Knowledge that Transforms

We show that leading investor disagreement proxies have near-zero cross-correlation – making disagreement pricing inferences challenging. We develop a frictionless model that generates overpricing from latent disagreement, jointly rationalizes major proxies, and generates new testable predictions. Equilibrium interrelationships motivate a new disagreement measure, DIS, which is more predictive of returns cross-sectionally than major proxies and more consistently predictive across macroeconomic regimes. We provide evidence that the friction-based channel of Miller (1977) and a frictionless mechanism coexist and are empirically separable. DIS also provides a micro-founded, disagreement-based account of the IVOL puzzle, absorbing IVOL’s predictability while revealing its regime and friction dependence.

We develop a complete-market production economy in which heterogeneous beliefs about productivity growth affect both asset prices and real activity. Firms must hire workers before productivity is realized, so operating leverage increases the exposure of profits to productivity risk. Because the firm’s discount factor depends on a wealth-weighted aggregation of investor beliefs, shifts in optimism compress risk premia and raise employment, while reversals trigger contractions. This mechanism generates an equilibrium feedback between asset prices, hiring, and risk exposure. A taxonomy of belief systems shows that only extrapolative beliefs produce amplification and endogenous risk build-up. Disciplined by survey expectations, the model jointly matches key asset-pricing, trading-volume, and business-cycle moments.

Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consistent large language models, ChronoBERT and ChronoGPT, which incorporate only the text data that would have been available at each point in time. Despite this strict temporal constraint, our models achieve strong performance on natural language processing benchmarks, outperforming or matching widely used models (e.g., BERT), and remain competitive with larger open-weight models. Lookahead bias is model and application-specific because even if a chronologically consistent language model has poorer language comprehension, a regression or prediction model applied on top of the language model can compensate. In an asset pricing application predicting next-day stock returns from financial news embeddings, we find that ChronoBERT and ChronoGPT’s real-time outputs achieve Sharpe ratios comparable to a much larger and potentially biased Llama model. Our results demonstrate a scalable, practical framework to mitigate training leakage, ensuring more credible backtests and predictions across finance and other social science domains.

We study branch openings and closures in U.S. banking. Both are concentrated where depositors are rate-sensitive and place less value on physical access, patterns driven by financially sophisticated households with greater digital adoption. The effects are strongest for large banks, with lending playing a lesser role. Incumbents retain branches where depositors are less sensitive and more reliant on proximity; entrants avoid such markets because sticky customers are difficult to attract. The Pandemic accelerated closures while allowing banks to retain deposits through online channels. Overall, depositor sophistication — through rate sensitivity and reduced branch reliance — drives modern branch restructuring.

Concerns about climate risk suggest it should affect risk assessment and pricing of corporate securities, particularly for firms facing potential regulatory restrictions. Employing a shock to expected climate regulations, we find support for this hypothesis given our evidence that climate regulatory risks causally affect bond credit ratings and yield spreads. Moreover, a structural credit model indicates the increased spreads for high carbon issuers, especially those located in stricter regulatory environments, derive from changes in firms’ asset volatilities rather than asset values, highlighting that regulatory uncertainty affects security pricing. The results have important implications for corporate decisions, portfolio management, and policymaking.

This paper focuses on actively managed bond mutual funds, whose performance is most sensitive to monetary policy shocks. We document significant and persistent FOMC-day outperformance by macro-active bond funds, which is particularly pronounced during periods of heightened macroeconomic disagreement and attention. Consistent with their forecasting ability, these funds’ pre-FOMC portfolio duration adjustments can predict FOMC-day monetary policy shocks beyond the information offered by economic data and asset returns. Furthermore, we show that this macro-investing skill extends to GDP and CPI announcements and is consistently shared across various fund styles within fund families.

We develop a theory of stakeholder governance examining how green stakeholders influence organizations. Conflicts of interest arise from heterogeneous green preferences and intensify as they diverge. Changes in green preferences can generate unexpected outcomes by shifting control rights: greener stakeholders may reduce organizational sustainability, while browner ones might enhance it. We show that a top-down approach consistently improves organizational sustainability, while a bottom-up approach can harm it. We also uncover an asymmetry in compensation and delegation decisions between green and brown principals, driven by the public good nature of social payoffs. These findings have implications for manager–employee, investor–entrepreneur, and board–CEO relationships.

I study how bankruptcy law firm advertisements affect household credit, exploiting the borders of local TV advertisement media markets. I document a significant advertising effect on filing rates and subsequently show that ad exposure is associated with better financial outcomes. I then characterize how ad-induced filers are different from existing filers in terms of case, financial, and credit variables. I find that ad-induced filers are more likely to receive a discharge, file with lawyer representation, and be first-time filers. I interpret these findings as evidence that bankruptcy law firm advertisements help mitigate frictions in the bankruptcy decision.