Using European stock data from two different venues and time periods for which we can identify each trade's aggressor, we test the performance of the bulk volume classification (Easley et al. (2016); BVC) algorithm. BVC is data efficient, but may identify trade aggressors less accurately than “bulk” versions of traditional trade-level algorithms. BVC-estimated trade flow is the only algorithm related to proxies of informed trading, however. This is because traditional algorithms are designed to find individual trade aggressors, but we find that trade aggressor no longer captures information. Finally, we find that after calibrating BVC to trading characteristics in out-of-sample data, it is better able to detect information and to identify trade aggressors. In the new era of fast trading, sophisticated investors, and smart order execution, BVC appears to be the most versatile algorithm.

Using Roberts (2015) loan-level data from 2000 to 2011, we find that the inception of CDS trading on reference firms’ debt is associated with a decreased number and lower probability of amendments, restatements, and rollovers to existing lenders of bank loans. Reference firms are also less likely to terminate loans prematurely or refinance with different lenders after the inception of CDS trading and tend to exhibit longer loan maturities. Our evidence is consistent with the empty creditor problem arising from CDS trading and the resulting decrease in the negotiation power of borrowers. Our research contributes to understanding how financial innovations alter bank-lending relationships.