Publications

Macro Sentiment and Hedge Fund Returns

Professor Tim Simin published “Macro Sentiment and Hedge Fund Returns” in the Journal of Banking & Finance.

Abstract: In heterogeneous agent models, systematic excess demand for risky assets, generated by sentiment, can lead to a sentiment risk premium for sophisticated arbitrageurs. Utilizing media-based sentiment on economic growth, inflation, unemployment, and sociopolitical conditions, we create a macro sentiment index (MSI) and show that hedge funds with negative exposure to MSI generate significantly higher abnormal returns. The predictive power of MSI betas remains after controlling for fund characteristics and other risk and uncertainty factors. We show that a tradable version of MSI meets the necessary criteria to be considered a state variable in the ICAPM for actively managed fund portfolios.

Relationship Capital and Financing Decisions

Professor Thomas Geelen published “Relationship Capital and Financing Decisions” in the Management Science.

Abstract: Lending relationships matter for firm financing. In a model of debt dynamics, we study how lending relationships are formed and how they impact the joint choice of leverage and debt maturity, thereby rationalizing recent empirical findings and generating new predictions. In the model, lending relationships evolve through repeated interactions between firms and debt investors. Stronger lending relationships lead firms to adopt higher leverage ratios, issue longer term debt, and raise funds from nonrelationship lenders via syndicated loans or bonds issues when relationship quality is sufficiently high. Debt contracts involving nonrelationship investors have longer maturity than those exclusively issued to relationship investors.

Hacking Corporate Reputations

Professor Stefan Lewellen published “Hacking Corporate Reputations” in the Review of Finance.

Abstract: We exploit unexpected corporate data breaches to study the loss and repair of corporate reputation. Reputation loss decreases equity and brand values, increases customer churn, and prompts more negative media coverage. Firms repair their reputation by increasing their charitable donations and have CSR scores that are more than 0.5 standard deviations higher. They increase political contributions, employee wages, and IT investment. These actions are targeted to stakeholders that are particularly important or in situations that are particularly salient to their stakeholders. We observe similar dynamics of reputation loss and repair following the release of negative news about firms’ social behaviors.

Biodiversity and Local Asset Values

Professors Jess Cornaggia and Peter Iliev published “Biodiversity and Local Asset Values” in the Review of Finance.

Abstract: We provide a national-scale benchmark on how biodiversity is priced in local assets in the United States. Property prices increase with local biodiversity loss, likely reflecting development-driven monetization. In contrast, at the regional level, greater species richness correlates with higher asset values, indicating that remaining regional biodiversity is viewed as an amenity. Over time, the premium from biodiversity loss weakens, while the valuation of regional biodiversity strengthens. The link between biodiversity and property prices is higher when public attention to biodiversity is high. Together, these results highlight a shift in market preferences: biodiversity is increasingly recognized and priced as natural capital since the turn of the century. This evidence shows how ecological conditions are capitalized into local asset values and highlights implications for conservation policy and sustainable land use.

AI-Powered (Finance) Scholarship

Professor Mihail Velikov published “AI-Powered (Finance) Scholarship” in the Journal of Economic Literature.

Abstract: This paper describes a process for generating academic papers using large language models (LLMs) and demonstrates this process's efficacy by producing hundreds of complete papers on stock return predictability, a topic well-suited for our illustration. After mining over 30,000 potential return predictors from accounting data, we generate template reports for 95 signals passing rigorous criteria from the Novy-Marx and Velikov (2024) Assaying Anomalies protocol. These templates detail signal performance predicting returns using a wide array of tests and benchmark performance against more than 200 documented anomalies. Finally, for each template we use state-of-the-art LLMs to generate multiple complete versions of academic papers with distinct theoretical justifications for the observed return predictability, incorporating citations to literature supporting their respective claims. This experiment illustrates the potential of artificial intelligence (AI) for enhancing financial research efficiency, but also serves as a cautionary tale, illustrating how it can be abused to industrialize hypothesizing after results are known (HARKing).