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.

We hypothesize that companies in the same product market avoid sharing the same audit partner when they are concerned about possible information spillovers. Consistent with our hypothesis, we find that product market rivals are less likely to share the same partner when they perceive that information spillovers are more costly. While concerns about information spillovers significantly reduce the likelihood of product market rivals sharing the same audit partner, we find that such concerns do not deter them from sharing the same audit office. Lastly, when companies are unconcerned with information spillovers, our results suggest that partner sharing can be beneficial because it can result in lower audit fees and fewer accounting misstatements.

We explore whether the introduction of transparent reporting rules increases credit standard harmonization within a bank. We exploit the new loan-level reporting rules imposed on banks that borrow from the European Central Bank using repurchase agreements collateralized by their asset-backed securities. We compare credit terms of similar mortgages issued by a bank across a country's regions and find that harmonization increases following the adoption of the new reporting rules. Learning and regulatory scrutiny constitute mechanisms underlying this economic effect. We also show that harmonization leads to more favorable lending terms to borrowers and higher loan quality for banks. Overall, these findings suggest that transparent reporting rules incentivize banks to improve their internal decision-making and thereby reduce regional divergence in their credit standards.

We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store‐level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short‐term trading profits.

To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of individuals lacking a prior credit history. However, short-term, high-interest digital loans have raised concerns about predatory lending practices. To examine how digital credit influences borrowers’ financial well-being, we use proprietary data from a digital lender in Kenya that randomly approves loan applications that would have otherwise been rejected based on the borrower’s credit profile. We find that access to digital credit improves borrowers’ financial well-being across various mobile-phone-based well-being measures, including monetary transactions and balances, mobility, and social networks as well as borrowers’ self-reported income and employment. We further show that this positive impact is more pronounced when borrowers have limited access to credit, take loans for business purposes, and obtain more credit.