Knowledge that Transforms

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.

We find that division managers who are connected to the CEO are substantially less likely than others to depart from the firm and are more likely to be promoted. Connected managers are protected when performance is poor, and they display no special ability to improve performance given this protection. Connections matter more in weak governance/incentive environments, and the external labor market and stock market appear skeptical of connected managers’ talents. While much of the evidence suggests inefficient favoritism, connected managers are protected more in peripheral segments, suggesting a possible efficiency benefit in helping to resolve intrafirm information problems.

We examine compensation for endowment Chief Investment Officers (CIOs) overseeing portfolios with significant allocations to alternatives. We find widespread use of bonuses and that large endowments with high alternative allocations hire CIOs with stronger backgrounds, pay them more, and have higher pay-for-performance sensitivity. We find weak evidence of a relationship between compensation and future performance. Our results align with contract theory predictions but differ from empirical findings on pension funds. Endowments pay CIOs more, rely more on bonuses, attract more experienced professionals, and have lower turnover than pensions. This suggests more effective talent management compared to politically influenced public pensions.

Antitakeover measures are controversial because the evidence of their net effect on shareholders is mixed. We propose that, for many firms, the potential bonding benefits outweigh the agency costs of the quiet life, explaining the mixed results. We study business combination and poison pill laws as exogenous shocks to takeover vulnerability and use shareholder valuation of internal slack as an indicator of the net effect of takeover protection. Firms susceptible to quiet life agency problems exhibit a decrease in the market-assessed value of internal slack. Conversely, cash appreciates at companies where takeover protection bonds commitments with major counterparties.

This paper studies how investor heterogeneity impacts equilibrium debt maturity. The optimal issuance strategy combines long- and short-term debts. A long-term debt contains default risk but hedges against intermediate downturns. A short-term debt provides repayment commitment but requires being rolled over and becomes risky during downturns. Issuing multiple debt maturities spreads the cost of these risky claims to investors most willing to hold risk at different points in time. The model predicts that debt maturity is more dispersed with lower financing costs and more investment opportunities when debt ownership is spread among many different types of investors.

We find that 43% of firms that make payouts also raise capital during the same year, resulting in 31% of aggregate payouts being externally financed, primarily with debt. Most financed payouts cannot be explained by payout smoothing in response to volatile earnings or investment (rather, they are the result of firms persistently setting payouts above free cash flow). In fact, 25% of aggregate payouts could not have been paid without the firms simultaneously raising capital. Profitable firms with moderate growth use debt-financed payouts to jointly manage their leverage and cash, thus highlighting the close relationship between payout and capital structure decisions.

This study employs variance ratios (VRs) to assess the roles of regulation and liquidity on cryptocurrency market efficiency, focusing on crypto-assets subject to varying degrees of regulation. Our findings reveal that cryptocurrencies supervised by FinCEN-licensed exchanges (IEO-L) exhibit market efficiency similar to SEC-regulated traditional stock offerings (IPOs). Conversely, noncompliant crypto-assets display higher market inefficiency. We also establish a connection between regulatory compliance and issuing entity reputation mechanisms. Our results indicate that compliance with existing regulatory norms enhances efficiency and reduces investor risks in crypto-assets. Furthermore, assets voluntarily adhering to regulatory norms can attain efficiency akin to government-regulated assets.