Contents
The Future Simulation tool in Stock Rover tests long-term expected portfolio growth under a variety of possible scenarios. It is based on Monte Carlo simulation.
There are a number of settings in the Future Simulation tool that allow you to tailor the simulations to your specific requirements. For example, you can decide how bullish or bearish the market is likely to be in the future and plug those odds into the tool.
You can also select a sample period of time from which to draw historical simulation data. These periods can be designed to include or exclude strong bull markets or bear markets, or include or exclude extraordinary periods of the market such as the great bubble or the pandemic.
Future Simulations can be configured to test portfolio survival under a variety of different situations. In addition to modeling the future bullishness of the market, you can model in inflation, as well as the effect of regular portfolio contributions or withdrawals. For example, running a series of simulations can help show you the odds that you can retire without going bankrupt under various withdrawal scenarios.
Future Simulation has a number of other capabilities, such as the ability to show how rebalancing changes the volatility and return of a portfolio and how a blend of stocks, bonds, and cash is likely to perform.
The Future Simulations tool starts with you specifying how many “Simulation Years” to compute, which is how far out into the future you would like the tool to project your portfolio returns. You also specify how many Monte Carlo runs you want to make, which is just the number of simulations scenarios to run. Each simulation is independent. After all the simulations are run, the results are compiled together to show the range of outcomes achieved.
The next thing to specify is the Sample Period, which is the historical period of time from which Stock Rover will sample data from which to simulate future performance. This is explained in more detail below.
There are additional settings you can specify, such as those related to bull/bear sentiment, inflation, withdrawals, and more. These settings are all outlined in the Settings section below.
The best way to understand simulation is to run through an example. Let’s do a simulation that runs 5 years into the future. We will do the simulation 1000 times and we will use the last 5 years of historical data as a basis for return data when performing the simulation.
When we run this simulation, Stock Rover will perform 1000 individual independent simulations, where each simulation projects portfolio return over a future 5 year period. The simulation will actually operate on a monthly basis, generating random returns for each of the 60 months in the future period. The returns are based on monthly data from the historical period selected. There are also additional parameters that shape the results of the sampling run, such as the bullishness setting.
Note that 1000 simulations is a reasonable number of simulations to use. Increasing the number of simulations will result in more simulated portfolios from which Stock Rover can extrapolate, which is a good thing. But with that said, increasing the number significantly may not yield meaningfully different results, as 1000 samples will provide a very high statistical confidence level.
For each simulation, each of these steps are run for each of the 60 months:
In our example, Stock Rover is returning 1000 simulated portfolios that span the 60 months, where each of those months is populated based on the 8 steps outlined above. The monthly returns for each simulation are based on the weighted size of each ticker relative to the size of the portfolio.
Below we see 2 consecutive months from a simulation. These values were pulled from the Balance of Key Portfolios chart in the Future Simulations tool. Using the tooltip, we selected a specific portfolio and the months February 2027 and March 2027. February 2027 is showing a negative return based on the sample month of January 2015 and March 2027 is showing a positive return based on the sample month of December 2021.
Future Simulations is accessed by selecting Portfolio Tools in the Start menu and either selecting Future Simulations in the Start menu or selecting Future Simulations from the Portfolio Tools pane.
When you select Future Simulations, you are presented with two sections: the Navigation panel which lists the available and selected portfolios, and the Future Simulations panel which is where settings are configured and a simulation is run.
The Navigation panel displays your portfolios in a tree-like structure. This is where you select the portfolios you want to include in your future simulation. You select a portfolio by clicking on the checkbox next to the portfolio. You can include or exclude any combination of portfolios.
You can “tune” the simulations to account for bear/bull markets, inflation, rebalancing, withdrawals, and more.
Simulation Years: The number of years into the future to run each portfolio simulation, the default is 10 years.
Monte Carlo Runs: How many individual simulations to run, the default is 1000.
Sample Period: Is very important as it isn’t just the length of time that is important, but what happened during the sample period. For example, if you picked the 2007-2008 time period, then the modeling for the financials would reflect the financial crisis. If you picked the period from 2017-2021, it is a period where tech stocks have significantly outperformed the S&P 500. Including the year 2020 in the sample period would include the great crash and recovery from the pandemic. The sample period allows you to choose the historical market you want to use for the simulation.
Bear vs Bull (%): Over a 100-year history the stock market has risen 63% of months. During the period covered by Stock Rover (1/1/2007 to present), positive months averaged 74% (through the end of 2021). In recent years (through 2021) the market has been very bullish.
Recalibrating this will affect sampling, specifically the percentage of time a month from the historical period with a positive return for the S&P 500 is randomly selected, versus randomly selecting a month with a negative return for the S&P 500.
Inflation%: The inflation rate reduces the returns in order to show the results in inflation-adjusted amounts (i.e. in today’s dollars). Over approximately 100 years, the average annual inflation rate has been 3.24%. In simulations, the inflation rate is applied evenly across the entire period.
Asset Returns: Choose a proxy for the performance of any unpriceable holdings that are tracked as portfolio assets. For example, if you hold bonds, you might select an ETF like BND as a proxy. Leave this field blank to completely exclude Assets from simulations.
New Listings: Equities that started trading a few years ago will not have complete historical data points. This controls what value is used for periods when an actual return is not available. You can use Sector or S&P 500 returns.
Exclusions: Excluded equities are treated as if they were not part of your portfolio. This can be helpful for analyzing “What If” scenarios or for removing a volatile position that dominates future returns.
Frequency: Frequency of Rebalancing – Never, Quarterly, or Yearly
Method: Rebalancing will redistribute the portfolio funds to match their current allocations as a percent of the portfolio(s)
Amount: This amount will be subtracted from the portfolio using an equal percent per holding. Use a negative value to simulate portfolio contributions.
Frequency: The frequency for withdrawals (or contributions). Withdrawals occur at the end of the period, after gains or losses.
Start Date: Withdrawals will start the first Month, Quarter, or Year after this date, depending on the selected frequency.
Performance by Percentile is showing the likelihood of a specific return and end value from the simulation runs.
Assuming the number of Monte Carlo runs is set to 1000, the 1000 simulations are grouped into percentiles. Stock Rover shows the annual return for each of the percentiles. Each of the bars represents two percentiles, as there are 50 bars. When the default of 1000 simulations is used, each of the 50 bars shows the median of 20 simulations in the percentile range.
The chart is showing the distribution of returns that you can expect for your portfolio along with the average return. Going from left to right the chart is showing the worst to the best-case scenario. The tooltip shows the annualized return, as well as the start and end value of the portfolio. The chart below is showing an 8.7% return in the 31st percentile, which means this portfolio has a 69% chance of at least returning 8.7%. The horizontal lines represent the portfolio(s) start value and the average ending value.
Balances of Key Portfolios shows the actual performance of selected key portfolios from the full simulation run. The performance is shown on a monthly basis. You can see the volatility over time and how rebalancing (when selected) reduces that. Mouse over any point to see how the top holdings performed and the individual ticker returns. These seven sample simulation portfolios can give insight into how the hundreds (or even thousands) of simulations reach their ending values.
The Summary Statistics table shows the mean of all simulations. In addition, each of the seven Percentile columns is displaying a specific portfolio that fell within that percentile.
The Top Holdings table shows performance statistics for the largest holdings by starting value for the median-performing portfolio.
