To understand the dynamics of investors’ asset demands, we develop a general-equilibrium model driven by a single latent variable: heterogeneity in investors’ confidence about mean endowment growth. The model predicts persistent heterogeneity in asset demands and concentrated portfolios. Consistent with the data, limited confidence reduces investors’ demand elasticities and makes stock prices excessively volatile—driven by latent demand rather than observable characteristics. The underlying economic mechanisms are driven primarily by investors’ desire to hedge changes in future beliefs instead of current disagreement. Finally, consistent with survey data, investors’ expectations correlate positively with past returns and negatively with future returns.

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 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.

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.

I investigate how index membership affects firms’ product-market strategy. After plausibly exogenous index inclusion, firms gain market share by reducing product prices, giving better trade credit, and increasing sales and marketing expenses. Firms reduce prices for products with low market share and higher switching costs and habits. This comes at the cost of lower profitability, which increases in subsequent periods. Further analysis suggests that managerial learning about an improved funding environment from the post–index inclusion stock price increase is the underlying channel that leads to the observed increase in investment to gain market share. A model further corroborates these findings.

We examine how macroeconomic expectations affect consumer decisions, using an experiment with 2,872 credit card customers at a large commercial bank. In the experiment, participants are randomized into receiving expert forecasts of inflation and the nominal exchange rate. We find that forecasts shift inflation and exchange rate expectations, but do not change spending or self-reported consumption plans as predicted by standard models of intertemporal choice. Results from a supplementary survey experiment suggest that consumers are sophisticated enough to anticipate nominal rigidities and reduce spending on durables for precautionary reasons, counteracting the effects predicted by standard models of intertemporal optimization.

Stock prices aggregate investor information about investment opportunities and reflect managerial performance. These dual roles may be in tension: when prices are more informative about investment opportunities, they may be less effective at incentivizing managerial effort. This tradeoff has novel consequences. Lower information costs can lead to both more efficient investment but lower firm value. The principal may strictly prefer to delegate investment to a manager who has no informational advantage and makes ex-post inefficient choices. Investment in diversifying and (ex-ante) negative NPV projects mitigate agency problems. Finally, standard measures of price efficiency provide an incomplete picture of firm value.