In July of this year NASA is planning to launch the next mission in their ongoing series of journeys to Mars. Named Perseverance, the robotic explorer is built on the successful design of the Curiosity rover that has been wandering the Red Planet since 2012. A machine capable of long-term exploration of another planet is a true engineering marvel, but controlling it remotely is not even the hard part. After decades of practice, even the requirements of launching devices out of the pull of Earth’s gravity are pretty well-known. But do you know what is still a real challenge even for the best rocket scientists?
Sticking the landing.
Imagine working for years to design and build an extremely expensive interplanetary robot. You pour countless long days and nights into perfecting every little detail and then launch it through millions of miles of empty space. After about 7 months, your prized work full of delicate scientific instruments is flying directly towards a huge rocky planet at Mach 1.7 like a speeding bullet.
At seemingly the last moment, the probe deploys a parachute. But not just any parachute. This one was specifically selected for this very moment to slow a payload the weight of a small car from a blistering 1300 mph to a more manageable 200 mph before landing thrusters are able to take over. And it’s just as risky as it sounds. Perfectly unfurling in an instant to save the day after being packed away for months is no guarantee. Surviving the initial force of 17,000 lbs of drag without ripping is also incredibly difficult. And doing it all in an alien atmosphere like nothing any other normal parachute has experienced on Earth makes it even harder. So many things can go wrong leaving Perseverance nothing more than the newest crater on a dead world.
Knowing the importance of the task and the severity of the situation, how is a wise engineer supposed to guarantee success? They really can’t. At some point all they have is faith that it will work the way they planned.
But here’s the thing — that faith is not unfounded. While no situation is ever completely in your control, engineers naturally understand a universal fact of life that is equally at home in a Sunday church sermon as in a NASA planning meeting.
A faith not tested cannot be trusted.
You see, lost in all of the future press releases featuring beautiful images and cheering faces on landing day is the insane amount of work that got them there. You’ll likely never get a glimpse of that parachute that made the difference between a successful landing and a pile of melted metal on the planet surface. And you also won’t see the years of testing that went into that parachute long before Perseverance ever took off. While engineers may have borrowed designs from previous successful landings, they did not simply rely on blind faith that it will work the same way this time. They tested that parachute more than you can ever imagine, from controlled wind tunnel tests in the title image to harrowing high-altitude deployment tests at the very edge of space.
That balance between trusting the designs of those who came before us while testing them for ourselves in extreme real-world conditions is a lesson I think a lot of investors can also use when selecting their own financial parachutes for uncertain market futures. Investing in risky markets always requires some level of faith, and until your faith is tested you really have no idea if your choice was truly wise.
Often those tests come the hard way. Just randomly picking an idea out of a hat, imagine if markets unexpectedly crater due to a pandemic that triggers a global depression. Like an engineer watching their life’s work race towards Mars, some of us are left watching our life savings speed towards impending doom at an alarming speed. How were we supposed to know when selecting our parachute long ago that it would function in these conditions?
Well luckily there are ways to simulate those extreme conditions while we’re still in the safe confines of the planning stages. Some are definitely better than others, so let’s put on our engineering hats and talk about four proven techniques of effective portfolio testing.
Use a sufficient sample size
When testing things like parachutes, one of the biggest rookie mistakes that young engineers make is to put too much faith in the first positive result. Sure, you should feel good about your design performing well in a wind tunnel. But that controlled environment is nothing like the wild, unpredictable experience of a parachute opening at speed on an alien world. That’s why NASA engineers also went to the effort of conducting deployment tests high in the Earth’s atmosphere where conditions are more similar to Martian skies. And they did it over and over again, putting the design through its paces testing everything from the tensile strength of the fabric to the packing method of the cables. One test is never enough to accurately probe every possible outcome of a parachute in the real world.
Likewise, too many investors new to backtesting fall into the trap of running a simulation over a single timeframe and stopping as soon as they like the result. Sometimes the automated tools crop the results where a specific fund first has data, and other times unscrupulous portfolio salesmen intentionally cherry-pick timeframes that particularly favor their preferred portfolio. Smart investors may even think they’re addressing this by modeling as far back in history as possible without realizing just how important the start and end dates are to the final numbers. The real world is messy, and more data isn’t necessarily better if you don’t account for how pure luck and sequence of returns jumble the outcomes for investors starting at different times.
