Retirement Spreadsheet Recommended By Reddit
The inputs for this spreadsheet include:
- Working ROI Estimated annual return on investment during working years.
- Retired ROI Estimated annual return on investment during retirement.
- Retired SWR Safe Withdraw Rate, or amount of total investment you plan on withdrawing annually during retirement.
- Savings Rate Percentage of income you will save during working years.
- Beginning Bal
They Measure Market Risk But Dont Eliminate It And They Don’t Consider All The Twists And Turns Life Can Take Instead Build A Plan To Create A Stable Income Throughout Your Retirement No Matter What
When you sit down with a traditional financial adviser to plan your retirement you will provide her with the spending budget you have in mind. The adviser will adjust that amount for inflation and after running the numbers through a black box model, will predict how many years your retirement savings typically made up of cash, stocks and bonds will last.
The model to make this prediction is called stochastic a fancy way to describe what is a typical Monte Carlo simulation model.
How Can You Simulate Values Of A Discrete Random Variable
Suppose the demand for a calendar is governed by the following discrete random variable:
Greater than or equal to 0.45, and less than 0.75
Greater than or equal to 0.75
To demonstrate the simulation of demand, look at the file Discretesim.xlsx, shown in Figure 60-2 on the next page.
The key to our simulation is to use a random number to initiate a lookup from the table range F2:G5 . Random numbers greater than or equal to 0 and less than 0.10 will yield a demand of 10,000 random numbers greater than or equal to 0.10 and less than 0.45 will yield a demand of 20,000 random numbers greater than or equal to 0.45 and less than 0.75 will yield a demand of 40,000 and random numbers greater than or equal to 0.75 will yield a demand of 60,000. You generate 400 random numbers by copying from C3 to C4:C402 the formula RAND. You then generate 400 trials, or iterations, of calendar demand by copying from B3 to B4:B402 the formula VLOOKUP. This formula ensures that any random number less than 0.10 generates a demand of 10,000, any random number between 0.10 and 0.45 generates a demand of 20,000, and so on. In the cell range F8:F11, use the COUNTIF function to determine the fraction of our 400 iterations yielding each demand. When we press F9 to recalculate the random numbers, the simulated probabilities are close to our assumed demand probabilities.
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So What Does The Newretirement Monte Carlo Simulation Tell You About Your Future
There are many different ways to define retirement success. And, the NewRetirement Planner offers many different analyses.
One measure is the probability that you have adequate savings to cover your expenses through your lifetime.
Here is the chart in the NewRetirement Planner that uses Monte Carlo simulations. It actually shows both linear and Monte Carlo projections:
In the chart, you can see your:
Linear Savings Projection : The light green line at the top of the chart indicates your linear projection for your savings. It is based on your specified rates of return and your inflation projections. You generate the chart using your optimistic, pessimistic or an average of those rates.
Using a linear projection, the person represented in the chart above will have $1.2 million in 2061 and adequate savings to fund retirement through their expected lifetime.
Downside risk : The dark line on the chart is generated with Monte Carlo simulations. We run multiple projections, randomly varying asset returns and inflation rates based on historical data and a normal distribution. We run 1,000 of these simulations and use the results to generate the probability of plan success.
The Poor Outcome is indicated by the dark green line in chart. It represents the 10th percentile of these simulations. In other words, 90% of the simulations did as well or better.
The 3 Best Free Retirement Calculators
Retirement calculators have an impossible job. They use various imperfect mathematical techniques in an attempt to predict the distant future. That cant be done reliably. So they will inevitably be wrong.
Add to that the difficulties in communicating the many variables involved, and all the possible simulation options, to everyday users with limited time. And then there are the usual issues with designing friendly software, getting the bugs out, and then delivering it to work reliably on dozens of different hardware/software platforms across the web.
In the years leading up to that retirement, plus the last couple years of blogging, Ive evaluated 34 different retirement calculators! And, Ive kept extensive notes on each.
For this post I went back through those notes and picked the top half-dozen or so free calculators to re-evaluate in depth. Of those, three made my final cut, as worthy of recommendation to you. And Ill unveil them shortly, below. But first lets develop a retirement scenario, and then talk a little about choosing and using retirement calculators.
