Dividend Dynamics, Learning, and Expected Stock Index Returns

Abstract

We show that, in a frictionless and efficient market, an asset pricing model that better describes investors' behavior should better forecast stock index returns. We propose a dividend model that predicts, out-of-sample, 31.3% of the variation in annual dividend growth rates (1976-2015). Further, when learning about dividend dynamics is incorporated into a long-run risks model, the model predicts, out-of-sample, 22.4% of the variation in annual stock index returns (1976-2015). This supports the view that both investors' aversion to long-run risks and learning about these risks are important in determining asset prices and expected returns.

Keywords: Dividends, Earnings, Stock Index Returns, Learning, Early Resolution of Uncertainty, Predictability of Stock Returns, Predictability of Dividends.

JEL Classification: G10, G11, G12, G14