Non-Stationary Dividend-Price Ratios

TL;DR

This paper investigates the predictive power of a modified dividend-price ratio (mdp) for stock returns, demonstrating its robustness and superior out-of-sample performance over the classical ratio, especially after 1965.

Contribution

It extends previous work by applying multivariate regressions and dynamic estimation to validate the mdp's effectiveness in forecasting returns.

Findings

Modified dp ratio (mdp) outperforms classical dp in return prediction.

Out-of-sample performance gain ranges from 30% to 50%.

Robustness confirmed after 1965, debunking risk-free return prediction bias.

Abstract

Dividend yields have been widely used in previous research to relate stock market valuations to cash flow fundamentals. However, this approach relies on the assumption that dividend yields are stationary. Due to the failure to reject the hypothesis of a unit root in the classical dividend-price ratio for the US stock market, Polimenis and Neokosmidis (2016) proposed the use of a modified dividend price ratio (mdp) as the deviation between d and p from their long run equilibrium, and showed that mdp provides substantially improved forecasting results over the classical dp ratio. Here, we extend that paper by performing multivariate regressions based on the Campbell-Shiller approximation, by utilizing a dynamic econometric procedure to estimate the modified dp, and by testing the modified ratios against reinvested dividend-yields. By comparing the performance of mdp and dp in the period after 1965, we are not only able to enhance the robustness of the findings, but also to debunk a possible false explanation that the enhanced mdp performance in predicting future returns comes from a capacity to predict the risk-free return component. Depending on whether one uses the recursive or population methodology to measure the performance of mdp, the Out-of-Sample performance gain is between 30% to 50%.