This calculator is designed to let you learn as you play with it. Tweaking inputs and assumptions and hovering and clicking on results will help you to really gain a feel for how withdrawal rates and market returns affect your chance of retirement success (i.e. making it through without running out of money).
Inputs You Can Adjust:
Options for Visualization:
The 4% rule is a “rule of thumb” relating to safe retirement withdrawals. It states that if 4% of your retirement savings can cover one years worth of retirement spending (an alternative way to phrase it is if you have saved up 25 times your annual retirement spending), you have a high likelihood of having enough money to last a 30+ year retirement. A key point is that the probabilities shown here are just historical frequencies and not a guarantee of the future. However, if your plan has a high success rate (95+%) in these simulations, this implies that retirement plan should be okay unless future returns are on par with some of the worst in history.
The overall goal of this rule and analysis is identifying a “safe withdrawal rate” or SWR for retirement. A withdrawal rate is the percentage of your money that you withdraw from your retirement savings each year. If you’ve saved up $1 million and withdraw $100,000 each year, that is a 10% withdrawal rate.
The “safe” part of the withdrawal rate relates to the fact that if your investments generally grow by more than your annual spending, then your retirement savings should last over the length of your retirement. But average returns do not tell the whole story as the sequence of returns also plays a very important role, as will be discussed later.
One way to test this is through a backtesting simulation which forms the basis for the “Trinity Study”.
The “Trinity Study” is a paper and analysis of this topic entitled “Retirement Spending: Choosing a Sustainable Withdrawal Rate,” by Philip L. Cooley, Carl M. Hubbard, and Daniel T. Walz, three professors at Trinity University. This study is a backtesting simulation that uses historical data to see if a retirement plan (i.e. a withdrawal rate) would have survived under past economic conditions. The approach is to take a “historical cycle”, i.e. a series of years from the past and test your retirement plan and see if it runs out of money (“fails”) or not (“survives”).
Given modern equity and bond market data only stretches back about 150 years, there is some, but not a huge amount of data to use in this simulation. One example of a 30 year historical cycle would be 1900 to 1930, and another is 1970 to 2000. The Trinity study and this calculator tests withdrawal rates against all historical periods from 1871 until the present (e.g. 1871 to 1901, 1872 to 1902, 1873 to 1903, . . . . 1986 to 2016). Then across this 115 different historical cycles, it determines how many of these survived and how many failed.
The thinking is that if your retirement plan can survive periods that include recessions, depressions, world wars, and periods of high inflation, then perhaps it can survive the next 30-50 years.
The 4% rule that comes out of these studies basically states that a 4% withdrawal rate (e.g. $40,000 annual spending on a $1,000,000 retirement portfolio) will survive the vast majority of historical cycles (~96%). If you raise your withdrawal rate, the rate of failure increases, while if you lower your withdrawal rate, your rate of failure decreases.
The goal of this tool is to help you understand the mechanics of the a historical cycle simulation like was used in the Trinity Study and how the 4% rule came to be. This understanding can help you better plan for retirement with the uncertainty that goes along with planning 30+ years into the future. If you want to also see how longevity and life expectancy play a role in retirement planning, you can take a look at the Rich, Broke and Dead calculator.
This post and tool is a work in progress. I have a number of ideas that I will implement and add to it to help improve the visualization and clarity of these concepts.
The future is unlikely to be identical to any of the set of historical cycles that are used in this simulation. And yet, there are enough years of data that there are a fairly large set of possible outcomes from running a simulation with this input data. One way to understand this variation is to see in the main graph above that the ending balance can potentially vary by more than $5 million dollars on an inflation adjusted basis on a starting balance of $1 million.
Another way to see this same variation in market returns is by looking at maximum withdrawal rate. This is the highest amount that you could withdraw annually over your retirement and (just barely) not run out of money by the end of your retirement.
This graph shows the maximum withdrawal rate for a given historical cycle (i.e. 1871 to 1901). For example, in the 1871 to 1901 30 year historical cycle, you could have used an 8.8% withdrawal rate (inflation adjusted $80,000 withdrawal annually on a $1 million initial investment balance) and not run out of money. This is because the sequence of market (stock and bond) returns in this historical cycle were able to (barely) outpace the rate of withdrawals at the end of the 30 year retirement period. Many other cycles show lower successful withdrawal rates, because those cycles had poorer sequences of returns, while some had higher maximum withdrawal rates.
The graph also highlights those cycles that show a maximum withdrawal rate below 4% in red, while all others are shown in green. Most of these withdrawal rates are well over 4%, with some quite a bit higher. This again shows that if the future is somewhat like one of these historical cycles, most likely a 4% withdrawal rate will be enough for you to retire without running out of money and that it is likely that you could end up with more money than you started.
I added a simple spending flexibility parameter that allows you to specify how much you could reduce spending when your portfolio is below a certain threshold (in this case, your original retirement amount (inflation adjusted)).
One of the key issues with retiring is the question of outliving your money. This is also known as Longevity Risk and is especially important if you want to retire early, since your retirement could be 50 years long (or more). This interactive post-retirement calculation and visualization looks at the question of whether your retirement savings can last long enough to support your retirement spending and combines it with average US life expectancy values to get a fuller picture of the likelihood of running out of money before you die.
It helps to answer the question: If I start out with $X dollars at the beginning of my retirement, will I run out of money before I die?
The stock market has been on a bull run (hitting numerous all time highs) for the last 8+ years and it’s not clear when it will end. Whenever there’s been an extended bull run, one question that comes to mind “Should I invest in the market now, or wait until a pullback?” The question comes about because of fear and loss aversion: fear that the market will drop right after they invest and the observation that people want to avoid losses more than they value gains. However, historically, the correct answer, at least over the last 69 years, has been to invest and not to try to time the market.
This was also demonstrated in the Market Timing Game; that people are pretty bad at predicting the direction of the markets and given the upward trend of the market, it’s simpler and more likely than not, better to just stay invested in the market. The corollary to this is that when you have additional money to invest (e.g. from regular savings from your paycheck or a one-time event like the sale of a house), it makes sense to invest the money and not worry about whether the market is at a high or low point. Some graphs that look at the distribution of returns when the market is at an all time high (ATH) can help answer this question of whether you expect to see worse returns than investing at other times.
The Market Timing Game simulation is premised on the idea that buying-and-holding index investing and index funds are a no-brainer investment strategy and market timing (i.e. trying to predict market direction and trading accordingly) is a less than optimal strategy. The saying goes “Time in the market not timing the market”. In this simulation, you are given a 3-year market period from sometime in history (between 1950 and 2018) or you can run in Monte Carlo mode (which picks randomly from daily returns in this period) and you start fully invested in the market and can trade out of (and into) the market if you feel like the market will fall (or rise). The goal is to see if you can beat the market index returns.