We find that firm-level variance risk premium, estimated as the difference between option-implied and expected variances, has a prominent explanatory power for credit spreads in the presence of market- and firm-level risk control variables identified in the existing literature. Such a...

We test the widely held assumption that longer restructurings are more costly. In contrast to earlier studies, we use instrumental variables to control for the endogeneity of restructuring time and creditor return. Instrumenting proves critical to our finding that creditor recovery rates...

Using only daily data on bond and stock returns, we identify and characterize flight to safety (FTS) episodes for 23 countries. On average, FTS days comprise less than 3% of the sample, and bond returns exceed equity returns by 2.5 to 4%. The majority of FTS events are country-specific not...

Dealers in over-the-counter securities form networks to mitigate search frictions. The audit trail for municipal bonds shows the dealer network has a core-periphery structure. Central dealers are more efficient at matching buyers and sellers than peripheral dealers, which shortens intermediation...

We exploit detailed transaction and position data for a sample of long-short equity hedge funds to study the trading activity of fundamental investors. We find that hedge funds exhibit skill in opening positions, but that they close their positions too early, thereby forgoing about a third of...

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...

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,...