Dividend Momentum and Stock Return Predictability: A Bayesian Approach
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DOI: 10.29338/wp2021-25
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- Juan Antolín-Díaz & Ivan Petrella & Juan F. Rubio-Ramírez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," Working Papers 2021-14, FEDEA.
- Rubio-RamÃrez, Juan Francisco & Petrella, Ivan & Antolin-Diaz, Juan, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," CEPR Discussion Papers 16613, C.E.P.R. Discussion Papers.
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More about this item
Keywords
CS restrictions; Bayesian VARs; optimal allocation;All these keywords.
JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CWA-2022-01-03 (Central and Western Asia)
- NEP-FOR-2022-01-03 (Forecasting)
- NEP-MAC-2022-01-03 (Macroeconomics)
- NEP-ORE-2022-01-03 (Operations Research)
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