Optimal investment of firms implies that expected stock returns are tied with the expected marginal benefit of investment divided by the marginal cost of investment. Winners have higher expected growth and expected marginal productivity (two major components of the marginal benefit of...

We examine information spillover as a source of stock return synchronicity, where information about highly-followed "prominent" stocks is used to price other "neglected" stocks sharing a common fundamental component. We find that stocks followed by few analysts co-move significantly with...

We study the interactions between the stock market and the labor market. When aggregate risk premiums are time-varying, predictive variables for market excess returns should forecast long-horizon growth in the marginal benefit of hiring and thereby long-horizon aggregate employment growth....

Is the value premium predictable? We study time-variations of the expected value premium using a two-state Markov switching model. We find that when conditional volatilities are high, the expected excess returns of value stocks are more sensitive to aggregate economic conditions than the...

Previous work shows that the growth rate of industrial production is a common macroeconomic risk factor in the cross-section of expected returns. We demonstrate the connection between momentum profits and shifts in factor loadings on this macroeconomic variable. Winners have temporarily higher...

Recent studies have used the value spread to predict aggregate stock returns to construct cash-flow betas that appear to explain the size and value anomalies. We show that two related variables, the book-to-market spread (the book-to-market of value stocks minus that of growth stocks) and the...

I construct a neoclassical, Q-theoretical foundation for time-varying expected returns in connection with corporate policies and events. Under certain conditions, stock return equals investment return, which is directly tied with firm characteristics. This single equation is shown analytically...