Best Long-term U.S. Stock Market Return Predictors?

Which previously researched variables or combinations of such variables best predict long-term U.S. stock market returns? In their June 2023 paper entitled “Estimating Long-Term Expected Returns”, Rui Ma, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare abilities of several yield, yield/growth, valuation variables and combinations across these categories of variables to predict 10-year and 20-year U.S. stock market returns out-of-sample. Specifically, they test 25 predictors from the following individual variables and combinations thereof:

• Yield category: dividend yield, total yield, net total yield and cyclically adjusted total yield.
• Yield/growth category: current values of these yields plus historical earnings growth, dividend growth, total yield growth and cyclically adjusted total yield growth, respectively.
• Valuation category: total return cyclically adjusted price-earnings ratio, total wealth portfolio composition, equity market value-to-gross domestic product ratio (the Buffett indicator) and cyclical consumption.
• Combining categories based on: simple prediction average, inverse variance-weighted prediction average, constrained regression and Bayesian model averaging.

Their benchmark predictor is the historical average return. They use annualized log returns for all predictors, focusing on mean absolute errors and mean squared errors relative to actual future returns as accuracy metrics. They also consider also a mean-variance asset allocation perspective, allocating to the S&P 500 Index and 10-year U.S. Treasury notes to maximize gross Sharpe ratio based on predicted equity returns. Using monthly data as described above during 1871 through 2020, they find that:

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