I examine blockchain congestion episodes caused by more than 4,400 triggers for ransomware attacks over a two-year period. When demand for settlement exceeds blockchain capacity, blockchain users engage in fee competition to prioritize their transaction settlement. A typical surge in ransomware activity causes transaction fees to increase by 2.1% and up to 28% in extreme cases. Consistent with theory literature, some users forgo blockchain settlement when transaction fees increase. An event study around an extreme spike in ransomware activity supports the findings of the main analysis.

Exchange ownership in the U.S. is often characterized as excessively concentrated. This leads to a concern that such concentration may prevent peripheral exchanges from mitigating adverse selection costs associated with low-latency arbitrage. We examine this concern using low-latency connectivity disruptions caused by temporary relocations of two markets, NYSE Chicago and Nasdaq PSX, in response to a transaction bill proposal. Although both exchanges had previously announced measures to curb low-latency trading, the connectivity disruptions cause a substantial reduction in adverse selection. These results suggest that peripheral markets have little incentive to implement measures restricting low-latency arbitrage.

Modern markets are characterized by speed differentials, with some traders being fractions of a second faster than others. Theoretical models suggest that such differentials may have both positive and negative effects on liquidity and gains from trade. We examine these effects by studying a series of exogenous weather episodes that temporarily remove the speed advantages of the fastest traders by disrupting their microwave networks. The disruptions are associated with lower adverse selection and lower trading costs. In additional analysis, we show that the long‐term removal of speed differentials results in similar effects and also increases gains from trade.

Researchers are often interested in modeling binary decisions made by firms (e.g., the yes or no decisions to split the shares, initiate a dividend, or acquire another firm) as functions of economy-wide variables (common factors). Although factor models for continuous dependent variables are used widely, the toolkit of a financial researcher does not contain a generally accepted methodology that allows estimating factor models for binary dependent variables. In this paper, we study such a methodology. Using simulations, we identify data characteristics that allow for reliable estimates of factor parameters and conclude that the methodology is appropriate for the panel datasets of the type often used in finance. As an illustration, we use the methodology to address a currently debated issue of common factors in firms’ decisions to split their shares.

Are endogenous liquidity providers (ELPs) reliable in times of market stress? We examine the activity of a common ELP type—high frequency traders (HFTs)—around extreme price movements (EPMs). We find that on average HFTs provide liquidity during EPMs by absorbing imbalances created by non-high frequency traders (nHFTs). Yet HFT liquidity provision is limited to EPMs in single stocks. When several stocks experience simultaneous EPMs, HFT liquidity demand dominates their supply. There is little evidence of HFTs causing EPMs.