We use machine learning methods to create a comprehensive measure of credit risk based
on qualitative information disclosed in conference calls and in management's discussion …

We examine the effect of credit default swaps (CDSs) on debt specialization. We argue that
reference firms in CDS contracts, seeking to minimize creditor conflicts and bankruptcy …

I explore whether big-data sources can crowd out the value of private information acquired
through lending relationships. Institutional lenders have been shown to exploit their access …

We document a novel trend in syndicated lending where some participants voluntarily waive
their rights to borrowers' private information. We posit that “public-side” lending emerged to …

We examine whether private lenders learn from the equity market. We conjecture that
lenders can learn new information from stock prices about firms' fundamentals and growth …

We examine the significance and uniqueness of individual-pair relationships cultivated
through repeated loan interactions. Using a hand-collected dataset compiled of borrowing …

We investigate whether lead arrangers opportunistically withhold their private information
from participant lenders and how this behavior affects the structure of loan syndicates. Using …

We find that following options listings, US firms enjoy an average 17 bps interest reduction in
new bank loans. These savings are greater among more opaque borrowers, which provides …

Researchers often use regression-based x-Scores (eg, conservatism C-Score, misstatement
F-Score) from a stage 1 model as a dependent variable in stage 2. We argue that this x …

Using a regression-discontinuity design and within lender–borrower variation, we analyze
how credit default swaps (CDSs) affect bank incentives and borrower outcomes in …