Because of that inherent unpredictability, if you claim success after only one test the chances of your portfolio letting you down in the first true flight experience are painfully high. But while the results may be disappointing, the numbers weren’t wrong! You were. Markets change all the time, and by placing too much faith in the results of a single investing timeframe you set yourself up for failure.
The solution to that is to run more experiments. Ideally, you should test your portfolio many times over every economic condition and sequence of returns you can get your hands on. While that sounds like an impossibly time-consuming process requiring more data than you know how to find, Portfolio Charts is specifically designed to make it really easy. Each chart models every possible annual start date since 1970 simultaneously and maps the real-world results in a single image. For example, here’s the full range of inflation-adjusted Portfolio Growth paths of a total US stock market index fund when investing $10k a year. By running 50 historically accurate tests at a time, you can quickly get a feel for the best outcomes, the worst outcomes, and everything in between.
Visualize the stress
Collecting data really isn’t the most challenging part about being an engineer. You can assign teams of interns or robots to run repetitive tests all day. The hard part is knowing how to process all of that data in a format that is not only accurate and repeatable but also actionable. A spreadsheet with millions of raw data points may sound impressive but is generally useless by itself.
A good example is stress analysis. Thanks to the miracle of modern computers, engineers are able to run incredibly sophisticated simulations on three-dimensional models and capture stress data for millions of individual points throughout the part. But having all of that data is useless without an effective means of interpreting the results. So stress analysis software is designed to visualize the data in a way human brains can process. Here’s an example showing the various stress levels in a parachute canopy.
The forces are color coded, where blue areas have the lowest stress and red areas have the highest stress. Since parts fail at the points of highest stress, engineers can look for the red spots to know where to reinforce the material and check the strength of the seams. Instead of drowning people in numbers, it presents the data in a manner that intuitively directs them to the right areas for further improvement.
Investing research is really similar in how it’s easy to get lost in the numbers. And when you’re doing your job of getting as much data as possible it’s even harder to keep things straight. So in the same way that stress analysis software visualizes mechanical stress, tools like the Heat Map visualize financial stress. See the similarity?
Borrowing a few design cues from stress analysis software, the blue squares represent the best results and the red squares represent the worst results. Making more than a thousand unique investing timeframes easy to visually process helps you quickly identify the pain points in the 1970’s and 2000’s. And by reinforcing the portfolio with assets that worked well in those conditions, you can intuitively design an investing plan to safely weather any financial storm.
Identify the breaking point
In engineering it’s often helpful to quantify boundaries. By pushing a design beyond its physical limits one can measure the forces guaranteed to cause catastrophic failure. While intentionally destroying a good part is an expensive experiment, the results are critical for understanding how far the next part can go. No matter how much thought you put into a design, everything has a breaking point.
The thing about portfolios is that they’re usually not the first to fail — we are. Like a fighter pilot who passes out from too many Gs in a turn long before the plane will even notice, investors all have a limit that they can no longer handle before capitulating.
Sometimes that limit is emotional, such as when the floor falls out of your portfolio and the drawdowns are far deeper or longer than you thought you were signing up for. No matter how strong you think you are ahead of time, when the pain exceeds your personal threshold the reaction is swift and completely predictable. You’re going to sell at a loss.
To combat that, I find it useful to study every historical drawdown. Not only does it prepare you for the ride, but it also puts any immediate uncomfortable situations in the proper historical perspective. More often than not, the losses driving you to the exits today are actually quite tame compared to the investing experience of people who held the same portfolio before you started paying attention. Here’s the Drawdowns chart for the total US stock market. Are you prepared to lose half your money or wait 13 years to break even?
Hardening your feelings to ignore every loss won’t necessarily save you, though, as not every failure point is emotional. Sometimes reality hits you in the face in a way you never expected. For example, some retirees like to talk about how variable withdrawal strategies guarantee that a portfolio will never run out of money. While that’s technically true, that’s only one way a retirement portfolio can fail! Another is when the budget allowed by the spending strategy falls below your minimum needs to feed your family and keep a roof over your head.
