Engaging Data

It's a valid question.

The honest answer could simply be "yes." Depending on your circumstances. The more nuanced answer is...

Look at things like The Trinity Study / Monte Carlo simulation, https://www.cfiresim.com/, https://engaging-data.com/visualizing-4-rule/, https://firecalc.com/intro.php, https://retirementplans.vanguard.com/VGApp/pe/pubeducation/calculators/RetirementNestEggCalc.jsf and so on.

To start, we assume that 1) we cannot predict the future but 2) the best predictor of the future is the past.

Then we make some assumptions that we think we could apply to the future, like how much we'll spend each year, how we'll allocate our assets / investments, and how long we need our money to last. From there, we take historical data and apply our unique assumptions to the historical data simulating each time period available, and we take a close look at the results.

To get 100% success, you'd need something like no inflation, or invested assets greatly exceeding our expenses. For example, if you spend $100,000 / year, and you have $4 million invested, even if inflation matched your investment return, you could still live off this money for 40 years.

But you probably don't need a true 100% success rate. More likely, you need just a bit of flexibility, and a backup plan. Most failures happen due to something like stagflation or negative returns very early into your retirement. Your best option at that point is to return to work and reduce expenses until you've passed…

I mean, this just appears to be fixed nominal returns, minus a fee, multiplied by a factor from an actuarial table.

Contrast it with a calculator like this [0] that uses combines historical return and inflation data with actuarial data to show the variance of outcomes, not just average returns.

For instance, your calculator shows a scenario of investing in bitcoin and withdrawing >20% of your portfolio every year which makes zero sense once you account for variance of returns.

I like the idea of tontines, I'm glad someone is trying to bring them back, and I don't doubt that your product could help with longevity risk, but I haven't seen anything so far that actually shows that.

I'd like to see actual results from backtesting, or a prediction that takes risk into account, not just a fixed return.

[0]https://engaging-data.com/will-money-last-retire-early/

This was a well written article.
Making people aware of the engineering/hydrological analysis of a possible event is not fear mongering. It is the first step to planning.

"modeled on the Great Flood of 1862, which also made a lake of the Central Valley". https://en.wikipedia.org/wiki/Great_Flood_of_1862

The failure of the Oroville Dam spillway in 2017 and the hasty evacuation of home downstream was a real crisis event. Today Oroville is at 100% capacity [1] but snowpack seems to have been greatly reduced from the peak this year [2].
One could easily visualize a scenario where a rapid warming in April of this year could have caused snow melt to overwhelm the reservior flood storage capacity.

[1] https://engaging-data.com/ca-reservoir-dashboard/

[2] https://engaging-data.com/california-reservoirs-and-snowpack/

> Yet the whole transaction needs the Sell part to realize the gains. Surprisingly, the Schwab experiment did not model this for the "participants".

The FIRE community did model this at great length though. And the example in TFA is just an example: saving $2K a year is basically drinking one or two beers less each day (so I wouldn't look too much into that amount). Most people in the west could save that. At the very least the people at which TFA is aimed could save $2K a year.

Try $20K a year: most working people here could save that.

Here's a nice "rich, broke or dead" FIRE calculator:

https://engaging-data.com/will-money-last-retire-early/

Yeah quite a few FIRE tools which allow you to model retirement scenarios will let you pre-fill return rates based on historic returns.

This one is pretty nice to mimic. You have to Ctrl+F for 'projection method' and then switch it to Historic Cycles, or Monte Carlo Simulation:

https://engaging-data.com/fire-calculator/

Then you'll see basically a distribution appear on the graph with the median (50%), but also the 25% & 75%, and the 10% & 90% likely return scenarios.

Quite useful to see what a bottom 10% ('worst-case') scenario would look like, and how it would affect your finances and your timelines.

It's pretty common in the US for a name that is traditionally associated with boys become unisex (Taylor) or even prominently female (Lauren). Typically it is an uncommon male name (so not, e.g. John or Brian) that sees a sudden increase in popularity and becomes unisex. Some other examples include Leslie, Casey, Jordan, Morgan, Addison, and Riley. I can't think of any examples of the reverse off the top of my head.

A chart of this phenomenon for Taylor:

https://engaging-data.com/baby-name-visualizer/?n=taylor&sex=b&data=n