Ultra-Simple Shiller’s CAPE: How One Year’s Data Can Predict Equity Market Returns Better Than Ten

Abstract

Professor Robert Shiller’s cyclically adjusted price/earnings ratio (CAPE) has proven to be a powerful descriptor, as well as a useful predictor, of long-term equity returns in the United States and many global markets. CAPE uses a 10-year average of real earnings to simultaneously filter noise in earnings and to estimate corporate profitability over a business cycle. In this article, the authors simplify the CAPE methodology by separating the filtering of noise from the detection of cyclicality in earnings. They filter noise by discarding the worst quarter’s earnings in each year, allowing them to use one year’s earnings instead of 10, and proxy temporal variation in profit margins using the sales-to-price ratio. In addition, they account for an empirical nonlinearity in the relationship between valuation ratios and equity market returns. They combine the output of two models, one based on earnings and the other on sales, to create a robust forecast of 10-year forward returns. In out-of-sample tests, their technique increases the correlation between out-of-sample-forecasts and realizations from 0.69 to 0.87, reduces the standard deviation of the forecast error for the 10-year returns of the S&amp;P 500 relative to CAPE by 40%, and linearizes the relationship between forecast and realized returns. <b>TOPICS:</b>Emerging, accounting and ratio analysis, fundamental equity analysis <b>Key Findings</b> • Campbell and Shiller’s CAPE predicts equity market returns using the average of the past 10 years’ earnings. The authors show that significantly better forecasts can be obtained using only one year’s quarterly earnings and revenues. • Noise reduction similar to that obtained using the 10-year average can be obtained by discarding the worst quarter’s earnings in each year to construct a new earnings measure based on the three best quarters’ earnings. Additionally, the sales-to-price ratio, which reflects current profit margins, usefully predicts future margins and earnings growth. • In out-of-sample tests, the new model’s predictions are statistically significant, while CAPE’s predictions are not.