Research

We analyze firms' IPO decisions using detailed financial data on US private firms. We find that firms with higher external capital needs are more likely to go public. Following the IPO, firms increase their investment and use of bank debt, resulting in leverage ratios close to their pre-IPO levels. Finally, newly public firms borrow from an expanded pool of lenders at improved terms, with a decrease in the within-firm dispersion in banks' private risk assessments. Our evidence is consistent with firms going public to improve their access to capital, which is facilitated by a reduction in asymmetric information.

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.

Media Coverage:

"Distortions Caused by Lending Fee Retention": Reuters, Canadian Investment Review

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