Knowledge that Transforms

I study the role of geographical proximity in hedge fund activism and find that activist hedge funds are more likely to target firms located closer to their headquarters. Despite this proximity preference, activism returns are lower for nearer targets. Alternative factors, including lower activism costs, target selection effects, and reduced employee wealth transfers at nearby firms do not explain lower returns to proximate targets. Instead, results are consistent with familiarity bias in hedge fund targeting decisions. Additional tests focusing on small targets, openly confrontational campaigns, and passive investments reinforce this behavioral explanation. • This paper studies the role of geographical proximity in hedge fund activism. • Results show that activist hedge funds are more likely to target firms located closer to their headquarters. • Despite this proximity preference, activism returns are lower for nearer targets. • Other factors do not explain lower returns to closer targets but results support familiarity bias in hedge fund targeting. • Additional tests focusing on small targets, hostile activism, and passive investments buttress this behavioral explanation.

We apply machine learning methods to granular environmental variables and test if there is a strong positive relation between environmental sustainability and future stock returns. A long-short portfolio that longs stocks with high forecasted returns and sells stocks with low forecasted returns earns large abnormal returns, and it performs better when climate concerns in the media are more intense. Further diagnosis shows that various dimensions of environmental sustainability help return predictions. High forecasted returns are associated primarily with strong environmental operational performance. The return prediction based on a customized transformer model is similar.

Cryptocurrency prices differ across countries, and these price deviations fluctuate widely. We model cryptocurrencies as an asset immune from local government expropriation, and trust serves as the perceived probability of government misconduct. After political scandal outbreaks, Bitcoin prices rise by 1.31% and Ethereum prices by 0.83% relative to their U.S. dollar prices on average. The price deviation responses are larger in countries where citizens have less trust in government. With panel regressions, we show domestic cryptocurrency prices become more expensive when higher volumes of Google searches are related to domestic institutional failures.

We assess the information content of order flow for the cross-section of cryptocurrency returns. Our analysis is based on a set of international order flows denominated in 11 major currencies that reflect world order flow. We find that world order flow has strong explanatory and predictive power for cryptocurrency returns. Order flow tends to dominate economic fundamentals for out-of-sample prediction, especially in the context of non-linear machine learning models, and its performance cannot be explained by limits to arbitrage. Overall, our findings indicate that order flow has a permanent effect on cryptocurrency returns.

We analyze the impact of stock market indexing on option market conditions using local linear regressions on Russell Index reconstitution. Our findings reveal that put-call parity deviations are significantly smaller for stocks at the top of the Russell 2000 Index, compared to those at the bottom of the Russell 1000 Index. Those top Russell 2000 stocks also exhibit higher trading option volume and narrower bid-ask spreads. Our results suggest that stock market indexing enhances option market conditions through increased liquidity, reducing hedging costs that benefit market makers.

Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.

Almost half of US share trading volume occurs in dark markets, prompting regulatory concerns. We examine the effects of dark trading on issuers and show that, at moderate levels, dark trading improves the quality of corporate investment decisions by increasing the amount of information in prices that is new to managers. Consistent with this mechanism, higher dark trading is associated with greater investment–price sensitivity, improved managerial forecast accuracy, stronger M&A-price sensitivity, and superior future operating performance. These benefits diminish, and can reverse, at high levels of dark trading. We establish causality using exogenous changes in dark trading.

We empirically identify a group of stock-exchange accounts that profit from 11 years of earnings surprises. Their trading behavior is consistent with privately informed trading, yet they have negative and temporary price impacts. We then empirically identify a second group of accounts that have positive and permanent price impacts. The trading behavior of the second group is more consistent with trading on public information, and they trade the wrong way before earnings surprises. The behavior of both account groups contrasts with models that associate permanent price impact with privately informed trading.