We use the introduction of satellite coverage of major retailers to study the capital market implications of unequal access to big data. Satellite data enabled sophisticated investors with access to such data to formulate profitable trading strategies, especially by targeting the upcoming reports of retailers with bad news for the quarter. The introduction of satellite data led to more informed short-selling activity, less informed individual buying activity, and lower stock liquidity around the reports of retailers with satellite coverage. We conclude that unequal access to big data can increase information asymmetry among market participants without immediately enhancing price discovery.

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 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.

Firms become more efficient at innovation activities when they face pressure to meet earnings per share (EPS) targets using stock repurchases. Using a regression-discontinuity framework, we find that incentives to engage in “EPS-motivated buybacks” are followed by more citations and higher values for firms’ new patents. We trace these effects to improved allocation of R&D resources and a greater focus on novel innovation. The positive effects are concentrated among ex ante “innovation-efficient” firms that achieve better patenting outcomes after reorganizing (but not cutting) their R&D investments. Our findings illustrate that short-term earnings pressure can act through a free cash flow channel that motivates more efficient spending.

This article examines the pricing of a firm’s carbon risk in the corporate bond market. Contrary to the “carbon risk premium” hypothesis, bonds of more carbon-intensive firms earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon alpha, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to the predictability of carbon intensity for firm cash-flow news, creditworthiness, and environmental incidents.

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.

How does access to public equity markets affect the human capital of IPO filing firms? While IPO filing firms have high average wages and limited industrial diversification, a successful IPO increases departures of high-wage employees to startups and triggers industrial diversification through employment growth in non-core industries. Surprisingly, IPOs do not significantly affect the earnings growth of pre-IPO workers. Instead, post-IPO hires receive larger earnings increases upon joining. Overall, going public has a significant effect on a firm’s workforce and labor reallocation across firms.

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.

Using a unique data set of firms listed on China’s Shenzhen Stock Exchange, we show that investors’ corporate site visits convey information about future stock returns. Firms with abnormally frequent investor visits predictably outperform firms with abnormally infrequent investor visits by approximately 70-to-100 basis points per month. This return predictability concentrates on neglected firms with low trading volumes and when investors incur higher travel costs. Abnormally frequent investor visits accompany increased holdings among visiting institutions and predict improvements in firms’ fundamental performance, consistent with institutions using visits to gain an information advantage regarding underpriced firms.

This article shows that changes in the tone of central bank communication have a significant effect on asset prices. Tone captures how the central bank frames economic fundamentals and its monetary policy. A positive tone surprise is associated with increases in stock prices and interest rates, whereas credit spreads and volatility risk premia decrease. These tone effects are robust to controlling for policy actions as well as for conventional measures of monetary policy shocks. Our results suggest that communication tone is a powerful instrument of monetary policy, which affects risk premia embedded in asset 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 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.

Long-term growth expectations are central to investment analysis and corporate valuation. Despite a dominant effect on firm value, the academic literature and practitioner conventions provide little guidance on determining this long-term growth rate. This article takes a step in addressing this gap: we estimate the relationship between long-term growth and an extensive selection of firm, industry, and market characteristics. Market prices do not seem to fully capture long-term growth information. Cross sectional tests yield substantial positive abnormal returns for firms with high expected long-term growth.

We develop an extensive mapping of the revolving door phenomenon by examining the work experience of 420,153 individuals in top corporate positions at 12,869 firms. More than half of these firms have at least one such individual with prior experience in one of 187 executive branch agencies. We find that firms are more likely to receive procurement contracts following the appointment of a former regulator transitioning within 2 years of leaving the agency, a result consistent with the “knowledge” hypothesis. Less-complex contracts signed following the appointment of former regulators are more likely to be renegotiated, increasing costs for the government.

We study when CEOs with legal expertise are valuable for firms. In general, lawyer CEOs are negatively associated with frequency and severity in employment civil rights, contract, labor, personal injury, and securities litigation. This effect is partly induced by the CEO’s management of litigation risk and reduction in other risky policies. Lawyer CEOs are further associated with an increase in gatekeepers providing additional legal oversight and a decrease in innovative activities with high litigation risk. Lawyer CEOs are more valuable during periods of enhanced compliance requirements and regulatory pressure and in industries with high litigation risk or better growth opportunities.

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.

Using comprehensive account-level data, we separate Chinese retail investors into 5 groups and document strong heterogeneity in trading dynamics and performances. Retail investors with smaller account sizes cannot predict future returns correctly, display daily momentum patterns, fail to process public news, and show overconfidence and gambling preferences, while retail investors with larger account balances predict future returns correctly, display contrarian patterns, and incorporate public news in trading. Using performance measures established in previous literature, we find that smaller retail investors suffer from poor stock selection abilities and trading costs, while large retail investors’ stock selection abilities are offset by trading costs.

I develop a dynamic model to investigate how labor mobility impacts firms’ decisions. In the model, firms make investment and financing decisions, hire labor with different skill and mobility levels, and set wages through bargaining. The model predicts that, in response to an increase in labor mobility, high-skill firms operate with lower financial leverage, become less responsive to investment opportunities, and invest at lower rates, while low-skill firms remain unaffected. I confirm these predictions in the data using shocks to workers’ mobility across firms. The results are useful in understanding the effects of labor mobility changes driven by government policies or technological shocks, such as the rise of remote work.

Although federal judges are the ultimate arbiters of insider trading enforcement, the role of their political ideology in insider trading is unclear. Using the partisanship of judges’ nominating presidents to measure judge ideology, we first document that liberal judges are associated with heavier penalties in insider trading lawsuits than conservative judges. Next, we find that firms located in circuits with more liberal judges have fewer opportunistic insider sales. Cross-sectional analyses show that this deterrent effect is stronger when managers face a higher risk of insider trading lawsuits. Finally, we find that the Securities and Exchange Commission considers judges’ ideology when selecting litigation forums.

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.