Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 29, 2022 erstellt
Description:
Stock valuation ratios contain expectations of returns, yet, their performance in predicting returnshas been rather dismal. This is because of an omitted variable problem: valuation ratios also contain expectations of cash flow growth. Time-variation in cash flow volatility and a structural shift towards repurchases have magnified this omitted variable problem. We show theoretically and empirically that scaling prices by forward measures of cash flows can overcome this problem yielding optimal return predictors. We construct a new measure of the forward price-to-earnings ratio for the S&P index based on earnings forecasts using machine learning techniques. The out-of-sample explanatory power for predicting one-year aggregate returns with our forward price-to-earnings ratio ranges from 7% to 11%, thereby beating all other predictors and helping to resolve the out-of-sample predictability debate (Goyal and Welch, 2008)