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 identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.

We show theoretically and empirically that the cross-section of stock return idiosyncratic volatilities contains useful information about the ICAPM. We construct a proxy cross-sectional bivariate idiosyncratic volatility (CBIV) for the covariance risk between the market and the unobserved hedge portfolio under the ICAPM. Consistent with the ICAPM pricing relation, CBIV is a robust and significant predictor of the equity risk premium. We further show that the return predictability of the tail index in Kelly and Jiang (2014) can be explained by the ICAPM covariance risk.

This study utilizes U.S. Patent Office data to explore potential improvements in the patent examination process through machine learning. It shows that integrating machine learning with human expertise can increase patent citations by up to 26%. Using machine learning predictions as benchmarks, I find that the early expiration rate of granted patents positively correlates with examiners’ false acceptance rates. These errors negatively impact public companies’ operational performance and reduce successful IPO or M&A exits for private firms. Overall, this study highlights significant social and economic benefits of incorporating machine learning as a robo-advisor in patent screening.

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 show that investors price short-term stock market outcomes very different from outcomes that occur further into the future. To this end, we introduce the expected forward pricing kernel and decompose long-term pricing kernels into short-term and expected forward pricing kernels. Using index options, we find that kernels with maturities of up to 12 months are U-shaped and show that this results from the shape of the 1-month pricing kernel. Once we remove the impact of the 1-month kernel, the expected forward kernels are in line with standard long-run risk models in terms of their shape, level, and time-series variation.

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.

International equity funds attain superior subsequent performance by actively changing their country asset allocations, which we capture through a new measure of active country rotation intensity. Across funds, those that rotate country allocations with the greatest intensity on average have the highest value added. We offer evidence that a fund’s change of holdings in a country is associated with future outperformance in those specific holdings. Outperformance is concentrated on the downside when funds sell down country holdings before subsequent poor country market returns. Overall, our findings affirm that active international mutual funds have country market timing abilities.

Using close to 800,000 transactions by 66,000 households in the United States and close to 2,000,000 transactions by 303,000 households in Finland, this paper shows that, on average, individual investors with longer holding periods choose to hold less liquid stocks in their portfolios. The relationship between holding periods and transaction costs is stronger among more financially sophisticated households. We confirm our findings by analyzing changes in investors’ holding periods around exogenous shocks to stock liquidity. Our findings challenge the notion that individual investors ignore non-salient costs when making investment decisions and suggest that they are cognizant of the cost of trading stocks.

This article provides the first comprehensive evidence that the return extrapolation behavior of investors leads to biases in the expectations of volatility. Lower past returns are associated with higher expectations of volatility when using the physical, risk-neutral, and survey measures to estimate volatility expectations. Consistent with the return extrapolation framework, recent past returns have a larger impact than distant past returns on volatility expectations. Biases in volatility expectations are i) distinct from extrapolating past realized volatility, ii) asymmetrically induced by recent past negative returns, and iii) lead investors to pay more to insure against the perceived higher expected volatility.

We investigate whether compensation consultants recommend excessive pay to earn repeat business by studying consultant changes. Our results show consultants’ interests are aligned with shareholders’ to appropriately pay the CEO. Boards dismiss consultants making large pay recommendation errors, particularly positive ones. However, powerful or poorly monitored CEOs interfere with such disciplinary turnover, weakening the relation. Peer groups are more likely to change with new consultant appointments. New consultants are less likely to include highly paid executives in the compensation peer group and CEO pay falls following the change. Directors earn higher votes in annual elections when they replace compensation advisors.

A well-documented pattern of bank lending during crises is allocating credit to insolvent firms at the expense of productive firms, leading to inefficient resource allocation at the macro level. I investigate the role of bank CEOs in influencing such distortions during crises, using the strictly enforced age-based retirement policy of Indian government-controlled banks. I find that banks experiencing a CEO turnover in a crisis are less likely to bail out insolvent borrowers, as the new CEO has a lower incentive to do so. Consequently, the efficiency of credit allocation improves, and the zombification of the economy decreases.

This study investigates how political patronage influences local firms’ investment decisions in China, focusing on changes in patronage ties resulting from provincial leadership turnover. By examining prefectural officials with connections to their provincial superiors, we find that firms in these regions experience increased investment expenditures, albeit with reduced efficiency. This effect is primarily driven by stronger promotion incentives for local officials, bolstered by favoritism from provincial patrons. While political patronage helps address agency problems within political hierarchies, our findings highlight its adverse economic impact due to misaligned interests between politicians and the public.

Filing agents—intermediaries used by 80% of U.S. firms—are associated with leakage of information that affects stock prices. Prior to the public release of a securities filing, most firms outsource the final processing and submission of the filing to a third-party filing agent. We find leakage is higher when firms use filing agents than when firms self-file, particularly pre-2018. Leakage is greater when the private information is more valuable and decreases when firms switch to self-filing. Our research suggests filing agents, and a firm’s choice to use them, are an important, understudied channel for the leakage of private information.

In 2017, the European Union Emissions Trading System underwent a policy intervention that resulted in a surge in carbon prices. Using this setting as a quasi-natural experiment, we focus on employment, productivity, and emission outcomes among covered enterprises. Results show that emission-intensive private firms, particularly those with financial constraints, are more likely to downsize by divesting production assets, reducing both workforce and emissions. Smaller, cash-strapped listed firms are also prone to downsize by decreasing their operating leverage while maintaining emission output and asset levels. Positive productivity outcomes indicate that both private and listed firms become leaner postintervention.