In a recent blog post, we explored the risks associated with using a simple or straight-line return rate estimate for our portfolios when planning for retirement. We discovered that due to the variability and sequence of actual market returns, straight-line return estimates can significantly overstate the growth of a portfolio over time. This can lead to retirees overestimating their resources and putting their retirement security at risk. Naturally, we aim to prevent that.
The graph above is what you might see if looking at the straight line or linear return of X-percent applied to your portfolio over several years. The thing that should jump out at you is the smooth, upward-sloping trajectory of the graph. If you look at this graph alone, you might determine that these clients are in the clear to enjoy a successful, drama-free retirement. But our last blog post proved that real life can be more complicated than this.
So, if we know straight-line return assumptions can be problematic, what alternative method can we employ to plan more accurately? The answer is that we should use a forecasting method that can account for the randomness of future returns, which is inherent in capital markets.
That’s where Monte Carlo simulations come in.
I won’t go into the required calculations, but I want to provide some context for our discussion. A mathematician on the Manhattan Project initially developed the Monte Carlo simulation. It’s named Monte Carlo after the famous casino in Monaco because the outcomes of casino games, like roulette and dice, result from random trials of games of probability. The idea behind the method was to predict the likelihood of a specific outcome when the data input is random and uncertain.
In our case, we are trying to predict whether a portfolio of a specific size and allocation can support someone’s retirement income needs when we know the returns we will earn in future years are unpredictable.
We use a software program that applies Monte Carlo simulations to our clients’ portfolios to solve this. Those simulations run 1000 different trials using sets of possible future returns. Those data sets are based on historical averages and standard deviations from those averages of the asset classes in the portfolio. Future cash flows into and out of the portfolio are also accounted for. It’s a highly complex calculation and very time-consuming if attempted by hand. Increased computing power allows these simulations to be run frequently and quickly for individuals.
So what’s the answer!?
Interestingly, we don’t have a single projected dollar value for the ending portfolio assets. Instead, those 1000 trials each produce a result that forms a range of possible values.
All those results are then averaged together to arrive at a single number, which isn’t a dollar value but a Probability of Success. The Probability of Success is an ending portfolio value above $0 in all years.
Looking back at the graph, the dark blue line in the middle represents the average result of all 1000 simulations. The light blue shaded area represents a range of portfolio values where we get above-average results, and the yellow shaded area represents below-average results.
For this client, we see a vast range of possible outcomes. This is the function of a couple of things:
- The wide range of possible future returns
- The clients' age
Our sample client is only 45, and we plan for them to live until they are 95, so this simulation uses 1000 random return sets for 50 years. The longer we look out, the more variability is possible and the wider the range becomes.
Ultimately, we want to see a high probability of success when using Monte Carlo analysis, say 80% or better at this stage. If the time frame is shorter, we probably want a higher number. If we keep saving and spending as planned, we can likely make it through retirement without exhausting our resources, even with unpredictable future returns.
We all know that life is unpredictable, and no matter how hard we try, we can’t plan for everything. But thanks to increased computing power and the right tools, we are much more able to plan for variable returns.
Stay tuned! Next, we will discuss strategies for increasing success probabilities and narrowing the range of potential outcomes.