Bank Information Production Over the Business Cycle, (with Cooper Howes)
Review of Economic Studies, forthcoming
Adverse Selection in Corporate Loan Markets, (with Mehdi Beyhaghi and Cesare Fracassi))
Journal of Finance, forthcoming UWFC Recording of Talk
Distortions Caused by Lending Fee Retention, (with Travis L. Johnson)
Management Science 71 (2025), 35-58 SoFi's Guide to Share Lending discussing our paper
The Term Structure of Short Selling Costs
Review of Finance 27 (2023), 2125–2161
What's in a Debt? Rating Agency Methodologies and Firms’ Financing and Investment Decisions, (with Cesare Fracassi)
Revise and Resubmit, Review of Corporate Finance Studies
The Information Advantage of Banks: Evidence From Their Private Credit Assessments, (with Mehdi Beyhaghi and Cooper Howes)
Revise and Resubmit, Journal of Finance
The Impact of Beliefs on Credit Markets: Evidence from Rating Agencies, (with Chen Wang)
Revise and Resubmit, Management Science
Access to Capital and the IPO Decision: An Analysis of US Private Firms, (with Andres Almazan, Nathan Swem and Sheridan Titman)
We analyze firms' IPO decisions using detailed supervisory data on a large sample of US private firms. We find that firms with higher ex-ante capital needs are more likely to go public. Newly public firms increase their investment relative to their counterparts that remain private. These firms tend to finance their new investment with bank debt, maintaining leverage ratios close to their pre-IPO levels. After going public, firms borrow from an expanded pool of lenders at improved terms. Finally, we find that the within-firm dispersion in banks' private risk assessments drops after the IPO. Overall, our evidence is consistent with firms going public to reduce information asymmetries, thereby improving their access to capital.
Reputational Algorithm Aversion
~Columbia & RFS AI in Finance Conference, HEC Paris AI and Entrepreneurship Workshop, AEA Annual Meeting
People are often reluctant to incorporate information produced by algorithms into their decisions, a phenomenon called ``algorithm aversion''. This paper shows how algorithm aversion arises when the choice to follow an algorithm conveys information about a human's ability. I develop a model in which workers make forecasts of a random outcome based on their own private information and an algorithm's signal. Low-skill workers receive worse information than the algorithm and hence should always follow the algorithm's signal, while high-skill workers receive better information than the algorithm and should sometimes override it. However, due to reputational concerns, low-skill workers inefficiently override the algorithm to increase the likelihood they are perceived as high-skill. The model provides a fully rational microfoundation for algorithm aversion that aligns with the broad concern that AI systems will displace many types of workers.
Relationship Lending in Bond Markets?, (with Paul Beaumont and David Schumacher)
~Aarhus Workshop on Strategic Interaction in Corporate Finance, NFA, Fixed Income and Financial Institutions Conference
We use callable bonds as a laboratory to test whether relationship lending can be sustained in public financial markets. Fixed-price calls allow firms to repurchase bonds at a low price, resulting in a transfer from debtholders to equityholders. We show that following a fixed-price call, existing bondholders are less likely to participate in the firm's future bond issuances. This behavior, which resembles punishment in reputation models, is more pronounced for funds managed by large families. We also show that large-family funds behave like relationship lenders and that firms are less likely to call their bonds when there are more of them in their bondholder base. Finally, firms that develop a reputation for calling aggressively incur higher subsequent borrowing costs. Our results provide evidence of relationship lending in bond markets sustained through firm reputation.
Information Externalities in Opaque Credit Markets, (with Mahyar Sefidgaran)
~OxFIT, BSE Summer Forum, Finance Forum, Midwest Theory, Vienna Festival of Finance Theory, Cambridge Corporate Theory Symposium
In many opaque markets plagued by asymmetric information, e.g., interbank and OTC markets, firms borrow from many lenders at once and individual contracts are not observable to other lenders. We identify a novel information externality in a model based on this type of setting. Due to adverse selection, lenders use their private information to adjust the size of loans rather than the prices they offer to borrowers. Each lender’s individual rationing decision creates an information externality that raises both lender profits and the efficiency of trade. This information externality occurs even though information is not shared and lenders compete with each other. The model provides a microfoundation for adverse selection-based peer monitoring in opaque credit markets and has implications for their optimal structure.
Margin Lending and Information Production
~OFR PhD Symposium on Financial Stability, Paris December Meeting, AFBC, MFA
I propose a new explanation for the widespread use of margin loans by financial institutions. Margin loans prevent lenders from producing too much information about borrowers' assets at origination by forcing borrowers to liquidate prior to maturity following a negative shock. However, while margin loans deter information production in the primary market, they induce it in the secondary market, depressing asset prices. The model can rationalize policies that curb margin lending in normal times and support it in periods of market stress.
"Distortions Caused by Lending Fee Retention": Reuters, Canadian Investment Review