We develop a model-free measure of the variance premium by constructing option portfolios whose returns are highly correlated with realized stock variance. This effectively decomposes returns into realized variance minus implied variance. We apply this decomposition to document a novel quarterly cross-sectional continuation pattern in both realized variance and implied variance of individual stocks. Implied variance underanticipates the seasonality of realized variance, so options that performed well at quarterly lags continue to earn high returns in the future. Quarterly periodicity in realized stock variance only occurs on days with analyst earning revisions, suggesting an informational channel for this pattern.

Generative artificial intelligence (AI) is increasingly consuming and producing huge amounts of data. We propose a social learning model of AI, emphasizing a data-AI feedback loop: data quality affects AI productivity, which influences AI adoption and, consequently, the composition (AI versus human-generated) and quality of future data. Calibrated to evidence on synthetic training loops, the model predicts hump-shaped labor dynamics—short-term displacement that partially reverses as data quality deteriorates. A Grossman–Stiglitz-style externality emerges: AI adopters free-ride on the human-generated actions that supply the novel information on which AI itself relies. In a competitive market, AI should be taxed to correct the data-quality externality; a concentrated AI industry overcorrects, making a subsidy optimal.

We conduct the first large-scale study of how different stakeholder groups assess corporate culture and quantify the economic implications of those differences. We employ generative AI to analyze analyst reports, call transcripts, and employee reviews, and organize the extracted information into a knowledge graph that links a culture type to its perceived causes and effects. We demonstrate that the divergence in different stakeholder groups' assessment of culture aligns with their distinct roles and economic incentives. Moreover, we show that analysts' culture analyses are incorporated into stock recommendations and target prices, and investors react to divergence in stakeholders' assessment of culture.

I document a significant gender gap in entrepreneurs’ access to early-stage equity financing. Using unique administrative data on French startups, I show that this gap is particularly pronounced in male-dominated sectors. Controlling for a comprehensive set of entrepreneur and startup characteristics—including demographics, backgrounds, and motivations—accounts for 42% of the average gender gap. However, in male-dominated sectors, 80% of the gap remains unexplained. Growth-oriented female entrepreneurs rely more heavily on bank loans, substituting equity with debt. Finally, VC-backed female-founded startups outperform their male counterparts in male-dominated sectors. These findings suggest that context-dependent stereotypes influence equity financing decisions.

We develop a dynamic theory of “Partial Equilibrium Thinking” (PET), which micro-founds time-varying return extrapolation: extrapolative beliefs are present at all times, but only sometimes manifest themselves in explosive ways. We formalize the distinction between normal times shocks and “displacement shocks,” and study their interaction with extrapolative beliefs. In normal times, PET generates constant extrapolation and momentum. After a displacement shock that increases uncertainty, PET leads to stronger and time-varying extrapolation, triggering bubbles and endogenous crashes. Our theory sheds light on both market dynamics in normal times and Kindleberger’s narrative of bubbles within a unified framework.

We show that positive flows to active mutual funds with high recent returns partially reverse at longer horizons. This outcome is robust across a broad range of alternative specifications. Reversal occurs from greater outflows associated with high prior returns, not reduced inflows. We test theories to explain the reversal: investment life cycles, tax loss selling, and a behavioral “disappointment” hypothesis based on investors’ overreaction to positive returns. While both tax loss selling and short investor life cycles can contribute, the evidence supports a role for investor disappointment, whereby investors redeem their capital when return performance fails to meet expectations.

We build a novel data set of time-stamped corporate decarbonization commitments—green pledges—for U.S. public firms by classifying news articles with large language models and human validation. Firms announcing green pledges tend to be larger and browner than other firms, both within and across industries. Green pledges significantly raise stock prices, consistent with a reduction in the carbon premium, and predict sizable declines in future carbon emissions and emission intensities. These effects tend to be strongest for firms in brown industries. Green pledges thus appear credible, convey relevant new information to investors, and provide meaningful financial incentives to decarbonize.

We study privatization and corporate ownership in transportation infrastructure, focusing on global airports. Privatization in general does not improve performance. However, private equity (PE) ownership has strong and persistent positive effects on efficiency, volume, and quality, with positive externalities for the local economy. We address selection using close auctions where PE and non-PE firms bid. PE funds expand physical capacity and encourage larger planes while adding low-cost carriers and international routes. They are especially effective in countries with more corruption or state-owned flag carriers. In contrast, the poorer performance of non-PE acquirers may reflect private benefits and historical state ties.

A fundamental concern about green investing is that it may crowd out political support for public policies addressing negative externalities. We examine this concern in a preregistered experiment conducted shortly before a real referendum on a climate law in Switzerland. We find that offering an opportunity to invest in a climate-friendly fund does not reduce individual support for climate regulation, measured by political donations and voting intentions. A replication of the experiment in the United Kingdom yields similar results. Our estimates reject a crowding-out effect, suggesting instead a modest crowding-in effect of green investing on political support for green policies.

Government intervention in the financial market through its own trading fundamentally changes the market’s structure, function, and outcome. I develop a general equilibrium framework to study the impact of government trading on investor welfare. I show that with incompleteness and information asymmetry, the market equilibrium is in general suboptimal, and government intervention can potentially improve investor welfare even with no additional information. However, the actual welfare impact of government intervention is sensitive to the details of the economy and the policy design. Popular policy targets such as price stability, market liquidity, and informational efficiency can have different welfare implications.