Knowledge that Transforms

Order flow segmentation prevents direct interactions between U.S. retail and institutional investors. Using the imbalance in observable internalized retail trades, we show wholesalers use retail flow to provide liquidity to institutional investors, especially when liquidity is scarce. Our institutional liquidity cost ( $ ILC $ ) measures average absolute retail trade imbalances, positing that institutions holding stocks with greater such averages more often resort to the expensive wholesaler-provided liquidity. $ ILC $ is correlated with expected institutional price impacts. Unlike existing illiquidity measures, $ ILC $ has economically meaningful relations with institutional holding horizons and yields annualized liquidity premia of 2.7%–3.2% post-2010, even after excluding microcap stocks.

This paper documents that 56% of nonprofessional social media investment analysts (SMAs) are skilled and declare beliefs that generate positive abnormal returns (ABRs), while 44% produce negative ABRs. 13% of all SMAs are high-skill type and produce a 1-week 3-factor alpha of 61 bps, while the remaining 87% generate only 6 bps. The distinctive features of high-skill SMAs are primarily firm and industry specializations. Although SMAs tend to extrapolate and herd, their expectations are not systematically wrong. For higher-skilled SMAs compared to the less-skilled ones, extrapolation fades more quickly, and herding is lower, consistent with theory.

We examine whether intellectual property protection facilitates the greater incorporation of firm-specific information into the stock price. Employing the staggered, country-level adoption of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), we find that after adoption, stock prices become less synchronous, consistent with more firm-specific information being impounded into the stock price. We further show that this effect is more pronounced for more innovative firms, firms in countries with stronger law enforcement, and firms with more financial analyst coverage. Finally, we document that TRIPS induces a richer information environment characterized by more management forecasts and media coverage.

We document that firms with greater product similarity to their peers exhibit lower rates of financial fraud. We show that peer similarity is associated with better information environments, which is consistent with monitors’ enhanced ability to benchmark against other firms. The negative relation between product similarity and fraud remains after controlling for alternative mechanisms including incentive compensation structures, competition, and internal and external governance characteristics. Overall, our findings suggest that greater peer similarity increases the marginal cost of fraud, and therefore, ex ante disincentivizing managers from committing fraud.

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.

We document significant increases in the suspension of ongoing drug projects following the passage of the Food and Drug Administration Amendments Act of 2007 (FDAAA), which mandates that pharmaceutical companies publicly disclose detailed clinical study results. Our results suggest a causal interpretation through difference-in-differences analyses that exploit variations in pre-FDAAA information environments. We also show evidence that fewer new projects are initiated after the FDAAA. Drug developers’ learning from peer failures is the primary mechanism, further amplified by financial constraints. We also examine the consequences of enhanced information disclosure, including changes in firm investment efficiency, drug quality, and disease morbidity.

We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.

We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.

I show that a tariff policy change that increased trade with China led to a decline in U.S. public listing rates and elevated industry concentration. Consistent with heterogeneous firm models of trade, the shock impeded the entry and performance of small domestic manufacturers but did not adversely impact large multinationals. In addition, stock price reactions to the tariff policy change and threat of reversal imply that trade liberalization creates or destroys value depending on firm size. These findings suggest that recent trends in the U.S. public equity market are driven, in part, by fundamental changes in the global competitive landscape.

Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.