We study specialized lending in a credit market competition model with private information. Two banks, equipped with similar data processing systems, possess "general" signals regarding the borrower's quality. However, the specialized bank gains an additional advantage through further...

We develop a credit market competition model that distinguishes between the information span (breadth) and signal precision (quality), capturing the emerging trend in fintech/non-bank lending where traditionally subjective ("soft") information becomes more objective and concrete ("hard"). In a...

Corporate credit lines are drawn more heavily when funding markets are more stressed. This covariance elevates expected bank funding costs. We show that credit supply is dampened by the associated debt-overhang cost to bank shareholders. Until 2022, this impact was reduced by linking the...

Many authors argue that asymmetric information between the Federal Reserve and the public is important to the conduct and the effects of monetary policy. This paper tests for the existence of such asymmetric information by examining Federal Reserve and commercial inflation forecasts. We...

A model of interest rate movements in response to new information on the money stock is developed.The model, which incorporates several earlier approaches as special cases, makes explicit the manner in which estimated interest rate responses to money surprises depend on the relative variances of...

Banks are optimally opaque institutions. They produce debt for use as a transaction medium (bank money), which requires that information about the backing assets - loans - not be revealed, so that bank money does not fluctuate in value, reducing the efficiency of trade. This need for opacity...

Using proprietary individual level loan data, this paper explores the economic consequences of the 2009 bank entry deregulation in China. Such deregulation leads to higher screening standards, lower interest rates, and lower delinquency rates for corporate loans from entrant banks. Consequently,...

When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending--where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm--we document large...

Nonbank lenders have been playing an increasing role in supplying debt, especially after the Great Recession. How important are the distortions in the greater regulation of banks that differentially limit risk-taking across alternative providers of credit? How might the growing role of nonbanks...