While a host of economic variables have been identified in the literature with the apparent in-sample ability to predict the equity premium, Goyal and Welch (2008) find that these variables fail to deliver consistent out-of-sample forecasting gains relative to the historical average. Arguing...

We investigate lead-lag relationships among country stock returns and identify a leading role for the United States: lagged U.S. returns significantly predict returns in numerous non-U.S. industrialized countries (after controlling for national economic variables and countries' own lagged...

Yes, they can! Machine learning models that exploit big data identify leverage determinants and predict leverage better than classical methods. By allowing for nonlinearities and complex interactions, machine learning boosts the out-of-sample R-squared from 36% to 56% over linear methods such as...

This paper investigates the impact of high-speed railroads (HSR) on city-level economic activity using a new dataset for approximately 200 cities in China from 2007-2014. We apply panel Granger causality methods to assess whether increases in a city's accessibility increases GDP growth, GDP per...

In this paper, we forecast industry returns out-of-sample using the cross-section of book-to-market ratios and investigate whether investors can exploit this predictability in portfolio allocation. Cash-flow and return forecasting regressions show that cross-industry book-to-market ratios...