My third book, “Rational Investing in Irrational Times,” was published in 2002. The book presented and analyzed 52 common mistakes investors make. By the time the updated version of the book was published in 2011, as “Investment Mistakes Even Smart Investors Make and How to Avoid Them,” the list of mistakes had grown to 77 (a full catalog of those errors is provided at the end of this article). Unfortunately, if I were to again update the book, the list would be even longer.
One of the additions I would make involves a mistake that, while expected among some individual investors, is surprisingly common among many professional advisors. Specifically, it’s the error of using historical stock and bond returns as the best estimator of their future returns. The rationale for the behavior is typically that forecasts other than those using historical returns are nothing more than opinion. Historical returns are then employed as inputs for planning tools (such as Monte Carlo simulations) used to develop strategies for retirement, including the amount of savings required, a safe withdrawal rate for determining spending and the appropriate asset allocation.
To explain why using historical returns is an error, I’ll review the academic literature on the subject. Investment decisions should be based not on opinions, but on solid, peer-reviewed academic research. Before discussing the literature, the following example addresses the issue of whether using historical returns is even a logical approach. Unfortunately, as you’ll see, the use of historical returns in retirement planning is highly likely to lead to failure (meaning investors outlive their assets) because, for both stocks and bonds, it leads to a forecast that’s extremely unlikely to occur. The reason is that stock valuations are much higher than they have been in the past, and bond yields are much lower.
From 1926 through 1948, the S&P 500 produced an annualized nominal return of 6.3% and an annualized real (inflation-adjusted) return of 5%. Relying on the historical record would lead you to forecast the same returns going forward. From 1949 through 1999, the S&P 500 returned 13.7% in nominal terms and 9.8% in real terms. The return over the full period, from 1926 through 1999, rose to 11.3% in nominal terms and 8.1% in real terms. Let’s see if it makes any intuitive sense to increase your forecast from 6.3% to 11.3% in nominal terms and from 5% to 8.1% in real terms because nominal returns were 13.7% and real returns were 9.8% over the prior 51-year period.
On January 1, 1949, the price-to-earnings ratio (P/E) of the S&P 500 was 6.6. That produces an earnings yield (E/P) of 15.2%. The Shiller CAPE 10 stood at 10.1, producing an E/P of 9.9%. On January 1, 2000, the P/E ratio stood at 29.0, producing an E/P of 3.4%, and the Shiller CAPE 10 stood at an even higher 34.4, producing an E/P of 2.9%. Does it make sense to forecast a nominal return of 6.3% and a real return of 5% when the E/P is 15.2%, and also to forecast a nominal return of 11.3% and a real return of 8.1% when the E/P is 3.4% and the Shiller E/P is just 2.9%? The answer is an obvious: no. Yet, that’s what you do if you rely on historical returns.
Common sense, which is all-too-uncommon, should lead you to conclude that this isn’t logical. Higher prices paid for the same dollar of earnings should lead one to forecast lower, not higher, returns. And the academic literature demonstrates current valuations do in fact provide us with the best estimate of future returns. In other words, high valuations don’t forecast high future growth in earnings (which would be required to generate a high return). Instead, the research shows high valuations predict low future returns. And from 2000 through 2015, the S&P 500 went on to return just 4.1% in nominal terms and 1.9% in real terms. It’s easy to see the damage that could be done if one built a retirement plan that relied on a nominal return forecast of 11.3% and a real return forecast of 8.1%.
The following is an even simpler and clearer explanation of why using historical returns is illogical. From 1926 through 2015, the return on long-term (20-year) Treasury bonds was 5.6%. As I write this, the yield on the 20-year Treasury is just 1.7%. Which is the best estimate of future bond returns, the historical return of 5.6% or the current yield of 1.7%? Obviously, the answer is that the current yield is the perfect predictor of the return over the next 20 years, at least in nominal terms.
