The dividend-growth based test of return predictability, proposed by Cochrane [2008, Review of Financial Studies 21, 1533-1575], is similar to a likelihood-based test of the standard return-predictability model, treating the autoregressive parameter of the dividend-price ratio as known. In...

We have seen China's growing role in the past decades, and the world economy has become more exposed to the influence of China. This paper explores emerging China's impact on the global equity market through the lens of asset pricing. We study the predictive properties of the lagged China...

In this paper I examine whether one can use analyst forecasts of macroeconomic variables to improve investors ex-ante allocation of wealth between stocks and bonds. Such forecasts provide a forward-looking approach which I find improves investor's information set for the myopic stock-bond...

We assess whether observable corporate political strategies can serve as channels of value-relevant political information flow into stock prices and form the basis for profitable return predictability strategies. We document that returns of politically connected firms' stocks lead those of their...

This paper constructs and analyzes various measures of trading costs in US equity markets covering the period 1926–2015. These measures contain statistically and economically significant predictive signals for stock market returns and real economic activity. We decompose illiquidity proxies...

This paper examines the importance of realized volatility in bond yield density prediction. We incorporate realized volatility into a Dynamic Nelson-Siegel (DNS) model with stochastic volatility and evaluate its predictive performance on US bond yield data. When compared to popular...

The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding...

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering...

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based upon observable characteristics, using machine learning principles with linear models. The best performing methods are flexible two-stage...

This paper revisits the predictability of bond excess returns by means of long-term forward interest rates. We assess the economic value of out-of-sample forecasting ability of empirical models based on forward rates in a dynamic asset allocation strategy. Our results show that the information...