This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are limited value since they carry no information about intrinsic predictive uncertainty. We compare empirical performance alternative forecasts for generated from a naïve model, univariate autoregressive and multivariate model (predictive r...

We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for data-snooping biases. We disaggregate the sources for predictability by using several asset groups, including size-sorted and industry-sorted portfolios...

In this note we propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on the correlogram of quantile hits. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to stock index return data. The ...

Article history: This paper explores whether the relevance of a conditional multifacReceived 12 July 2012 tor model and autocorrelation in predicting the Russian aggregate Received in revised form 2 October 2012 stock return fluctuates over time. The source of return predictability is Accepted 16 December 2012 shown to vary considerably with information flow. In general, Available online 22 Dec...

We examine the evidence on excess stock return predictability in a Bayesian setting in which the investor faces uncertainty about both the existence and strength of predictability. When we apply our methods to the dividend-price ratio, we find that even investors who are quite skeptical about the existence of predictability sharply modify their views in favor of predictability when confronted b...