Past returns are often negatively correlated with those in the future, and not the opposite.
Earlier this week, we discussed some of the academic literature surrounding historical versus current valuations as a metric for forecasting future returns. We learned that because there’s so much variation over time in the equity risk premium, there isn’t any methodology that will produce highly accurate forecasts of stock returns. Stocks are risky investments, no matter the horizon.
However, we do know that starting valuations clearly matter—a whole lot. We also know they are a far more accurate predictor of returns over the next decade than historical returns. The correlation between historical returns and the next 10 years of actual returns even had the wrong (negative) sign.
Furthermore, we know higher starting values mean future expected returns are lower, and vice versa. But we also know there’s still a wide dispersion of potential outcomes for which we must prepare when developing an investment plan.
So how should you address the problems surrounding the difficulty of forecasting returns? We think the best way to deal with the issue of probabilistic forecasts is to use a Monte Carlo simulator.
Monte Carlo simulations require a set of assumptions regarding time horizon, initial investment, asset allocation, withdrawals, savings, retirement income, rate of inflation, correlation among different asset classes and—very importantly—return distributions of the portfolio.
Monte Carlo Simulations
In Monte Carlo simulation programs, the growth of an investment portfolio is determined by two important inputs: portfolio average expected return and portfolio volatility, which is represented by the standard deviation measure. It’s important to note that the Monte Carlo simulation is not used to predict returns. The forecasted returns are one of the inputs.
Based on these two inputs, the Monte Carlo simulation program generates a sequence of random returns from which one result is applied in each year of the simulation’s time frame. The process is repeated thousands of times to calculate the likelihood of possible outcomes and their potential distributions.
It allows an investor to look at a number of alternative scenarios, including ones where returns will be well below expectations. The evidence we discussed earlier this week has shown such outcomes are quite possible.
Being forewarned about the potential for adverse events allows investors to prepare for the various possible outcomes. That includes putting in place a contingency plan of action (or “plan B”) to be implemented if a “black swan”—a major unexpected event—appears.
The plan should detail what actions will be taken if financial assets fall to such a degree that the investor runs an unacceptably high level of risk of failure. For individuals, those actions might include remaining in, or returning to, the work force, reducing current spending, reducing the financial goal, selling a home and/or moving to a location with a lower cost of living.
Monte Carlo simulations also provide another important benefit. They allow investors to view the outcomes of various strategies and examine how marginal adjustments in asset allocation, savings rate and withdrawal rate change the odds of these outcomes. Looking at various alternatives will help you determine the right asset allocation for your unique situation.
Larry Swedroe is the director of research for the BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.