive to the risk factors.  If a single point of failure can destroy your portfolio then you might not be adequately capitalized to begin with, so grasping for precision might be a sign that you don't have enough safety margin. 

[TomTX]

Okay, I'm definitely in agreement with you all and in favor of the goal, which is to determine how different factors affect the survivability of a portfolio. But to get engineering-geeky, my question has to do with the margin of error. How do we know how precise these outcomes really are?

Precision is for theorists.  We all know what happens when you try to turn a skewed bell curve (especially one with fat tails) into a log-normal distribution. 

Everybody builds huge statistical structures on top of that assumption and loses sight of the questionable validity of the original premise.

Instead of trying to quantify the precision, apply a better algorithm derived from weapons engineering.  Shoot at the target as soon as you have it in range and can track a decent solution, then depend on mid-course guidance and terminal homing. 

If you're 95% certain that you can hit a target, then would you shoot?  This is where the term "polishing a cannonball" came from.

I used to shoot 50-knot torpedoes at targets that could evade at 30 knots.  We used to do it from ranges as great as 10,000 yards and solutions that were only within about 1000 yards of accuracy, even though the torpedo might run out of fuel at 20,000 yards.  But the torpedo had a pretty good sonar sensor on it, and its terminal homing did a great job of refining our initial solution even if the target deviated from its original track and tried to evade...

In financial terms, you want a portfolio that's relatively insensitive to the risk factors.  If a single point of failure can destroy your portfolio then you might not be adequately capitalized to begin with, so grasping for precision might be a sign that you don't have enough safety margin.

I'm taking your analogy differently, at least for the sonar and homing - you want some flexibility in your retirement, whether that's reducing spending when the market crashes, or picking up a side hustle you enjoy. If the market crash is your target evading, the reduced spending and side hustles are your terminal homing and sonar.

[DoubleDown]

Right, I understand the difference in these simulations with historical data from simulations with, say, a built-in margin of error for measurements. Ultimately what I'm trying to say is I think these strategies for modifying spending based on CPI, down-market years, etc., is overly complex/precise, based on what the simulations give us. I think MMM's broad stroke approach with "safety margins" against 4% SWR failure is already ample ammunition (to further use Nords' metaphor!). Trying to tweak the simulations to the nth degree with slightly variable spending plans is putting too much confidence (imo) into the historical data.

In personal terms, if there was a bad market year or three, most should likely evaluate their retirement plan to see where they stand, and make some decisions about increasing income or decreasing spending  as needed. Walk a dog, or eat beans tonight, or both. Then they'd evaluate again after another year or two and see where they stand. I don't think it's advisable to think, "Everything's going to be okay, the robotic withdrawal simulations from history proved that as long as I withdraw no more than 3.876% this year of my original portfolio amount, I can likely weather it with 98% chance of success based on historical returns."

Anyway, thanks for putting up with my complainy-pantsiness. I really don't mean to rain on the parade, so carry on and make that simulation better (seriously, I will be very happy if some improved strategies come out of it)!

[DoubleDown]

In financial terms, you want a portfolio that's relatively insensitive to the risk factors.  If a single point of failure can destroy your portfolio then you might not be adequately capitalized to begin with, so grasping for precision might be a sign that you don't have enough safety margin.

I'm taking your analogy differently, at least for the sonar and homing - you want some flexibility in your retirement, whether that's reducing spending when the market crashes, or picking up a side hustle you enjoy. If the market crash is your target evading, the reduced spending and side hustles are your terminal homing and sonar.

Bingo on both counts (Tom's and Nords')! And to carry the metaphor further, your guy on the ship (Nords) is going to make adjustments along the way to deal with the enemy's own advances. Nords and his superiors are not going to follow a predetermined script laid out 14 years ago using chances of success based on historical battle outcomes. They're going to adapt (or die) based on current, evolving conditions.

[lauren_knows]

Right, I understand the difference in these simulations with historical data from simulations with, say, a built-in margin of error for measurements. Ultimately what I'm trying to say is I think these strategies for modifying spending based on CPI, down-market years, etc., is overly complex/precise, based on what the simulations give us. I think MMM's broad stroke approach with "safety margins" against 4% SWR failure is already ample ammunition (to further use Nords' metaphor!). Trying to tweak the simulations to the nth degree with slightly variable spending plans is putting too much confidence (imo) into the historical data.

In personal terms, if there was a bad market year or three, most should likely evaluate their retirement plan to see where they stand, and make some decisions about increasing income or decreasing spending  as needed. Walk a dog, or eat beans tonight, or both. Then they'd evaluate again after another year or two and see where they stand. I don't think it's advisable to think, "Everything's going to be okay, the robotic withdrawal simulations from history proved that as long as I withdraw no more than 3.876% this year of my original portfolio amount, I can likely weather it with 98% chance of success based on historical returns."

