We study whether a large set of financial ratios provides valuable information about future excess stock returns. Confronted with a data-rich environment, we propose a novel ``divide and conquer" methodology that allows to efficiently retain all of the information available to investors. In...

We provide a measure of sparsity for expected returns within the context of classical factor models. Our measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions...

We study the effect of the predictability of order imbalance on market quality. We measure the degree of predictability by using the predictive likelihood from a dynamic linear model where the dependent variable is the day-ahead order imbalance. Empirically, we show that increasing order...

I use Bayesian tools to develop a dynamic testing methodology for conditional factor pricing models, in which time-varying betas, idiosyncratic risks, and factors risk premia are jointly estimated in a single step. Based on this framework, I test over fifty years of post-war monthly data some of...

We investigate the determinants of the commodity (ex-ante) risk premia for different maturities through the lens of a model of adaptive learning in which expected future spot prices are revised based on past prediction errors and changes in economic fundamentals. The main results show that risk...