A forecast evaluation of expected equity return measures

Recent studies find evidence in favour of return predictability, and argue that their positive findings result from their ability to capture expected returns. We assess the forecasting performance of two popular approaches to estimating expected equity returns, a dividend discount model (DDM) commonly used to estimate `implied cost of capital', and a vector autoregression (VAR) model commonly used to decompose equity returns. In line with recent evidence, in-sample tests show that both estimates generate substantially lower forecast errors compared to traditional predictor variables such as price-earnings ratios and dividend yields. Out-of-sample, the VAR and DDM estimates generate economically and statistically significant forecast improvements relative to a historical average benchmark. Our results tentatively suggest that the VAR approach better captures expected returns compared to the DDM.