This paper studies invariance relationships in tick-by-tick transaction data in the U.S. stock market. Over the 1993–2001 period, the estimated monthly regression coefficients of the log of trade arrival rate on the log of trading activity have an almost constant value of 0.666, strikingly...

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...

We study whether it is better to enforce the zero lower bound (ZLB) in models of U.S. Treasury yields using a shadow rate model or a quadratic term structure model. We show that the models achieve a similar in-sample fit and perform comparably in matching conditional expectations of future...

We use non-Gaussian features in U.S. macroeconomic data to identify aggregate supply and demand shocks while imposing minimal economic assumptions. Recessions in the 1970s and 1980s were driven primarily by supply shocks, later recessions were driven primarily by demand shocks, and the Great...