Publications
Publications
- April–May 2024
- Journal of Accounting & Economics
Gone with the Big Data: Institutional Lender Demand for Private Information
By: Jung Koo Kang
Abstract
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 to borrowers’ private information by trading on it in financial markets. As a shock to this advantage, I use the release of the satellite data of car counts in store parking lots of U.S. retailers. This data provides accurate and near–real-time signals of firm performance, which undermines the value of borrowers’ private information obtained through syndicate participation. I find that once the satellite data becomes commercially available, institutional lenders are less likely to participate in syndicated loans. The effect is more pronounced when borrowers are opaque or disseminate private information to their lenders earlier and when the data predicts borrower performance more accurately. I also show that institutional lenders’ reduced demand for private information leads to less favorable loan terms for borrowers.
Keywords
Analytics and Data Science; Borrowing and Debt; Financial Markets; Value; Knowledge Dissemination; Financing and Loans
Citation
Kang, Jung Koo. "Gone with the Big Data: Institutional Lender Demand for Private Information." Art. 101663. Journal of Accounting & Economics 77, nos. 2-3 (April–May 2024).