The literature on expected returns
Eugene Fama (winner of the Nobel Prize) and Kenneth French are two of the most respected names in finance. In their 2002 paper, “The Equity Premium,” which appeared in The Journal of Finance, they estimated the equity premium by using dividend and earnings growth rates to measure the expected rate of capital gain. From the abstract: “Our estimates for 1951 to 2000, 2.55% and 4.32%, are much lower than the equity premium produced by the average stock return, 7.43%. Our evidence suggests that the high average return for 1951 to 2000 is due to a decline in discount rates that produces a large unexpected capital gain. Our main conclusion is that the average stock return of the last half-century is a lot higher than expected.” Fama and French were essentially saying that using the historical return gave you a misleading estimate of future returns.
In the 2013 edition of his paper, “Equity Risk Premiums (ERP): Determinants, Estimation and Implications,” Aswath Damodaran, a professor of finance at NYU’s Stern School of Business, examined three approaches to estimating the returns: the historical approach, a survey approach and a current valuation approach.
Damodaran explained the problem with the historical approach as follows: “When stock prices enter an extended phase of upward (downward) movement, the historical risk premium will climb (drop) to reflect past returns. Implied premiums will tend to move in the opposite direction, since higher (lower) stock prices generally translate into lower (higher) premiums. In 1999, for instance, after the technology induced stock price boom of the 1990s, the implied premium was 2% but the historical risk premium was almost 6%.”
He then explained the problem with using the survey approach: “Survey premiums reflect historical data more than expectations. When stocks are going up, investors tend to become more optimistic about future returns and survey premiums reflect this optimism. In fact, the evidence that human beings overweight recent history (when making judgments) and overreact to information can lead to survey premiums overshooting historical premiums in both good and bad times. In good times, survey premiums are even higher than historical premiums, which, in turn, are higher than implied premiums; in bad times, the reverse occurs.”
Finally, he provided this insight on the valuation approach: “When the fundamentals of a market change, either because the economy becomes more volatile or investors get more risk averse, historical risk premiums will not change but implied premiums will. Shocks to the market are likely to cause the two numbers to deviate. After the terrorist attack on the World Trade Center in September 2001, for instance, implied equity risk premiums jumped almost 0.50% but historical premiums were unchanged (at least until the next update).”
To determine the “right” approach, Damodaran studied the predictive powers of each, looking at the returns over the following 10 years. The study covered the period from 1960 through 2012. He found that the correlation of the current implied premium with returns over the next 10 years was 0.43. On the other hand, the correlation of the historical premium with returns over the next 10 years was in the wrong direction, negative 0.48.
He concluded: “The implied equity risk premium at the end of the prior period was the best predictor of the implied equity risk premium in the next period, whereas historical risk premiums did worst.” Damodaran also writes: “If you believe that markets are efficient in the aggregate, or at least that you cannot forecast the direction of overall market movements, the current implied equity premium is the most logical choice, since it is estimated from the current level of the index…. Historical risk premiums are very poor predictors of both short-term movements in implied premiums or long-term returns on stocks.”
The 2014 paper, “A History of the Equity Risk Premium and its Estimation,” by Basil Copeland of Chesapeake Regulatory Consultants Inc., studied this issue and came to the same conclusions as Fama and French and Damodaran. From the abstract: “There has been general acceptance, a consensus if you will, that historical equity return premia overstate what was anticipated or expected and that a large component of the historical equity return premium constitutes unanticipated capital gains.” He, too, concludes that current valuations are the best predictor of future returns
Vanguard’s research team studied the issue of expected returns in their October 2012 paper, “Forecasting Stock Returns: What Signals Matter, and What Do They Say Now?” The firm’s researchers found that “many commonly cited signals have had very weak and erratic correlations with actual subsequent returns, even at long investment horizons. These poor predictors include trailing values for dividend yields and economic growth, the difference between the stock market’s earnings yield and Treasury bond yields (the so-called Fed Model), profit margins, and past stock returns.”
Consistent with the other research cited, Vanguard found that P/E ratios explain about 40% of the time variation in net-of-inflation returns. Their results were similar whether or not trailing earnings are smoothed or cyclically adjusted (as is done in Robert Shiller’s CAPE 10 ratio). As an interesting aside, Shiller was awarded the Nobel Prize in the same year as Fama.
