Knowledge that Transforms

Abstract 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. (JEL D72, D82, D83, G34, K22)

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.”

Abstract 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.

Abstract We study “Algorithmic Market Makers” (AMs) that use Q-learning algorithms to set prices for a risky asset. We find that while AMs successfully adapt to adverse selection, they struggle to learn competitive pricing strategies. This failure is driven by limited experimentation and noisy feedback regarding the profitability of undercutting a competitor. Consequently, an increase in AMs’ profit volatility tends to result in less competitive market outcomes. These features leave identifiable patterns: for example, AMs earn higher rents in the absence of adverse selection, and their bid-ask spreads respond asymmetrically to symmetric shocks to their costs.

Abstract Fraud indicators within the Paycheck Protection Program (PPP), a major COVID relief program, are highly geographically concentrated. ZIP codes and counties with high rates of suspicious PPP loans are strongly socially connected, with evidence that fraud spreads spatially over time through social networks. Individuals in suspicious social media groups have higher rates of PPP fraud, and socially connected ZIP codes frequently use the same specific FinTech lenders, consistent with social networks influencing detailed loan decisions. Our findings suggest that more proactive data analysis is needed for fraud prevention, detection, and prosecution to prevent the social spread of fraudulent schemes.

Abstract We quantify the impact of perceived cancer risk on housing values using widely advertised national reclassifications of chemical carcinogenicity in the United States. Combining these information events with an empirical design that compares changes in house values closer to affected toxic plants against those farther away isolates the effect of cancer risk news from other local factors. Focusing on plants previously emitting reclassified carcinogenic chemicals, we estimate a 1–2% decline in housing values within a 3-mile radius compared to those located farther away. The effects are stronger in areas with higher media presence underscoring the role of salience as a mechanism.