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Constant Probabilities Of Success With Constant Adjustments
Lets first look at a scenario where we are targeting a constant 95% probability of success throughout retirement for the client, which is most akin to the typical financial advisor approach of targeting a 95% probability in the Monte Carlo retirement plan and adjusting spending recommendations over time to keep clients at that threshold of success. .
To do this, the clients spending levels are first set with a traditional Monte Carlo retirement analysis using capital market assumptions based on monthly historical returns going back through 1871. Since were using the same capital market assumptions at all points in time , every scenario is going to start out with the same monthly income.
In this 95% probability of success scenario, that amount is $6,769 per month , which, once we back out the $3,500 in monthly Social Security income, amounts to an initial portfolio withdrawal rate of approximately 3.9%.
From a statistical perspective, it would be ideal to use an in-sample approach and change capital market assumptions based on the information that one actually had available to them at each point in history as it unfolded, so that we arent cheating by giving someone access to data they would otherwise not have. However, for simplicity, we have used a single out-of-sample set of capital market assumptions based on historical averages over the entire time period.
Which raises the question what happens at even lower constant probabilities of success?
Why Are Monte Carlo Simulations Important For Retirement Planning
Building a financial forecast requires you to make guesses about things you have little or no control over that may happen in the future. These guesses are called assumptions. Assumptions are unknown factors that will impact your finances.
Some of the assumptions that will impact your future retirement finances include:
- Rates of return on savings and investments
- Cost of living increases
- Future tax rates and income thresholds
- Whether or not youll require long term care or suffer a major medical event
- Growth rates for work income
- And more
Monte Carlo simulations are a way of predicting the range of possible outcomes for these types of unknowns or assumptions.
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The Alternative Is Income Allocation
Income Allocation focuses on providing income that lasts a lifetime, instead of predicting the date your savings will run out.
The addition of annuity payments to the dividends and interest your portfolio generates guarantees income for life and smooths out the volatility that hangs like a dark cloud over other plans. That makes your planning much simpler, because you no longer must be totally dependent on the stock and bond markets.
Applying The Monte Carlo Simulation
The Monte Carlo simulation has numerous applications in finance and other fields. Monte Carlo is used in corporate finance to model components of project cash flow, which are impacted by uncertainty. The result is a range of net present values along with observations on the average NPV of the investment under analysis and its volatility. The investor can, thus, estimate the probability that NPV will be greater than zero. Monte Carlo is used for option pricing where numerous random paths for the price of an underlying asset are generated, each having an associated payoff. These payoffs are then discounted back to the present and averaged to get the option price. It is similarly used for pricing fixed income securities and interest rate derivatives. But the Monte Carlo simulation is used most extensively in portfolio management and personal financial planning.
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What Is Monte Carlo And What Does It Have To Do With Your Retirement Plan
How might you go about using Monte Carlo simulations for your retirement plan?
Well, first you need to understand what Monte Carlo is and what it isnt.
According to Dana Anspach, CFP, RMA, the president and founder of Sensible Money, its a form of “stress-testing” your future financial wealth against various market conditions.
Monte Carlo is based on using an assumed rate of return and an assumed level of volatility as measured by standard deviation, she said. Read How to Use Monte Carlo Simulations for Retirement Planning.
So, for example, for an asset class like large-cap U.S. stocks, you may have an assumed rate of return of 10% with a standard deviation of 15%. About 68% of values would be within one standard deviation, so in any one year you may expect large cap stocks to be up by 25% or be down in value by 6% – but of course you have an additional 32% of outcomes that can fall outside those ranges, said Anspach.
With Monte Carlo, you can change certain assumptions to stress test different scenarios. For instance, the user may choose historical returns, or forward-looking returns. If you used historical returns for the S& P 500, you’d assume an 11% average return, but if you were of the view that large-cap U.S. stocks are currently overvalued, you may assume a lower average going forward, she said.
So, what are some of the pros and cons of Monte Carlo?
So, should you incorporate Monte Carlo into your retirement planning?
Accounting For Investment Expenses
Lastly, note that its very rare for retirement calculators to prompt for an investing expense ratio. They may include it in their calculations automatically. In which case it may be much too high if youre a low-cost passive index investor. Or they may ignore it altogether in which case youd better be a low-cost investor, because otherwise the results will be too optimistic!
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Monte Carlo Methods In Finance
Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of stochastic asset models. The advantage of Monte Carlo methods over other techniques increases as the dimensions of the problem increase.