And finally, we’ll look at research by Cliff Asness of AQR Capital. In a November 2012 paper, “An Old Friend: The Stock Market’s Shiller P/E,” Asness found that 10-year forward average real returns fell nearly monotonically as starting Shiller CAPE 10 P/E ratios increase. He also found that as the starting Shiller CAPE 10 ratio increased, worst cases got worse and best cases get weaker. And while the metric provided valuable insights, he found there were still very wide dispersions of returns. For example:
- When the Shiller CAPE 10 was below 9.6, 10-year forward real returns averaged 10.3%. In relative terms, that is more than 50% above the historical average of 6.8% (9.8% nominal return less 3.0% inflation). The best 10-year real return was 17.5%. The worst was still a pretty good 4.8% real return, just 2.0% below the average, and 29% below it in relative terms. The dispersion between the best and worst outcomes was a 12.7% difference in real returns.
- When the Shiller CAPE 10 was between 15.7 and 17.3 (about its average of 16.5), the 10-year forward real return averaged 5.6%. The best and worst 10-year forward returns were 15.1% and 2.3%, respectively. The dispersion between the best and worst outcomes was a 12.8% difference in real returns.
- When the Shiller CAPE 10 was between 21.1 and 25.1, the 10-year forward real return averaged just 0.9%. The best 10-year forward real return was still 8.3%, above the historical average of 6.8. However, the worst 10-year forward real return was now -4.4%. The dispersion between the best and worst outcomes was a difference of 12.7% in real terms.
- When the Shiller CAPE 10 was above 25.1, the real return over the following 10 years averaged just 0.5% — virtually the same as the long-term real return on the risk-free benchmark, one-month Treasury bills. The best 10-year real return was 6.3%, just 0.5% below the historical average. But, the worst 10-year real return was now -6.1%. The dispersion between the best and worst outcomes was a difference of 12.4% in real terms.
Similarly, the research shows that today’s bond yields are a better predictor of future returns than are historical returns.
The best tool we have for forecasting equity returns are current valuations. With that in mind, the Shiller CAPE 10 earnings yields as of June 30, 2016 are 4.1% for the United States, 6.6% for developed markets and 8.2% for emerging markets. To forecast expected returns, we need to make an adjustment to account for the fact that the Shiller CAPE 10 uses a 10-year average of earnings (adjusted for inflation) and real earnings grow about 2% a year. Thus, we need to multiply the earnings yield by 1.1 [1 + (5 x .02)]. That results in expected real returns to U.S. stocks of 4.5%, to developed market stocks of 7.3% and to emerging market stocks of 9.0%. To get an estimate of nominal returns, you can add the current spread between the 10-year nominal bond and the 10-year TIPS, which is currently about 1.5%.
What can we learn from the above data? 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, and they matter a lot. We also know that they are far more accurate than historical returns at predicting returns over the next decade. The correlation between historical returns and the next 10-year returns even has the wrong (negative) sign. We know that higher starting values mean future expected returns are lower, and vice versa. However, we also know that there’s still a wide dispersion of potential outcomes for which we must prepare when developing an investment plan.
Thus, if you have been using historical returns as your estimate of future returns, it’s time to correct that error to prevent the damage this error will cause.
I have one more important issue to cover: How to address issues surrounding the difficulty of forecasting returns. One of the best ways to address probabilistic forecasts is to use a Monte Carlo (MC) simulator. MC simulations require a set of assumptions regarding time horizon, initial investment, asset allocation, withdrawals, savings, retirement income, rate of inflation, correlation among the different asset classes and — very importantly — return distributions of the portfolio.
Monte Carlo simulations
In MC simulation programs, the growth of an investment portfolio is determined by two important inputs: portfolio average expected return and portfolio volatility, represented by the standard deviation measure.
MC simulations are not used to predict returns. The forecasted returns are one of the inputs.
Based on these two inputs, the MC simulation program generates a sequence of random returns from which one return is used in each year of the simulation. This process is repeated thousands of times to calculate the likelihood of possible outcomes and their potential distributions. This allows you to look at alternative scenarios, including ones where returns will be well below expectations (which Asness showed is quite possible). Being forewarned about the potential for such events allows you to prepare for the various possible outcomes. That includes putting in place a contingency plan of action (a “Plan B") to be implemented if a major unexpected event (a “black swan”) appears. The plan should detail what actions to take 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.