That’s why tools like the Retirement Spending chart that allow for a fixed spending floor are so important. Your favorite rules-based withdrawal system may be brilliant at cutting spending to safely prolong retirement, but that’s not very useful when you realize your mortgage lender expects you to pay the same amount and there’s no more room to cut. Witnessing that parachute rip in real time is not an experience you want to have, so checking for that possibility with historical data is a lifesaver.
Stay away from the edge
No matter how many tests we run there are no guarantees in life. Some things are really hard to accurately model and others are simply impossible to predict. Engineers know this well. In life-or-death things like bridges, planes, and parachutes where failure really isn’t an option, it’s important to bring sober honesty to any analysis and account for the possibility of uncertainty.
To do their very best to make sure that a particular design is safe, engineers use an idea called the factor of safety. They estimate the most severe forces that the item might be expected to experience in a crazy situation, and they design it to withstand some multiple of that worst case scenario. The difference between the design specification and the extreme load defines the factor of safety. For example, the Perseverance parachute has a factor of safety of 1.4, which means it is designed to withstand forces 40% higher than anything expected during the mission. While there are no guarantees, that’s how responsible engineers cover their bases.
Unfortunately very few people think that way in investing. Most people either default to average returns that completely mask the worst case scenarios or fixate on maximization ideas with overly optimistic expectations. If an engineer did that they’d eventually lose their job when the design inevitably crashes and burns. But in finance the same reckless behavior is often celebrated as state of the art! Luckily you don’t have to fall into that trap.
One example of using a factor of safety in investing involves planning for uncertain future portfolio growth. Instead of depending solely on long-term averages that hide the worst-case scenarios, tools like the Target Accuracy chart include two more pessimistic possibilities — the minimum return and the baseline return. Remember, these outcomes happened before and they can happen again. Plan your savings rate so that your portfolio exceeds your needs even in these pessimistic scenarios, and success over your own investing timeframe is all but guaranteed.
Another good example of using a factor of safety in your own planning applies to withdrawal rates. While many people are tempted to tread as close to the edge as possible to maximize spending, a more conservative approach might be to build in a factor of safety of 20%. So if the Withdrawal Rates calculations say that your portfolio survived a worst-case withdrawal rate of 5% since 1970, nothing requires you to do that! Plan for a more conservative 4.2%, and that leaves you an extra 20% of headroom to account for the unexpected.
Stress, smarts, and the power of effective backtesting
While all of that is interesting, do you really need an engineering degree to be a successful investor? Of course not! Not everyone is willing or able to design parachutes for NASA. And make no mistake — just because you’re an engineer and great with numbers doesn’t automatically make you good with money. But as a student of both fields, I’ve personally noticed that some guiding engineering principles are particularly helpful when it comes to designing financial plans in an uncertain world. To recap:
- Use a sufficient sample size
- Visualize the stress
- Find the breaking point
- Stay away from the edge
It’s no accident that the Portfolio Charts tools are designed with the first two points in mind. Presenting mountains of historical data using intuitive visualizations is my way of bringing helpful engineering ideas to the financial field. And even though the math powering the charts can get pretty advanced behind the scenes, investing complexity is definitely not the objective. In fact, the reason I share these tools is to hopefully make sophisticated portfolio analysis so simple that everyday DIY investors feel empowered to design, test, and manage their own portfolios.
So when picking your own portfolio parachute, don’t simply select one based on a recommendation and trust it with blind faith. Be critical. Test it like crazy and put it through its paces in both good times and bad. Make sure your total financial plan is built to meet your needs even in when markets don’t cooperate. And rather than simply hoping for the best, use that knowledge to invest with confidence.
When it’s your turn to jump out of the plane into turbulent markets with your chosen portfolio parachute, will you be afraid? Or will your faith be built on a strong foundation? You have the time, and you have the tools. Take charge of your testing plan and the end result will be one great ride.
Have you successfully tested your faith in your parachute?