Predicting stock returns with Bayesian vector autoregressive models

We derive a vector autoregressive (VAR) representation from the dynamic dividend discount model to predict stock returns. This valuation approach with time-varying expected returns is augmented with macroeconomic variables that should explain time variation in expected returns and cash flows. The VAR is estimated by a Bayesian approach to reduce some of the statistical problems of earlier studies. This model is applied to forecasting the returns of a portfolio of large German firms. While the absolute forecasting performance of the Bayesian vector-autoregressive model (BVAR) is not significantly different from a naive no-change forecast, the predictions of the BVAR are better than alternative time-series models. When including past stock returns instead of macroeconomic variables, the forecasting performance becomes superior relative to the naive no-change forecast especially over longer horizons.