Possible outcomes may well be below, and even well below, the expected rate of 7 percent. Please note that the numbers in this example are for illustration purposes only, and are not based on any actual distribution.
Investors should design investment plans that incorporate the potential for years, and perhaps even longer periods, of negative returns. They should also consider the possibility of so-called black swans, or major market-moving events considered so unlikely that investors make the mistake of confusing the improbable with the impossible.
Distributions Are Estimated, And Not Known
This raises the issue of another prevalent mistake made by investors. Even among those who view expected stock returns to stocks correctly, many make the additional mistake of treating the potential distribution of outcomes as if they are known, when they can only be estimated.
A good example of this problem arises when advisors discuss the outcomes of Monte Carlo simulations with clients. I often hear statements such as, “The odds of success of this portfolio are 85 percent under this withdrawal assumption.”
Unfortunately, we don’t know the odds—we can only estimate them. And that’s an important distinction because we know that shocks to the uncertainty premium help explain asset price fluctuations, business cycles and financial crises. Thus, the advisor’s statement should be something similar to, “Based on our current assumptions, we estimate that the odds of success are ….”
Anna Orlik and Laura Veldkamp—authors of the 2014 paper, “Understanding Uncertainty Shocks and the Role of Black Swans”—show how shocks (often caused by black swan events) lead to dramatic changes in our estimates of the potential future dispersion of returns.
The authors note that: “[R]eal people do not know what the true distribution of economic outcomes is, when it changes, or by how much. They observe economic information and, conditional on that information, estimate the probabilities of alternative outcomes. Much of their uncertainty comes from not knowing if their estimates are correct.”