Managing Expectations & Expected Returns

One of the most influential lines of research in financial economics over the past generation has been the “discovery” that asset returns are predictable. The predictability, as documented in the literature, isn’t much help to day traders. Instead, numerous studies lay out the empirical case for arguing that a) expected returns fluctuate, and b) they fluctuate with some degree of recurring patterns relative to various factors, such as dividend yield and price-to-earnings ratio across medium- and long-term horizons in the equity market, for instance. This news surprised a lot of economists, including several who have helped lay the groundwork for indexing, which is widely favored by investors who think that forecasting generally is bunk. So far, so good, although it’s easy abuse this discovery, as a recent research paper from Vanguard reminds.


“We find that many commonly cited signals have had very weak and erratic correlations with actual subsequent returns, even at long investment horizons,” advise the authors of “Forecasting stock returns: What signals matter, and what do they say now?” Doesn’t that evidence conflict with much of the research published in the last several decades? Not necessarily, although the Vanguard paper reminds us what’s too often forgotten: the signals that provide assistance in developing robust forecasts for asset returns are often murky in real time, and always less than flawless. As the authors explain,

We confirm that valuation metrics such as price/earnings ratios, or P/Es, have had an inverse or mean-reverting relationship with future stock market returns, although it has only been meaningful at long horizons and, even then, P/E ratios have “explained” only about 40% of the time variation in net-of-inflation returns. Our results are similar whether or not trailing earnings are smoothed or cyclically adjusted (as is done in Robert Shiller’s popular P/E10 ratio).

Consider, for instance, one of the paper’s graphs, which compares several predictors in terms of how they stack up in history relative to future equity market returns:

The best track record goes to P/E ratios based on trailing 10-year earnings (P/E 10). According to Vanguard, 43% of the variance of 10-year future real stock returns is “explained” by P/E 10 across time (1926-2011). That’s a relatively high reading, compared with most other metrics. But if 43% of future returns are explained by P/E 10, it follows that 57% of the variance is effectively noise. The record is even less encouraging for shorter-term horizons. Vanguard notes that P/E 10 for the year-ahead perspective is around 10%, and generally speaking “the estimated historical correlations of most metrics with the 1-year-ahead return were close to zero.”
Does that mean we should ignore predictors and give up trying to develop informed estimates of expected return? No, absolutely not. As Antti Ilmanen persuasively outlines in his invaluable reference work, Expected Returns: An Investor’s Guide to Harvesting Market Rewards, “investing involves both art and science; a solid background in the science can improve the artist.”
That includes familiarizing yourself with the pros and cons of the various predictors of risk premia that have been identified by financial economists over the years. Unless you’re truly a long-term investor, and have no plans to modify your portfolio, understanding the finer points of generating expected return estimates is critical. Why? Because producing a reasonable forecast of risk premia requires that we focus on risk. Obvious, perhaps, but too often ignored. There’s a reason why it’s called a risk premium.
Quite a lot of the troubles that investors suffered in recent years could have been minimized to a large degree if they’d been forecasting returns through a risk-management framework. Several asset classes were priced for perfection ahead of the market crash in late-2008. The preference at the time was to overlook the warning signs and assume that the rules had changed. Big mistake, and for a number of reasons that weren’t a secret then, or now.
Even under the best of circumstances, of course, quite a lot of the future will remain obscure, uncertain, and otherwise unknowable. What should we do? The Vanguard paper suggests that we start by thinking in probabilistic terms rather than coming up with point forecasts for expected returns. Agreed. If you’re crunching the numbers and deciding that stocks have a real expected return of 6%, you should understand the applicable prediction interval, as one example.
You should also have a solid understanding of the methodology that created the forecast. Even better, use several methodologies to cross check the predictions, and make a habit of forecasting regularly. Predictions are almost always wrong, but you can learn a lot from the process, particularly if you analyze how your forecasts fared vs. the actual data.
Favor a multi-asset class strategy too. The Vanguard paper’s conclusion that more than half of the stock market’s variance is unrelated to P/E10 implies that owning stocks will take you on a bumpy ride for reasons that are hard if not impossible to fathom at times. But volatility, understood or not, also presents opportunity, especially in the context of asset allocation.
As I explain in some detail in my book, Dynamic Asset Allocation, is the “natural extension of asset allocation” because it’s a relatively reliable tool for harvesting risk premia via price volatility. The combination of diversifying across asset classes and rebalancing the weights back to some pre-determined mix has an encouraging history of minimizing risk, modestly boosting return, or a bit of both.
If you also keep an eye out for falling rocks, including dark turns in the business cycle, you can improve the odds that you won’t be blind-sided by shocks. The infamous unknown unknowns are always lurking, of course, but you can do quite a lot with a plain vanilla risk-management strategy.
What’s the catch? You’ve got to do your homework, or hire someone who’ll do it for you. What you can’t do, at least not without suffering large doses of unnecessary hazards, is ignore history. Earning a reasonable risk premium doesn’t require a Ph.D. or a team of securities analysts. In fact, you can do quite well by simply holding a broad asset allocation that’s comprised of the major asset classes and rebalancing the mix every year or two. But you can’t get blood out of stone or beat the mathematics of finance for very long. Every investor who beats the market does so because another investor has fallen short of the benchmark.
That brings us back to investing 101, as explained by the Vanguard paper. “Forming reasonable long-run return expectations for stocks and other asset classes can be important in devising a strategic asset allocation. But what precisely are “reasonable” expectations in the current environment, and how should they be formed?” The answer begins by recognizing that there are no silver bullets.
If you’re skeptical, take a hard look at investors with a verifiable track record that’s above average, vs. a relevant benchmark and for 10 years or more. What you’re sure to find is an individual, or team of individuals, who are well versed in developing intelligent forecasts of return and risk.

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