Conference Recap: Yale Junior Finance Conference

This entry pertains to the Yale Junior Finance Conference 2025 in New Haven.

Introduction

This conference was unique in that only assistant professors of a similar tenure stage are invited and are explicitly invited to present early-stage work. That creates a certain freedom: the discussions are candid, the work is preliminary, and the atmosphere is collegial. The agenda was compact with one full day of eight papers.

Paper Sessions

Bradford Levy (Chicago Booth) presented “A Framework for Generative Modeling of Financial Markets.” Inspired by architectures in image tokenization, the paper tries to build asset pricing models using data from order book and trade data (ICE). One question is how firm characteristics enter such models. Brad emphasized that finance might learn from AI research norms, where practical success often comes before theoretical elegance.
Suproteem Sarkar (Chicago Booth) presented “AI, Productivity, and Higher-Order Thinking.” Using data on adoption of coding agents, he showed that experienced workers adopt these tools more frequently, with short-run effects of higher productivity and similar quality. One point of discussion was that regarding technical debt: the delayed costs of code generated with assistance, which reminded me of a parallel to investment in eco-friendly technology where long-run outcomes also differ from short-run gains. Another striking aspect was a figure showing how adoption rates vary sharply across the experience distribution: the more experienced you are, the more likely you are to adopt.
Julian Terstegge (Michigan Ross) presented “Option Dealer Hedging Flows and Equity Returns Around Economic Events.” A new term I learned was that dealers are often “long vanna” (vanna refers to the sensitivity of option delta to changes in volatility). Dealer vanna predicts both the price–uncertainty correlation and stock returns around FOMC announcements. Much of the discussion centered on framing: what is the underlying source of this volatility, and what exactly is new about this uncertainty channel?
Julie Zhiyu Fu (Washington University in St. Louis) presented “A Bound on Price Impact and Disagreement.” I’ve seen earlier versions of this paper, so it was illuminating to see how it has evolved. One figure that stood out compared bounds using daily trade volume data versus quarterly data. But the daily-data bound is itself a bound on a bound, which makes interpretation tricky—and highlights how hard it is to convey these comparisons visually in a natural way.
Katrin Gödker (Bocconi University) presented “Trading Narratives.” The core question: what cognitive factors drive the decision to trade? Using a survey, the authors ask participants to provide narratives for their trades, at two different points in time. While the title emphasizes “narratives,” the paper is as much about the recall of narratives as their initial formation.
Susan Cherry (UT Austin) presented “Regulating Credit: Price Regulations and Lender Technologies.” I had seen this on the job market, and it’s an example of a paper that sheds light on an understudied but important segment of the market, particularly for households. One assumption that could benefit from more support is that of a fixed cost preventing better-informed lenders from switching into newly regulated segments, which is a key input into the narrative surrounding lender exits.
Olivia Kim (Harvard) presented “Character Loans.” The title draws from Jeremy Stein’s (2002) concept of “character loans”—credit decisions based on soft information rather than hard data. The paper asks: does human discretion add value, or merely amplify biases, when hard information is scarce? Olivia focused on Community Development Financial Institutions (CDFIs). The project is still early, but I thought a stylized example contrasting two loan applicants—one riskier, one safer, but only the riskier one approved due to discretion—would be a powerful way to open the presentation.