This paper investigates spillovers from foreign economies to the U.S. through changes in longterm Treasury yields. We document a decline in the contribution of U.S. domestic news to the variance of long-term Treasury yields and an increased importance of overnight yield changes—a rough proxy...

We explore the role of evolving beliefs regarding the structure of the macroeconomy in improving our understanding of the term structure of interest rates within the context of a simple macro-finance model. Using quarterly vintages of real-time data and survey forecasts for the United States...

This paper argues that changes in the propagation of idiosyncratic shocks along firm networks are important to understanding variations in asset returns. When calibrated to match key features of supplier-customer networks in the United States, an equilibrium model in which investors have...

In this paper, we extract common factors from a cross-section of U.S. macro-variables and Treasury zero-coupon yields. We find that two macroeconomic factors have an important predictive content for government bond yields and excess returns. These factors are not spanned by the cross-section of...

We study whether stock market returns in oil-exporting countries can be predicted by oil price changes, and we investigate the link between predictability and the quality of each country's institutions. Returns are predictable for half the countries we consider, and predictability is stronger...

We propose a method to decompose stock returns period by period. First, we argue that one can directly estimate expected stock returns from securities available in modern financial markets (using the real yield curve and the Martin (2017) equity risk premium). Second, we derive a return...

While many studies find that the tail distribution of high frequency stock returns follow a power law, there are only a few explanations for this finding. This study presents evidence that time-varying volatility can account for the power law property of high frequency stock returns. The power...

This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news,...

This paper shows that funding liquidity risk is priced in the cross-section of excess returns on agency mortgage-backed securities (MBS). We derive a measure of funding liquidity risk from dollar-roll implied financing rates (IFRs), which reflect security-level costs of financing positions in...

Using high frequency data, we develop an event study method to test for level shifts in beta and measure abnormal returns for events that produce such level shifts. Using this method, we estimate abnormal returns for the Troubled Asset Relief Program (TARP) announcement and find that its...