Non-stationary dividend-price ratios
Autor: | Ioannis Neokosmidis, Vassilis Polimenis |
---|---|
Rok vydání: | 2019 |
Předmět: |
Multivariate statistics
Information Systems and Management Strategy and Management Population Measure (mathematics) FOS: Economics and business Portfolio Management (q-fin.PM) 0502 economics and business Econometrics Business and International Management education Robustness (economics) Quantitative Finance - Portfolio Management Mathematics 040101 forestry education.field_of_study 050208 finance 05 social sciences 04 agricultural and veterinary sciences 0401 agriculture forestry and fisheries Dividend Stock market Cash flow Pricing of Securities (q-fin.PR) Unit root General Finance (q-fin.GN) Quantitative Finance - General Finance Quantitative Finance - Pricing of Securities |
Zdroj: | Journal of Asset Management. 20:552-567 |
ISSN: | 1479-179X 1470-8272 |
Popis: | 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%. |
Databáze: | OpenAIRE |
Externí odkaz: |