This study examines the impact of recreational marijuana laws (RMLs) on firm-level employment using an imputation-based difference-in-differences (DiD) approach across U.S. states. RMLs significantly reduce employment, particularly among firms with high-skilled labor, strong union presence, permissive corporate cultures, and in states with greater dispensary density. Alternative explanations—including economic crises, COVID-19, fiscal changes, labor regulations, and related policies such as smoking bans and right-to-work (RTW) laws—are systematically ruled out through a series of placebo and robustness tests. RMLs also reduce investment, sales growth, and innovation, suggesting that legalization introduces labor-related frictions with broad implications for firm performance and long-term dynamism.

We administer a theory-driven, lab-in-the-field experiment to study the disposition effect among financial professionals. Our novel design identifies, at the individual participant level, key behavioral drivers of the disposition effect: reference-dependent risk attitudes (“tastes”), second-order uncertainty attitudes (including “ambiguity”), and subjective likelihood assessments (“beliefs”). Among the 237 professionals in our sample, 34% exhibited the disposition effect, which seems to be primarily driven by non-Bayesian beliefs. Our experimental results suggest that, when faced with new information about their asset’s performance, financial professionals failed to update their beliefs sufficiently leading them to sell the asset that gained (lost) value more (less) readily.

Equity returns follow a pronounced V-shape pattern around the onset of recessions. They sharply drop into negative territory just before business cycle peaks and then strongly recover as the recession unfolds. Recessions are typically preceded by a flat yield curve. Probit models relying on the term spread as a predictor therefore time the beginning of recessions well. We show that model-implied recession probabilities based on the term spread strongly improve equity premium prediction in- and out-of-sample and outperform several benchmark predictors. Correcting for a structural break in the mean of the term spread in 1982 further strengthens the forecast performance.

We investigate how the government, as a customer, affects a supplier’s environmental policies. We find that government contractors have significantly lower levels of toxic pollution. Exploring the mechanisms, we show that government contractors reduce pollution by strengthening internal environmental governance and increasing investments in pollution abatement. Further analysis rules out alternative explanations related to reduced economic activities and financial constraints. Overall, our results highlight the government’s important role in disciplining corporate environmental misbehavior.

We show that banks use industry knowledge acquired through corporate lending in mortgage lending, a phenomenon we refer to as the “industry expertise channel.” Specifically, banks that specialize in particular industries expand their mortgage lending activity in regions where those industries are concentrated. The impact of industry expertise increases with information asymmetry and borrower risk. In addition, mortgages originated from this channel contain more soft information and perform better. The effect of the channel increases after unexpected industry distress and the 2008 financial crisis, suggesting that the effect is likely causal.

Using model-free skewness measures that exploit the asymmetry in semivariances and option data from the over-the-counter currency market, we find that buying currencies with a high skewness risk premium (SRP) and selling currencies with a low SRP generates high returns and a Sharpe ratio. Asset pricing tests—which control for omitted variables and measurement errors—show that a SRP factor enters the currency pricing kernel and is central to the pricing of risks inherent in a broad currency cross section of 60 portfolio excess returns. These results imply that skewness risk is a strong and priced source of currency risk.

We study removals of “credit ceilings,” quantitative limits on bank credit supply imposed by many countries until the 1980s. Exploiting differences in loan types affected, we find that these removals predict increases in bank credit, residential investment, house prices, and bank stock prices, followed by reversals, recessions, and banking crises. These effects are separate from those of other financial deregulations. Overall, our results suggest that credit supply shocks do not simply amplify existing fragilities but can initiate economic boom-and-bust cycles on their own.

After medical marijuana legalization (MML) by U.S. states, firms’ cost of equity (COE) decreases, especially for those with more growth opportunities, higher productivity, or a more skilled workforce. This policy change also reduces firm risk and leads to an increase in labor supply through increased labor force participation, employment, hours worked, and net migration. Further, home prices rise after MML, reflecting increased local housing demand due to a growing supply of workers. These findings align with theoretical models that link asset prices to labor markets and suggest that MML can lower firms’ COE by mitigating labor search frictions.

Using the near universe of online job postings from 2007 to 2021, we construct a firm-level metric of labor market power. We find that firms with higher labor market power tend to have higher financial leverage. Our findings are not driven by product market competition or correlated labor market characteristics. The evidence is less pronounced among firms hiring in occupations with high labor mobility and skill transferability. To establish causality, we exploit the establishment of Amazon HQ2 in Crystal City as a shock to the labor market power of local firms and show consistent findings with our baseline results.

This article studies the importance of corporate boards through a learning model in which capital markets learn about incoming directors’ quality. The model’s predictions are tested across a large sample of director appointments. Estimates show that governance-related uncertainty accounts for about 10% of stock return volatility when a new director joins. The learning framework provides a theoretically grounded approach to identify when directors matter more to investors. The analysis shows that director importance varies with board composition and firm attributes: Investors perceive directors as more important on boards with greater generational diversity, in smaller firms, and firms with higher knowledge capital.

