Gone with the big data: Institutional lender demand for private information

• 成果类型: Article
• 署名作者: Kang, Jung Koo
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2023.101663
• 发表日期: 2024
• 关键词: Public disclosure; bank reputation; capital-markets; trading volume; earnings; debt; cost; acquisition; COMPETITION; asymmetry

摘要

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 can undermine 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.