This entry pertains to the UT Dallas 2025 Fall Finance Conference held October 3–4, 2025.
I flew into Dallas Friday morning and had a chance to meet one of my closest college friends, who grew up in Plano and is now an accounting faculty at Berkeley, before heading to the conference venue. UT Dallas is strikingly large, with sprawling undergraduate schools feeding into the business school.
The conference itself was hosted at the Jindal School of Management (JSOM II), with a full slate of paper sessions, a PhD poster showcase, and (deliberately designed) plenty of opportunities for informal exchange.
Eva Steiner (Penn State) presented “The Biodiversity Protection Discount” which quantifies the land value costs of biodiversity protections using parcel-level data and regression discontinuity. A natural question is how different this is from generic land-use regulation? The authors argue biodiversity is unique because it restricts options to develop even in relatively undeveloped areas, rather than just tightening density or zoning rules. --> mention that it would be good to connect a bit more to the discussion of biodiversity's implications for growth, for example
Jerry Hoberg (USC) presented “Haven’t We Seen This Before? Return Predictions from 200 Years of News.” The basic premise is that current economic states often resemble past states. Using a 200-year corpus of 210 million news articles, the authors build “SeenItRet,” a predictor that averages the returns following the 25 most historically similar months. Remarkably, SeenItRet forecasts U.S. stock returns for up to two years, with an annualized impact of 4–7% for a one-standard-deviation change
What is especially interesting is that nearest-neighbor methods are typically weak in high-dimensional settings, but here they perform strongly—perhaps because “economic states” are, in practice, low-dimensional. The paper’s interpretability results highlight which themes drive predictability at different horizons: momentum and liquidity concepts at short horizons, deregulation and war discourse at medium horizons, and inflation and bonds for long horizons.
One obvious extension is to integrate with quantitative data: while the text corpus captures rich narratives, augmenting with numeric state variables (P/E ratios, spreads, macro indicators) could refine the measure. Also, a higher dimensional embedding approach could capture synonyms and context better than unigram-based curation.
Shan Ge (NYU Stern) presented “How Do Financial Conditions Affect Professional Conduct? Evidence from Opioid Prescriptions.” The idea is that doctors prescribe more opioids when they suffer wealth shocks via housing, and much of the identification comes from office-year FE, which assuages a lot of concerns about patients’ demand. The design is reinforced by showing similar patterns when restricting to providers who live far from their practice, further ruling out neighborhood demand effects.
The deeper puzzle is why providers did not already maximize these incentives. The paper argues that negative wealth shocks tighten the objective function, making doctors more willing to exploit patient demand through short-acting opioid prescriptions, which boost repeat visits and patient satisfaction scores. This interpretation opens up questions about internal allocation: office managers or partners may decide which doctors see which patients, potentially channeling wealth-shocked providers toward patient segments more sensitive to opioids.
Dan Luo (CUHK) presented “Corporate Finance Through Loyalty Programs.” Typically, loyalty programs are viewed through the lens of industrial organization—as tools for boosting demand, sustaining markups, or raising switching costs. This paper instead takes a corporate finance perspective: loyalty programs function as a form of financing.
The paper’s model shows that firms can raise funds at relatively low cost by offering consumers “convenience” in redemption, which increases willingness to hold points. But unlike bonds, the flow of financing is endogenously tied to consumer spending patterns and the firm’s issuance policies. This makes LP financing especially powerful for high-value, low-frequency services (like flights and hotel stays), where overaccumulation of points is common.