It’s impossible to build an investment plan without estimating the return to stocks (as well as bonds and any alternative investments). One reason is that the estimate of returns determines your need to take risk—how high an allocation to equities you will need to reach your goal.
If your estimate is too high, it’s likely you won’t have sufficient assets to reach your retirement goal. If it’s too low, it could lead you to allocate more to equities, which means taking more risk than necessary. Alternatively, it could lead you to lower your goal, save more or plan on working longer.
Despite its importance, there is much disagreement about how to estimate stock returns. As is always the case, at Buckingham Strategic Wealth we rely on academic evidence.
Research On Expected Returns
Research on the expected equity premium, including Aswath Damodaran’s paper “Equity Risk Premiums (ERP): Determinants, Estimation and Implications,” has found that the best predictor of future equity returns is current valuations—whether using measures such as the earnings yield (E/P) derived from the Shiller CAPE 10 (or for that matter, the CAPE 7, 8 or 9) or the current E/P—not historical returns.
A review of the evidence led Damodaran to conclude: “Equity risk premiums can change quickly and by large amounts even in mature equity markets. Consequently, I have forsaken my practice of staying with a fixed equity risk premium for mature markets, and I now vary it year to year, and even on an intra-year basis, if conditions warrant.”
Damodaran’s approach is similar to the one we use at Buckingham Strategic Wealth. At the beginning of each year, we estimate returns for all asset classes and then use those estimates in our assumptions when running Monte Carlo simulations. For equities, while our methodology has changed somewhat over time, our forecast has always been based on current valuations, not historical returns.
Today we take the average of the estimate using the Shiller CAPE 10 E/P and the Gordon Growth Model (also known as the dividend discount model). For factors other than market beta (such as size and value), we have chosen to give the historical premium a one-third haircut—based on research showing some degradation of premiums, even risk-based ones, occurs post-publication. That, in turn, helps us determine the most appropriate asset allocation for clients.
We have been following this process since 2003. I thought it would be an interesting exercise to evaluate the accuracy of our forecasts. Before digging into the results, it’s important to understand that investors should treat all estimates of returns to risky assets only as the mean of what is potentially a wide dispersion of returns.
In other words, it’s unlikely you will earn the mean, estimated return. That is why we use Monte Carlo simulations to help us determine the most appropriate asset allocations—expected returns are not deterministic, but probabilistic. For example, let’s consider the results from Cliff Asness’ study on the Shiller CAPE 10’s ability to forecast future returns.
Asness On Shiller P/E
In a November 2012 paper, “An Old Friend: The Stock Market’s Shiller P/E,” Asness, of AQR Capital Management, found that the Shiller CAPE 10 provides valuable information. Specifically, he found 10-year-forward average real returns drop nearly monotonically as starting Shiller P/Es increase.
He also found that, as the starting Shiller CAPE 10 ratio increased, worst cases became worse, and best cases became weaker. Additionally, he found that, while the metric provided valuable insights, there were still very wide dispersions of returns. For instance: