Working papers
Deposit Specialization and Lending Behavior with Kristian Blickle and Anthony Saunders December 2025
We examine how banks’ depositor composition shapes lending behavior, using granular supervisory data on deposits, loans, and securities for the largest U.S. banks. Classifying banks by depositor specialization, we find persistent differences in funding that translate to differences in asset allocations. Retail-depositor oriented banks hold longer-maturity loans and conduct more real estate lending, while corporate- and NBFI-oriented banks, whose funding is more volatile, hold shorter loans and liquid securities. Loan-level analyses show that stable funding is associated with lower rates, longer maturities, and larger loans. Growth in deposits is allocated differently depending on the depositor specialization of the bank, something we can explore using exogenous deposit growth during
We examine how banks’ depositor composition shapes lending behavior, using granular supervisory data on deposits, loans, and securities for the largest U.S. banks. Classifying banks by depositor specialization, we find persistent differences in funding that translate to differences in asset allocations. Retail-depositor oriented banks hold longer-maturity loans and conduct more real estate lending, while corporate- and NBFI-oriented banks, whose funding is more volatile, hold shorter loans and liquid securities. Loan-level analyses show that stable funding is associated with lower rates, longer maturities, and larger loans. Growth in deposits is allocated differently depending on the depositor specialization of the bank, something we can explore using exogenous deposit growth during
Probability Pricing with Eduardo Dávila and Ansgar Walther October 2025
This paper develops probability pricing, extending cash flow pricing to quantify the willingness-to-pay for changes in probabilities. We show that the value of any marginal change in probabilities can be expressed as a standard asset-pricing formula with hypothetical cash flows derived from changes in the survival function. This equivalence between probability and cash flow valuation allows us to construct hedging strategies and systematically decompose individual and aggregate willingness-to-pay. Four applications examine the valuation of changes in the distribution of aggregate consumption, the efficiency effects of varying performance precision in principal-agent problems, and the welfare implications of public and private information.
This paper develops probability pricing, extending cash flow pricing to quantify the willingness-to-pay for changes in probabilities. We show that the value of any marginal change in probabilities can be expressed as a standard asset-pricing formula with hypothetical cash flows derived from changes in the survival function. This equivalence between probability and cash flow valuation allows us to construct hedging strategies and systematically decompose individual and aggregate willingness-to-pay. Four applications examine the valuation of changes in the distribution of aggregate consumption, the efficiency effects of varying performance precision in principal-agent problems, and the welfare implications of public and private information.
Information Span in Credit Market Competition* with Zhiguo He, and Jing Huang August 2025
We develop a credit market competition model that distinguishes between information span (breadth) and signal precision (quality), capturing the rise of fintech/non-bank lending where traditionally subjective (“soft”) information is transformed into objective (“hard”) data. Borrower quality depends on multidimensional fundamentals, assessed through hard or soft signals. Two banks observe private hard signals, but only the specialized bank receives a soft signal. Expanding the span of hard information enables the non-specialized bank to evaluate characteristics previously only available to the specialist, and reducing its winner’s curse. By contrast, greater precision of hard signals strengthens the specialized bank’s informational advantage.
* Previously titled "Specialized Lending when Big Data Hardens Soft Information ".
We develop a credit market competition model that distinguishes between information span (breadth) and signal precision (quality), capturing the rise of fintech/non-bank lending where traditionally subjective (“soft”) information is transformed into objective (“hard”) data. Borrower quality depends on multidimensional fundamentals, assessed through hard or soft signals. Two banks observe private hard signals, but only the specialized bank receives a soft signal. Expanding the span of hard information enables the non-specialized bank to evaluate characteristics previously only available to the specialist, and reducing its winner’s curse. By contrast, greater precision of hard signals strengthens the specialized bank’s informational advantage.
* Previously titled "Specialized Lending when Big Data Hardens Soft Information ".