MC simulations also provide another important benefit: They allow investors to view the outcomes of various strategies and how marginal changes in asset allocations, savings rates and withdrawal rates 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 director of research for The BAM ALLIANCE, a community of more than 150 independent registered investment advisors throughout the country.
The following is the list of the 77 mistakes covered in my book, “Investment Mistakes Even Smart Investors Make and How to Avoid Them”:
1: Are you overconfident of your skills?
2: Do you project recent trends indefinitely into the future?
3: Do you believe events are more predictable after the fact than before?
4: Do you extrapolate from small samples and trust your intuition?
5: Do you let your ego dominate the decision-making process?
6: Do you allow yourself to be influenced by a herd mentality?
7: Do you confuse skill and luck?
8: Do you avoid passive investing because you sense a loss of control?
9: Do you avoid admitting your investment mistakes?
10: Do you pay attention to the “experts”?
11: Do you let the price paid affect your decision to continue to hold an asset?
12: Are you subject to the fallacy of the “hot streak”?
13: Do you confuse the familiar with the safe?
14: Do you believe you are playing with the house’s money?
15: Do you let friendships influence your choice of investment advisors?
16: Do you fail to see the poison inside the shiny apple?
17: Do you confuse information with knowledge?
18: Do you believe your fortune is in the stars?
19: Do you rely on misleading information?
20: Do you only consider the operating expense ratio when selecting a mutual fund?
21: Do you fail to consider the costs of an investment strategy?
22: Do you confuse great companies with high-return investments?
23: Do you understand how the price paid affects returns?
24: Do you believe more heads are better than one?
25: Do you believe active managers will protect you from bear markets?
26: Do you fail to compare your funds to proper benchmarks?
27: Do you focus on pretax returns?
28: Do you rely on a fund’s descriptive name when making purchase decisions?
29: Do you believe active management is a winner’s game in inefficient markets?
30: Do you understand the tyranny of the efficiency of the market?
31: Do you believe hedge fund managers deliver superior performance?
32: Are you subject to the money illusion?
33: Do you believe demographics are destiny?
34: Do you follow a prudent process when choosing a financial advisory firm?
35: Do you fail to understand the arithmetic of active management?
36: Do you understand bear markets are a necessary evil?
37: Do you treat the highly likely as certain and the highly unlikely as impossible?
38: Do you take risks not worth taking?
39: Do you confuse before-the-fact strategy with after-the-fact outcome?
40: Do you believe stocks are risky only if your horizon is short?
41: Do you try to succeed even when success is highly unlikely?
42: Do you understand the importance of saving early in life?
43: Do you properly evaluate the real cost of an expenditure?
44: Do you believe diversification is the right strategy only if the investment horizon is long?
45: Do you believe this time it’s different?
46: Do you fail to tax manage your portfolio throughout the year?
47: Do you let taxes dominate your decisions?
48: Do you confuse speculating with investing?
49: Do you try to time the market?
50: Do you rely on market gurus?
51: Do you use margin to try to enhance investment returns?
52: Do you understand there is only one way to be a buy-and-hold investor?
53: Do you work with commission-based advisors?
54: Do you spend too much time managing your portfolio?
55: Do you prepare your heirs?
56: Did you begin your investment journey without a road map?
57: Do you understand the nature of risk?
58: Do you consider investments in isolation?
59: Do you have too many eggs in one basket?
60: Do you underestimate the number of stocks needed to build a diversified portfolio?
61: Do you believe diversification is determined by the number of securities held?
62: Do you believe focused funds outperform?
63: Do you understand that in times of crisis the correlations of all risky assets rises?
64: Do you fail to consider your labor capital when constructing your portfolio?
65: Do you believe the investment world is flat?
66: Do you confuse indexing with the exclusive use of an S&P 500 fund?
67: Do you consider your home as your exposure to real estate?
68: Do you fail to see the risk in high-yield investments?
69: Do you purchase products meant to be sold, not bought?
70: Do you chase the IPO dream?
71: Do you understand that you can be too conservative?
72: Is your withdrawal assumption rate in retirement too aggressive?
73: Do you hold assets in the wrong location?
74: Do you believe all passively managed funds are created equal?
75: Do you trust but fail to verify?
76: Do you have a Plan B?
77: Do you keep repeating the same mistakes?
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