Anyway, thanks for putting up with my complainy-pantsiness. I really don't mean to rain on the parade, so carry on and make that simulation better (seriously, I will be very happy if some improved strategies come out of it)!

No need to be overly apologetic.  I think that we both have similar views.  Yes, MMM's "safety margins" are a great way to think about how to go through the future.  But, I think that trying to simulate those safety margins to any bit of accuracy, so that you can get an actual visual representation of how things might play out, is what drives people to use sites like cFIREsim.  Even if doing endless simulations is just teaching you a rough idea of how to actually manage your spending in retirement based on the market changes, it's better than winging it :)    That's how I look at it at least.  I prefer the idea of a variable spending with a "target" spending and a floor/ceiling.  In reality, I'm not going to follow a precisely laid-out 30yr long script for spending, but when the market drops 30% I'll know to tell myself "Hey, we should probably not go on vacation this year".   That's a good enough education for me.

[arebelspy]

In reality, I'm not going to follow a precisely laid-out 30yr long script for spending, but when the market drops 30% I'll know to tell myself "Hey, we should probably not go on vacation this year".   That's a good enough education for me.

And to add to that (because you could be doing that anyways, going off gut feeling), it's somewhat helpful, IMO, to go "Hey, market was down, I'll cut spending by X% by not going on vacation, and historically that would have kept me from running out of money a few extra times."  Knowing that data gives you more than a gut feeling to go on, and may also help with the motivation when you aren't getting that vacation.

Regardless of if the future is identical to the past, if it's even similar in nature, that's useful information.

[boarder42]

yes.  and not being on vacation in that down year could encourage some lower cost vacations in the future years so that even in a down year you can still go on a vacation

[Nords]

In reality, I'm not going to follow a precisely laid-out 30yr long script for spending, but when the market drops 30% I'll know to tell myself "Hey, we should probably not go on vacation this year".   That's a good enough education for me.

And to add to that (because you could be doing that anyways, going off gut feeling), it's somewhat helpful, IMO, to go "Hey, market was down, I'll cut spending by X% by not going on vacation, and historically that would have kept me from running out of money a few extra times."  Knowing that data gives you more than a gut feeling to go on, and may also help with the motivation when you aren't getting that vacation.

Regardless of if the future is identical to the past, if it's even similar in nature, that's useful information.

Just because this discussion isn't complicated enough already, here's another aspect of the choices.

When the market is down 30%, there's probably a recession.  That's a fantastic time for bargain vacations and contractor discounts.

Even if you feel obligated to cut back the vacation spending by 30%, there may still be 50%-off deals.  (Especially if you've been tracking prices.)  Better yet, your vacation location is likely to be less crowded with more opportunities for fun and less competition for resources like transportation, dinner reservations, or concert tickets.

When 9/11 happened, we'd been planning a trip to Disneyland for the October school break.  We had a long discussion about the issues, and we decided to go anyway.  When we started looking at tickets, there were discounts everywhere:  air travel, Disney, the local hotels, the local restaurants.  Best of all, the parks were practically empty... nobody needed a FastPass.

When the economy cratered again in 2008, we'd been talking about major concrete work around our house.  (Removing old FuturaStone, pouring a new lanai slab, stamped-concrete finishes.)  We got a 30% discount and the contractors were some of the happiest workers we've ever had.  We actually expanded the job scope because the labor was already on site and ready to go.

On the other hand, buying a used Prius when oil was over $140/barrel turned out to be a dumb idea-- even during a recession. 

[RootofGood]

I used to shoot 50-knot torpedoes at targets that could evade at 30 knots.  We used to do it from ranges as great as 10,000 yards and solutions that were only within about 1000 yards of accuracy, even though the torpedo might run out of fuel at 20,000 yards.  But the torpedo had a pretty good sonar sensor on it, and its terminal homing did a great job of refining our initial solution even if the target deviated from its original track and tried to evade...

Very apt analogy.  Don't wait and close to 1000 yards to get a 99.999% certainty of hitting your target.  Wait too long to pull the trigger and you'll be dead.  "Close enough" works in horseshoes, hand grenades, portfolio survivability, and torpedo launching apparently. 

[much ado]

I know this is an old thread, but the Mad Fientist wrote a nice article using the Shiller P/E ratio to predict the success of different withdrawal rates here
http://www.madfientist.com/safe-withdrawal-rate/