I exploit information in the cross-section of bid-ask spreads to develop a new measure of extreme event risk. Spreads embed tail risk information because liquidity providers require compensation for the possibility of sharp changes in asset values. I show that simple regressions relating spreads and trading volume to factor betas recover this information and deliver high-frequency tail risk estimates for common factors in stock returns. My methodology disentangles financial and aggregate market risks during the 2007–2008 financial crisis; quantifies jump risks associated with Federal Open Market Committee announcements; and anticipates an extreme liquidity shock before the 2010 Flash Crash. Received April 27, 2016; editorial decision August 10, 2018 by Editor Andrew Karolyi.

I demonstrate an important tension between acquiring information and incorporating it into asset prices. As a salient case, I analyze algorithmic trading (AT), which is typically associated with improved price efficiency. Using a new measure of the information content of prices and a comprehensive panel of 54, 879 stock-quarters of Securities and Exchange Commission (SEC) market data, I establish instead that the amount of information in prices decreases by 9% to 13% per standard deviation of AT activity and up to a month before scheduled disclosures. AT thus may reduce price informativeness despite its importance for translating available information into prices. Received May 21, 2016; editorial decision October 25, 2017 by Editor Itay Goldstein.

Is there a gap between the profitability of a trading strategy on paper and that which is achieved in practice? We answer this question by developing a general technique to measure the real-world implementation costs of financial market anomalies. Our method extends Fama-MacBeth regressions to compare the on-paper returns to factor exposures with those achieved by mutual funds. Unlike existing approaches, ours delivers estimates of all-in implementation costs without relying on parametric microstructure models or explicitly specified factor trading strategies. After accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum.

Equal compensation across assets for the same risk exposures is a bedrock of asset pricing theory and empirics. Yet real-world frictions can violate this equality and create apparently high Sharpe ratio opportunities. We develop new methods for asset pricing with cross-sectional heterogeneity in compensation for risk. We extend k-means clustering to group assets by risk prices and introduce a formal test for whether differences in risk premiums across market segments are too large to occur by chance. We find significant evidence of cross-sectional variation in risk prices for almost all combinations of test assets, factor models, and time periods considered.