Key Points

  • Using past performance to forecast future performance is likely to disappoint. We find that a factor’s most recent five-year performance is negatively correlated with its subsequent five-year performance.
  • By significantly extending the period of past performance used to forecast future performance, we can improve predictive ability, but the forecasts are still negatively correlated with subsequent performance: the forecast is still essentially useless!
  • Using relative valuations, we forecast the five-year expected alphas for a broad universe of smart beta strategies as a tool for managing expectations about current portfolios and constructing new portfolios positioned for future outperformance. These forecasts will be updated regularly and available on our website.

In a series of articles we published in 2016,1 we show that relative valuations predict subsequent returns for both factors and smart beta strategies in exactly the same way price matters in stock selection and asset allocation. To many, one surprising revelation in that series is that a number of “smart beta” strategies are expensive today relative to their historical valuations. The fact they are expensive has two uncomfortable implications. The first is that the past success of a smart beta strategy—often only a simulated past performance—is partly a consequence of “revaluation alpha” arising because many of these strategies enjoy a tailwind as they become more expensive. We, as investors, extrapolate that part of the historical alpha at our peril. The second implication is that any mean reversion toward the smart beta strategy’s historical normal relative valuation could transform lofty historical alpha into negative future alpha. As with asset allocation and stock selection, relative valuations can predict the long-term future returns of strategies and factors—not precisely, nor with any meaningful short-term timing efficacy, but well enough to add material value. These findings are robust to variations in valuation metrics, geographies, and time periods used for estimation.

Two assumptions widely supported in the finance literature form the basis for how most investors forecast factor alpha and smart beta strategy alpha. We believe both, although strongly entrenched in investors’ thinking, are wrong. The two assumptions we take issue with are that past performance of factor tilts and smart beta strategies is the best estimate of their future performance, and that factors and smart beta strategies have constant risk premia (value-add) over time.

Common sense tells us that current yield begets future return. Nowhere is this more intuitive than in the bond market. Investors fully understand that the average 30-year past return of long bonds, currently north of 7%, tells us nothing about the future return of long bonds. The current yield, around 3%, is far more predictive. In the equity market, at least since the 1980s, we know that the cyclically adjusted price-to-earnings (CAPE) ratio, as demonstrated by Robert Shiller, and the dividend yield are both good predictors of long-term subsequent returns.

If relative valuation, and the implication it has for mean reversion, is useful for stock selection and for asset allocation, why would it not matter in choosing factor tilts and equity strategies? The widespread promotion by the quant community of products based on past performance—often backtests and simulations—has contributed, and still does contribute, to investors’ costly bad habit of performance chasing. The innocent-looking assumption of “past is prologue” conveniently encourages investors and asset managers to pick strategies with high past performance and to presume the past alpha will persist in the future.

In our 2016 smart beta series we offer evidence that relative valuations are important in the world of factors and smart beta strategies. We show that variations in valuation levels predict subsequent returns and that this relationship is robust across geographies, strategies, forecast periods, and our choice of valuation metrics. Our research tells us that investors who (too often) select strategies based on wonderful past performance are likely to have disappointing performance going forward. For many, mean reversion toward historical valuation norms dashes their hopes of achieving the returns of the recent past.

These conclusions are, of course, just qualitative. To make them practical, we need to quantify the effects we observe. In this article we do precisely that. We measure the richness of selected factors based on their relative valuations versus their respective historical norms and calculate their implied alphas. We also call attention to the real-world “haircuts” on the implied alphas—implementation shortfall, trading costs, and manager fees—which don’t show up in paper portfolios and simulations.

Why Valuations Matter

We can easily see the link between valuation and subsequent performance on a scatterplot created using these two variables. The two scatterplots in Figure 1 are from Arnott, Beck, and Kalesnik (2016a) and are examples of the historical distributions of valuation ratios and subsequent five-year returns for a long–short factor, the classic Fama–French definition of value, and for a smart beta strategy (the low volatility index), as of March 31, 2016. In June 2016, we identified the former as the cheapest factor, relative to its history, and the latter as the most expensive strategy, relative to its history.

The value factor consists of a long value portfolio and a short growth portfolio. We measure performance and relative valuation by comparing the value portfolio relative to the growth portfolio. For the low-volatility index we measure performance and relative valuation by comparing the low-volatility portfolio with the cap-weighted stock market. The dotted line shows the average relationship between valuations and subsequent five-year performance. Both scatterplots show negative slope: richer valuations generally imply lower subsequent returns, while cheaper valuations imply higher subsequent returns. We use the same method for other factors and smart beta strategies. For most strategies and factors across multiple geographic regions the relationship is both statistically and economically significant.

Comparing Alpha-Forecasting Models

Many investors expect the alpha of a strategy to be its historical alpha, so much so that this assumption itself is an example of an alpha-forecasting model. One of the cornerstones of any investment process is an estimate of forward-looking return. We argue that a good alpha-forecasting model, whether for a strategy or a factor tilt, should have three key attributes::

  1. Forecasts should correlate with subsequent alphas.
  2. Forecasts should be paired with a measure of the likely accuracy of the forecast. A standard statistical way to measure the accuracy of a forecast is mean squared error, a measure of how reality has differed from past forecasts.
  3. Forecasts should provide realistic estimates of expected returns.

These criteria provide useful metrics for us to compare different alpha-forecasting models. We select six models for comparison. One model assumes an efficient market: no factors or strategies have any alpha. Two of the models use only past performance and ignore valuations, and four of the models are based on valuation levels relative to historical norms.

Model 0. Zero factor alpha. In an early version of the efficient market hypothesis—the capital asset pricing model, or CAPM—researchers argued that an asset’s return was solely determined by its exposure to the market risk factor. Similarly, Model 0 assumes the risk-adjusted alpha of a factor tilt or smart beta strategy is approximately zero. We measure the mean squared error relative to an expected alpha of zero.

Model 1. Recent past return (most recent five years). This model uses the most recent five-year performance of a factor or strategy

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