Monte Carlo methods were first introduced to finance in 1964 by David B. Hertz through his Harvard Business Review article, discussing their application in Corporate Finance. In 1977, Phelim Boyle pioneered the use of simulation in derivative valuation in his seminal Journal of Financial Economics paper.
This article discusses typical financial problems in which Monte Carlo methods are used. It also touches on the use of so-called “quasi-random” methods such as the use of Sobol sequences.
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Get Started With An Easy Retirement Template
You can build your own retirement planner, but its faster and easier to get started with a free template. So here are seven of the best free retirement spreadsheet templates.
These spreadsheets can even help you find the best age to retire. For example, you might want to plan for early retirement, or a hybrid scenario where you keep working part-time or otherwise choose to delay your social security benefits.
The Value Of Monte Carlo Simulations In Financial Planning
As part of our financial planning process, when clients have to make important decisions, we like to stress test their plan. One of the tools that we may use is a Monte Carlo simulation. When used in coordination with our traditional straight-line financial planning projections and our Hurdle Rate calculations, Monte Carlo helps us compare different retirement planning options or scenarios to help project and illustrate which alternatives will have the highest probability of success . Bottom line Monte Carlo is a tool that can help us help our clients make better and more informed retirement and investment planning decisions.
Estimating Investment Returns
All financial forecasts must account for variables like inflation rates, market volatility and investment returns. The catch is that these variables have to be estimated, and the estimate used is key to a forecasts results. For example, a forecast that assumes stocks will earn an average of 4% each year for the next 20 years will differ significantly from a forecast that assumes an average annual return of 8% over the same period.
Estimating investment returns is particularly difficult. For example, the volatility of stock returns can make short-term projections almost meaningless. Multiple factors influence investment returns, including events such as natural disasters and terrorist attacks, which are unpredictable. So, its important to understand how different forecasting methods handle uncertainty.
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Where Does The Market Risk Go
Admittedly the legacy you leave your kids or grandkids might be lower earlier in retirement when you employ an Income Allocation plan. Thats because your plan protects your retirement income for life in the form of annuity payments, which reduce your risks against long-term poor market performance. But it comes with a trade-off. In this case it will be the amount you pass along to your heirs early in retirement.
My view is that heirs should welcome that trade-off. If your Monte Carlo scenario or just poor planning leaves them on the hook for your income when your savings run out, they will appreciate your smarter lifetime planning. Although theres a chance you may be passing along a smaller legacy to the kids, Income Allocation allows you to maintain your independence as long as possible.
Bonus: Net Worth Tracker
Whether your goal is to retire at 35 or 75, tracking your net worth can keep you organized and speed your progress. Indeed, tracking your net worth is a powerful way to stay aware of your financial standing, making it easier to prepare for retirement.
It can also help you make smarter money decisions for your long-term financial health.
Read more about tracking your net worth with Tiller
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What Is A Monte Carlo Simulation
Analysts can assess possible portfolio returns in many ways. The historical approach, which is the most popular, considers all the possibilities that have already happened. However, investors shouldn’t stop at this. The Monte Carlo method is a stochastic method to solve a statistical problem, and a simulation is a virtual representation of a problem. The Monte Carlo simulation combines the two to give us a powerful tool that allows us to obtain a distribution of results for any statistical problem with numerous inputs sampled over and over again.
Uses In Portfolio Management
A Monte Carlo simulation allows an analyst to determine the size of the portfolio a client would need at retirement to support their desired retirement lifestyle and other desired gifts and bequests. She factors into a distribution of reinvestment rates, inflation rates, asset class returns, tax rates, and even possible lifespans. The result is a distribution of portfolio sizes with the probabilities of supporting the client’s desired spending needs.
The analyst next uses the Monte Carlo simulation to determine the expected value and distribution of a portfolio at the owner’s retirement date. The simulation allows the analyst to take a multi-period view and factor in path dependency the portfolio value and asset allocation at every period depend on the returns and volatility in the preceding period. The analyst uses various asset allocations with varying degrees of risk, different correlations between assets, and distribution of a large number of factors including the savings in each period and the retirement date to arrive at a distribution of portfolios along with the probability of arriving at the desired portfolio value at retirement. The client’s different spending rates and lifespan can be factored in to determine the probability that the client will run out of funds before their death.
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