This article examines how the entry of commercial lenders (CLs) transforms microfinance markets, focusing on borrower outcomes and market-wide spillovers. Using detailed credit registry data, we show that increased competition improves loan terms for both graduating and staying borrowers, generating sustained benefits. Our setting also allows us to document what happens when entry fails and entrants retreat following a crisis. Despite increasing defaults, borrowers who graduate to banks experience long-term gains, particularly through lower borrowing costs. Our findings highlight the broader benefits and risks of fostering competition in microfinance, providing valuable insights for policymakers and financial inclusion initiatives.

Employing a cross-country sample, we examine how a population’s underlying cultural values help explain gender compensation variation across corporate executives. The results show that the cultural differences, embedded in societies long before the board’s compensation decisions, have significant explanatory power for the observed gender gap in executive compensation. Using an Oaxaca–Blinder decomposition combined with variables previously shown to be fundamental determinants of executive compensation, we find that adding cultural measures increases the model’s explanatory power of the gender compensation gap from 44% to 95%. We use further identification strategies to support causal inference.

We argue that cross-ownership increases the amount of private information in stock prices, enhancing the ability of stock prices to provide feedback to managers. Consistent with this argument, we find greater cross-ownership heightens a firm’s investment-q sensitivity. This effect is stronger for firms with a lower propensity for voluntary disclosure and for firms whose managers hold less private information. Furthermore, we find that cross-ownership is negatively associated with the sensitivity of a firm’s investment to its peers’ stock prices. Additionally, cross-ownership has a stronger impact on the investment-q sensitivity when measured among investors who trade more actively in the firm’s shares. By using financial institution mergers as an identification strategy, we strengthen the causal inference. Overall, our results suggest that cross-ownership helps increase revelatory price efficiency (RPE), potentially leading to more efficient corporate decisions.

In a setting with a tradable value-weighted market index, ambiguity-averse investors do not trade, and the index is not mean–variance efficient. But when a passive fund offers the risk-adjusted market portfolio (RAMP), whose weights depend on information precision as well as market values, investors share risk via index investing and effectively hold the same portfolios as in the economy without model uncertainty. This follows from a new Information Separation Theorem: equilibrium portfolios are the sum of RAMP, which is the optimal portfolio conditional on public information, and their optimal private-information-based portfolios. RAMP is in equilibrium mean–variance efficient.

This article studies how ESG and conventional mutual funds trade stocks during the COVID-19 crash. Both fund types trade individual stocks similarly: Net purchases of ESG stocks are less sensitive than other stocks to fund flows pre-crash, but sensitivities increase for all stocks during the crash. In contrast, ESG funds’ aggregate net purchases are less sensitive than those of conventional funds during the crash. This difference is due to ESG funds’ portfolio tilt toward the less flow-sensitive ESG stocks. There is no evidence of an ESG clientele effect in trading decisions, as both fund types trade individual stocks similarly.

Existing portfolio combination rules that optimize the out-of-sample performance under parameter uncertainty assume multivariate normally distributed returns. However, we show that this assumption is not innocuous because fat tails in returns lead to poorer out-of-sample performance of the sample mean–variance and sample global minimum-variance (GMV) portfolios relative to normality. Consequently, when returns are fat-tailed, portfolio combination rules should allocate less to the sample mean–variance and sample GMV portfolios, and more to the risk-free asset, than the normality assumption prescribes. Empirical evidence shows that accounting for fat tails in the construction of optimal portfolio combination rules significantly improves their out-of-sample performance.

Earlier research finds correlation between sentiment and future economic growth, but disagrees on the channel that explains this result. We shed new light on this issue by exploiting cross-sectional variation in country size and market efficiency. We find that sentiment shocks in the largest advanced economies increase economic activity, but only temporarily and without affecting productivity. Conversely, sentiment shocks in smaller or less advanced economies predict prolonged economic growth and a corresponding increase in productivity. The results support the view that sentiment can create economic booms, although only in economies where sentiment and fundamentals are harder to disentangle.

Predictive regressions of market returns on option-implied moments measured before pre-scheduled FOMC meetings show that tail risks play an important role in understanding the market risk premium around FOMC announcement days. Skewness and kurtosis, which capture investors’ expectations of the tails of the return distribution, robustly predict post-FOMC returns both in-sample and out-of-sample. The predictability lasts up to 1 week and is stronger for expansionary monetary policy shocks. The signs of the corresponding risk premiums are consistent with economic intuition, illustrating the role of periods with high risk premiums to confirm theoretical predictions.

The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via maximum likelihood estimation (MLE) and filter the short-run and long-run components of disaster risk. Our estimation results are independent of the stock market and serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.