We propose a statistical model of heterogeneous beliefs wherein investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 13% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

We use variation in state corporate income tax rates to re-examine the relation between taxes and corporate leverage. Contrary to prior research, corporate leverage rises after tax cuts for small private firms. An estimated dynamic equilibrium model shows that tax cuts make capital more productive and spur the use of leverage. Tax cuts also produce more distant default thresholds and lower credit spreads. These effects outweigh the lower interest tax deduction and lead to higher optimal leverage choices, especially for firms with flexible investment policies. The presence of the interest tax deduction raises consumer welfare in equilibrium.

This article investigates sample selection bias in early-stage investment. We use comprehensive administrative data on the universe of new firm starts in Norway, allowing us to compare venture-backed firms with ex ante similar firms that do not receive venture funding. The valuation premium for venture backing is sizeable at firm birth and doubles over the first 5 years, implying a substantial upward bias in venture capital (VC) returns relative to comparable firms. In contrast, the premium for firms receiving multiple rounds of outside equity emerges only after the first year and remains significantly smaller than the VC premium throughout the firm life cycle.

We study the use of generative AI for firm‐specific financial analysis on the Seeking Alpha platform. After the initial launch of ChatGPT in November 2022, the share of AI‐generated articles rose sharply to 13.5% of all articles, then declined in late 2023 after Seeking Alpha equated the use of AI to plagiarism and announced a prohibition on its use. We organize our study around two questions: (1) Does AI use increase author productivity? and (2) does AI use have capital market consequences and ultimately affect the informational landscape? We find that authors who adopt AI become more productive, publishing more articles and covering more new firms than non‐adopters. Findings on AI article informativeness are more nuanced. On average, AI articles are less informative than human‐written articles, eliciting smaller trading volume and abnormal return responses. However, AI use leads to increased firm coverage and in turn to improved liquidity and faster price discovery. Our findings suggest that, while AI‐generated articles are currently perceived as less informative than human‐written articles, their comparatively low cost enables increased firm coverage and thereby improves the overall informational landscape.

In 2001 a large and sustained supply shock halted a heroin epidemic in Australia. We use drug offenses to identify individual opioid users and examine how the shock affected their mortality risks and criminal activity over the next eight years. Initially, gains from fewer overdoses are offset by drug substitution and more crime, including homicides. Most adverse effects dissipate over time, whereas persistent mortality reductions save the lives of around one in 48 individuals in our sample. Our results demonstrate that reducing the supply of illicit opioids can lead to meaningful longer-term improvements, even when the short-term effects are ambiguous.

Using a proprietary data set covering all foreign investors’ daily trades in the Chinese stock market from 2016 to 2019, we find that foreign order flows, facilitated by regulatory liberalization through several channels, present strong predictive power for future stock returns, implying that these order flows are likely informed. We track the source of this informativeness and find that foreign order flows significantly predict firm-level news and news-day returns, suggesting that foreign investors can effectively process local firm information. Finally, we find that regulatory reforms that generally relax investment access requirements further improve foreign investors’ predictive power.

We study privatization and corporate ownership in transportation infrastructure, focusing on global airports. Privatization in general does not improve performance. However, private equity (PE) ownership has strong and persistent positive effects on efficiency, volume, and quality, with positive externalities for the local economy. We address selection using close auctions where PE and non-PE firms bid. PE funds expand physical capacity and encourage larger planes while adding low-cost carriers and international routes. They are especially effective in countries with more corruption or state-owned flag carriers. In contrast, the poorer performance of non-PE acquirers may reflect private benefits and historical state ties.