Since January 2014, the U.S. Treasury has been issuing floating rate notes (FRNs). These notes pay quarterly interest based on an average of the constant maturity rates of newly issued three-month T-bills during the quarter. We show how to price such FRNs. We estimate that they have been paying excess interest between 3 and 42 basis points above the implied interest of other Treasury securities. We interpret this fact through the lens of a model where money-like assets differ in their degrees of moneyness. Additional empirical evidence supports this interpretation.

We study aggregate lapsation risk in the life insurance sector. We construct two lapsation risk factors that explain a large fraction of the common variation in lapse rates of the 30 largest life insurance companies. The first is a cyclical factor that is positively correlated with credit spreads and unemployment, while the second factor is a trend factor that correlates with the level of interest rates. Using a novel policy-level database from a large life insurer, we examine the heterogeneity in risk factor exposures based on policy and policyholder characteristics. Young policyholders with higher health risk in low-income areas are more likely to lapse their policies during economic downturns. We explore the implications for hedging and valuation of life insurance contracts . Ignoring aggregate lapsation risk results in mispricing of life insurance policies. The calibrated model points to overpricing on average. In the cross-section, young, low-income, and high-health risk households face higher effective mark-ups than the old, high-income, and healthy.

We examine social media attention and sentiment from three major platforms: Twitter, StockTwits, and Seeking Alpha. We find that, even after controlling for firm disclosures and news, attention is highly correlated across platforms, but sentiment is not: its first principal component explains little more variation than purely idiosyncratic sentiment. Using market events, we attribute differences across platforms to differences in users (e.g., professionals versus novices) and differences in platform design (e.g., character limits in posts). We also find that sentiment and attention contain different return-relevant information. Sentiment predicts positive next-day returns, but attention predicts negative next-day returns. These results highlight the importance of considering both social media sentiment and attention, and of distinguishing between different investor social media platforms.