Every investment decision has consequences; only the future will determine if it is a good decision. Due to the uncertainty of the markets and while many try to forecast the future, they have yet to achieve success consistently or reliably.
Often investors assume the future will resemble the recent past when making investment decisions. However, we know that relying on the past is deceptive, and the SEC requires us to tell investors not to base their expectations of future results on past performance. It is the law for us to tell you this, and it is also excellent advice.
So how do thoughtful people make decisions in the face of uncertainty? They ensure they are very clear with their desired outcome; Nobel-prize-winning psychologist Daniel Kahneman has said that overconfidence is the bias he’d eliminate first if he had a magic wand. It’s ubiquitous among men, the wealthy, and even experts. Overconfidence is not a universal phenomenon — it depends on factors including culture and personality — but the chances are that you’re more confident about each step of the decision-making process than you ought to be. You need to understand the risks and obstacles to achieving the outcome and consider the probability of their achievement (according to the research, this is the best place to start when making a prediction). Also, calculate the odds of their failure and calculate all the possibilities to ensure they fully understand their decisions’ best-case and worst-case scenarios.
All too often, our subconscious plays out one of these scenarios as our imagination runs wild. Optimists will focus more on the best outcome, and pessimists will only see the worst. To make a good decision, we need to seek both perspectives, ensuring we are prepared to weather either extreme while targeting the most likely outcome.
Just like business owners and CEOs planning for different business decision scenarios, including a base case (most likely targeted outcome), the best and worst cases, we also need to prepare. Your base-case scenario is the average financial return that will most likely happen if the markets behave as expected. Your best-case scenario is the best possible financial returns if the markets perform exceptionally well beyond expectations. Your worst-case scenario is the most negative potential financial returns if the markets behave exceptionally poorly.
Financial planners and investment managers use a modeling algorithm called Monte Carlo Simulation to calculate the probability of outcomes.
What is Monte Carlo Simulation?
When faced with significant uncertainty in a forecast, the Monte Carlo Simulation uses multiple random values to predict the breadth of uncertainty (the range between worse and best case).
Monte Carlo simulations have applications in business and finance and other industries such as telecoms, insurance, meteorology, astronomy, and particle physics, wherever outcomes are random and uncertain.
A scientist introduced the fundamental principle in the 18th century, further developing it over time. In the 1950s, mathematicians used simulations to investigate radiation shielding and the distance that neutrons would likely travel through various materials while developing the Hydrogen bomb. The finance profession began using it more recently in the 90s.
Before the ’90s, financial planning software created a “static” analysis, and the assumption was unrealistic. One of the problems with assuming a static return is that it does not account for sequence risk. Returns are never identical every year but instead of a series of good years, bad years, and many combinations of returns. For example, consider two extreme hypothetical performance scenarios year by year for 30 years.
Portfolio A experiences negative returns for the first few years but rebounds with positive numbers in later years. And Portfolio B experiences the opposite, with positive returns in the early years and negative returns in the later ones.
But this doesn’t give you the whole picture. To better understand the impact of cash flow on your retirement portfolio, let’s look at how each of these portfolios might perform given three different and independent scenarios:
- With no cash flows, the order of your returns doesn’t matter.
It’s the multiplication principle: 2×4 = 4×2
Think of it this way:
- A $100 investment goes up 10% and reaches $110, and if it then loses 10%, the value drops to …$99
- A $100 investment goes down 10% and drops to $90. If it then gains 10%, the value increases to …$99
It all compounds the same way.
- With cash inflows, it’s better to have the bad returns first, as they allow you to buy more shares at a lower price.
Downside volatility helps in accumulation by enhancing the effect of compounding.
This may look familiar to you. It’s the most basic tenet of investing we teach to every 401(k) participant. It’s dollar-cost averaging
- When you are taking withdrawals or distributions, the opposite is true.
Good returns at the beginning help preserve and grow your assets; bad returns up front eat away at your principal, and you can never get it back.
Although these three scenarios reflect unrealistic extremes, they demonstrate the general concept of how and when market returns impact your portfolio during retirement accumulation and distribution phases.
Even if there is no consideration for inflows or outflows, sequential risk can affect a portfolio over the short to mid-term.
Monte Carlo analysis is a superior forecasting approach to the standard “straight-line” projection that someone might create in an excel spreadsheet because it considers average returns and a range of potentially volatile returns. However, with the additional capability to illustrate a range of volatile returns – potentially across multiple investments or asset classes – comes a more significant burden to craft appropriate investment assumptions for the Monte Carlo analysis.
Three core points of data are necessary for each investment: expected return, volatility, and correlation. While all of these assumptions are challenging, correlations are particularly complex since they measure the relationship between each investment and every other investment included in the portfolio. And with the number of asset classes we use in our portfolios, this means that with 13 asset classes, we need to formulate 78 correlation relationships!
Calculations become even more complicated since correlations are not static; they change over time. Unfortunately, there’s no commercially available Monte Carlo software that models regime-based retirement projections where the correlations change over time, so we partnered with another firm to program these changing relationships in our proprietary software.
The video that accompanies this blog is shown below. You can also watch this and many other videos on our YouTube channel: