Knowledge that Transforms

We study how firm-level carbon emissions affect bank lending and real outcomes in a sample of global firms with syndicated loans. We exploit bank-level climate commitments as firm-level shocks to lending relationships, using firms' prior credit exposures to identify credit supply effects. Firms with higher emissions that previously borrowed from committed banks receive less bank credit. Evidence from lending volumes, prices, and within-firm-time loan-level data indicates a supply-side shift away from high-emission firms, not explained by borrower risk. Affected firms reduce debt, leverage, size, and investment, yet we find no reduction in future emissions, instead documenting evidence consistent with greenwashing.

Why do so few women become CEOs? To understand this glass ceiling, we estimate a dynamic model of the CEO gender decision, which contains perceived gender productivity differences, search costs reflecting limited female labor supply, and employer disutility from discrimination. The key factor is the shortage of suitable female candidates, as boards prefer hiring women, and productivity differences between genders are minimal. We find no evidence of a glass cliff in which women become CEOs just as firms are failing. While better governance is associated with women becoming CEOs, the importance of limited female labor supply is unrelated to governance.

Our study provides the first causal test of classical and behavioral asset pricing models that incorporate skewness pricing. In line with these models, our experimental markets show that skewness is systematically priced. Our findings also reveal that positively skewed assets available in small supply exhibit negative expected returns, which is consistent with prospect theory, but not with expected utility models. Furthermore, in line with the mechanism underlying prospect theory, we show that the negative returns of the positively skewed asset are most pronounced during market sessions where traders overweight the low probability of receiving a large payoff.

Traditionally, fund managers cast votes on behalf of fund investors. Recently, there is a shift toward “pass-through voting,” with funds offering investors a choice: delegate votes to the fund or vote themselves. We develop a framework to study the implications of voting choice. While it helps reflect heterogeneous investor preferences, it also shapes the informational content of the vote, and these forces can conflict. When interests are aligned, voting choice improves information aggregation and investor welfare. With preference heterogeneity or costly information, however, it can make investors worse off by weakening informed.

We present and discuss the papers in this special issue. We use the themes that these papers study to illustrate interesting new directions that are being pursued in the household finance literature and highlight open questions on which more work is needed.

Analyzing approximately 2,000 applicants to a U.K. seed fund, this study examines how venture capital (VC) due diligence affects startup outcomes independent of funding decisions. Leveraging random reviewer assignment, we find that due diligence increases 2-year growth but lowers continuation rates among nonfunded applicants, reflecting a dynamic of accelerated scaling or exit. Evidence points to a learning mechanism: due diligence exposes founders to advanced website technologies, prompting capability building in digital skills. Firms selected for due diligence adopt these technologies, even before raising external capital. The findings highlight VC due diligence as a formative process influencing startups beyond the funded few.

We document that firms exhibit “excess” commitment to R&D projects and examine its consequences for innovation outcomes. Using detailed data on pharmaceutical firms’ clinical trial projects, we find that trial delays, empirically uncorrelated with multiple project-quality measures, substantially reduce firms’ subsequent project-termination propensity. This result remains robust when we use variation in clinical trial site congestion to instrument for unexpected delays. Excess commitment intensifies when CEO compensation has greater stock-price sensitivity and the CEO is responsible for the project’s initiation. Our findings have broader implications: delay-driven commitment reduces new drug project initiations, with further evidence suggesting efficiency losses for firms.

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the “implementable efficient frontier.” While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”

Option wholesalers specialize in purchasing and executing against retail option order flow. Orders are internalized via auctions (which provide price improvement) and the limit order book. Designated market makers (DMMs) have a key advantage in internalizing limit order book trades: they obtain the first five contracts of any order they bring to an exchange where they are a DMM. We exploit variation in DMM assignments and allocation rules to highlight how these rules create a barrier to entry in option wholesaling that does not exist for equity wholesaling, protecting wholesaler profits and high option PFOF.