The Optimal Stock Valuation Ratio

Abstract

Trailing price ratios, such as the price-dividend and the price-earnings ratio, scale prices by trailing cash flow measures. They theoretically contain expected returns, yet, their performance in predicting stock market returns is poor. This is because of an omitted variable problem: trailing price ratios also contain expected cash flow growth. We show that structural changes in cash flow growth have undermined the power of trailing price ratios to predict returns. To construct an optimal valuation ratio, we propose scaling prices by cash flow forecasts (forward price ratios). We implement this idea for the S&P500 index using machine learning forecasts of firms’ cash flows. The out-of-sample explanatory power for one-year stock returns ranges from 7% to 11%, thereby beating all other predictors and helping to resolve the out-of-sample predictability debate (Goyal and Welch, 2008).

Keywords: Stock market valuation, return prediction, out-of-sample prediction, machine learning

JEL Classification: G00, G11, G12, G14