Gone with the big data: Institutional lender demand for private information
Jung Koo Kang
Journal of Accounting and Economics, 2024, vol. 77, issue 2
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 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.
Keywords: Debt contract; Relationship lending; Information asymmetries; Institutional investors; Informed trading; Big data; Satellite images; Alternative data; Fintech (search for similar items in EconPapers)
JEL-codes: D82 G14 G21 G23 G30 M41 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaecon:v:77:y:2024:i:2:s0165410123000873
DOI: 10.1016/j.jacceco.2023.101663
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