The growth of big data, particularly the availability of real-time information through alternative sources, may be reshaping the dynamics of institutional lending. A recent paper, “Gone with the Big Data: Institutional Lender Demand for Private Information”, by HBS Assistant Professor of Business Administration and researcher at D^3’s Digital Value Lab, Jung Koo Kang, explores these changes as it investigates whether readily available big data can diminish the value of private information acquired through lending relationships. Specifically, Kang analyzes the impact of the availability of alternative data on institutional lenders (non-bank lenders, such as hedge funds and private equity firms) and their participation in syndicated loans (loans made by groups of institutional lenders).
Key Insight: Big Data Reduces the Value of Private Information in Lending Decisions
“When institutional lenders can access near–real-time information on a borrower’s performance through this alternative source, the value of early access to borrowers’ performance information through syndicate participation diminishes.” [1]
Kang’s research considers how satellite imagery of U.S. retail parking lots can be used to track retail performance and how it, subsequently, reduces the reliance of institutional lenders on private borrower information, such as financial reports and projections. The finding suggests that the opportunity to gain early access to such information, previously crucial to fostering lending relationships, will, in cases where alternative data exists, become less valuable.
Key Insight: Banking Lenders Compared to Institutional Lenders
“[I]nstitutional lenders are less likely to issue loans to borrowers covered by the satellite data.” [2]
Kang’s analysis shows that the lending patterns of non-bank institutional lenders, in particular, are affected by the availability of alternative data. Unlike traditional banks, which are more highly regulated and have stricter internal controls, institutional lenders are more likely to leverage private information to inform their investment strategies and trading activities, also known as insider trading. The widespread availability of more precise and timely alternative data undermines this competitive advantage, potentially reducing their participation in syndicated loans and reshaping their role in the credit market.
Key Insight: Borrower Opacity and Information Demand
“Opaque borrowers provide imprecise public signals; therefore, traders have more heterogeneous beliefs about them, which encourages private information acquisition and informed trading.” [3]
In the period before a loan is issued, opaque borrowers—those with less publicly available information—have traditionally been highly attractive to institutional lenders, given that the latter benefited from the informational advantage they gain through access to private data. Measuring opacity by analyst coverage and issuance rates of earnings forecasts and press releases, Kang’s research shows that when alternative data becomes available, it reduces institutional lending particularly to opaque borrowers.
Key Insight: Data Accuracy and Impact on Borrowers
“These borrowers pay higher interest rates, receive smaller loan amounts, and obtain loans with shorter maturities.” [4]
Kang’s study finds that the effect of satellite data coverage on institutional lending is amplified when the satellite data is highly accurate in predicting the borrower’s performance. That is, when satellite imagery provides precise signals (e.g., high correlation between car counts in parking lots and firm sales or low variability of car counts across different stores), it diminishes the value of borrowers’ private information even further. This highly accurate data makes institutional lenders even less likely to extend loans and, in turn, leads to less favorable loan terms for affected borrowers.
Why This Matters
From 2000 to 2014, non-bank lenders’ share of the syndicated loan market grew from 40% to 60%, so it’s crucial for business professionals, particularly C-suite executives in the retail space, to understand the current shift in institutional lending trends in order to recalibrate their strategies and leverage the power of big data. These firms may be less able to rely on private relationships with institutional lenders to secure favorable loan terms. Instead, the transparency provided by big data demands that firms be more proactive in managing their public financial disclosures and operational data. For C-suite executive lenders—especially non-bank lenders—it’s important to understand how the availability of alternative data sources may reduce their participation in syndicate loans, lower their ability to exploit traditional private data, and affect the terms they offer to borrowers.
References
[1] Jung Koo Kang, “Gone with the Big Data: Institutional Lender Demand for Private Information”, Journal of Accounting and Economics 77 (April/May 2024): 1-29, 2.
[2] Kang, “Gone with the Big Data: Institutional Lender Demand for Private Information”, 3.
[3] Kang, “Gone with the Big Data: Institutional Lender Demand for Private Information”, 21.
[4] Kang, “Gone with the Big Data: Institutional Lender Demand for Private Information”, 27.
Meet the Authors
Dr. Jung Koo Kang is an Assistant Professor of Business Administration at Harvard Business School and is an affiliated faculty member of D^3’s Digital Value Lab. Professor Kang’s research areas are in financial technology and innovation, alternative data, debt contracting, financial intermediation, and auditing. Specifically, he studies how financial technologies and alternative data drive innovation in credit markets, create value for businesses, and address important social problems.