Tick sizes affect market quality through a tradeoff between pricing fidelity and undercutting. The U.S. Tick Size Pilot (TSP), which raised the minimum tick from 1¢ to 5¢, provides a natural experiment to study this tradeoff. We find that the TSP harmed liquidity for stocks with spreads below 10¢ but improved liquidity for stocks with spreads above 15¢. These opposing effects explain the mixed results across prior studies which pool together stocks with very different prevailing spreads. We recommend researchers using the TSP for causal inference should, at minimum, split samples at 10¢-spreads to account for these heterogeneous liquidity effects.

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.

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.

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 examine the cross-sectional predictability of corporate bond returns using a novel international dataset and a set of machine learning techniques. We find strong predictability in both U.S. and non-U.S. markets, with differing predictive factors. Bonds in developed markets show greater integration with the U.S. market and stronger ties to equity markets. Predictive performance of machine learning models varies over time and is greater before the onset of the COVID-19 pandemic and during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. The results offer insights into bond pricing and global diversification opportunities.

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.

High-frequency traders (HFTs) mainly operate on public exchanges. Since the European regulatory changes in 2018 (Markets in Financial Instruments Directive II), some HFTs began operating Systematic Internalizers (SIs), i.e., single-dealer platforms where clients trade against the dealer's inventory. Using Swedish equity market data, we show that higher HFT dealer-platform activity reduces displayed exchange liquidity: quoted spreads widen and depth falls. Effective spreads are largely unchanged for HFTs but increase for non-HFT traders. Price efficiency improves as return autocorrelations and excess variance ratios move closer to random-walk benchmarks. Evidence suggests HFT dealers' inventory management is the main channel behind these results.

We explore the use of technical indicators to forecast corporate bond returns with various machine learning models. We show that technical indicators yield statistically significant and economically meaningful results, consistently outperforming bond characteristics. Although bond characteristics possess predictive power for bond returns, they do not provide incremental value beyond technical indicators across all bonds. Additionally, machine learning models do not offer substantial improvements over the benchmark linear model. These results underscore the significance of technical indicators in the corporate bond market.