We provide the first analysis of the risk exposure and risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. We introduce a dataset of impact fund cash flows and exploit distortions in VC performance measures to characterize risk profiles. Impact funds have a lower market β than comparable private market strategies. Accounting for β, impact funds underperform the public market, though not necessarily more so than comparable strategies. We consider alternative pricing models, accounting for sustainability and emerging markets risk. We show investors’ wealth portfolios and taste change the perceived financial merit of impact investing.

Financial event studies, ubiquitous in finance research, typically use linear factor models with known factors to estimate abnormal returns and identify causal effects of information events. This paper demonstrates that when factor models are misspecified—an almost certain reality—traditional event study estimators produce inconsistent estimates of treatment effects. The bias is particularly severe during volatile periods, over long horizons, and when event timing correlates with market conditions. We derive precise conditions for identification and expressions for asymptotic bias. As an alternative, we propose synthetic control methods that construct replicating portfolios from control securities without imposing specific factor structures. Revisiting four empirical applications, we show that some established findings may reflect model misspecification rather than true treatment effects. While traditional methods remain reliable for short-horizon studies with random event timing, our results suggest caution when interpreting long-horizon or volatile-period event studies and highlight the importance of quasi-experimental designs when available.

We study how relationships between lenders and venture capital (VC) investors shape venture debt deals and startups’ post-deal outcomes. We develop a model that highlights two competing mechanisms of this relationship: an information channel in which lenders benefit from VC certification and a market power channel in which lenders extract rents through bargaining power. Using a comprehensive dataset on global venture debt, we test these channels at different stages of venture debt. At entry, the relationships mitigate asymmetric information and increase the likelihood of obtaining venture debt. In the investment stage, relationship lenders reduce hard restrictions while charging higher spreads. Post-deal, relationship-backed startups are more likely to secure subsequent VC funding and successful exits by reallocating innovation toward commercially salient and safer projects. Our findings highlight that VC-lender relationships alleviate information frictions and allow rent extraction, yet ultimately facilitate value creation in high-growth ventures.

We study how an acquirer's public visibility affects antitrust enforcement in mergers and its real economic consequences. Using a novel dataset linking FTC and DOJ inspection outcomes to news coverage of merger parties, we find that a 10 percent increase in the acquirer's share of industry news coverage raises the likelihood of being flagged by 1.5 to 3 percent. We establish causality using geographical proximity to media outlets as an exogenous source of variation in news coverage. We interpret these results through a political‐accountability framework: higher visibility raises the reputational stakes of enforcement, leading regulators to challenge more visible acquirers. Consistent with this accountability framework, the visibility gradient strengthens during congressional appropriations hearings and disappears in lame-duck periods. It is also substantially stronger in consumer-facing industries, where accountability pressures are highest. Finally, visibility-driven scrutiny has real effects: overlooked low-visibility deals gain market power, while flagged acquirers expand inefficiently and face a higher financial burden. Our results highlight how public salience distorts merger review and generates persistent post-merger inefficiencies.

This paper examines the biases in managerial expectations as a function of firms' financial conditions. Using microdata from the Duke CFO Survey and managerial guidance, I document pervasive overreaction to news across forecasts of firm profitability, operations, and financing decisions, and even the aggregate market returns. Forecast errors are predictable from past realizations, consistent with extrapolative belief formation. I demonstrate that these biases are context-dependent: overreaction is stronger among financially constrained firms. Using exogenous shocks to earnings and financial conditions, I show that constrained managers overreact more strongly to negative earnings news, while managers with improved financial conditions exhibit weaker overreaction to positive earnings news. Evidence from moderate earthquakes as exogenous emotional shocks supports an affect-based mechanism: negative emotions distort managerial expectations, and these expectations, in turn, influence managers’ future investment decisions. Overall, the results uncover novel affect-driven channels through which managerial expectations amplify distortions in firm investment.

We document a relationship between memory-based models of beliefs and a general class of kernel methods from the statistics and machine learning literature. Motivated by this relationship, we propose a new form of memory-based beliefs which aligns more closely with the state of the art in the machine learning literature. We explore this approach empirically by introducing a measure of “narrative memory”– similarity between states of the world based on similarity in narrative representations of those states. Using textual embeddings extracted from conference call transcripts, we show that our estimates of memory-based beliefs explain variation in errors in long-term growth forecasts of IBES analysts. We conclude by discussing implications of this relationship for the literature on memory-based models of beliefs.