This article systematically examines the importance of exchange rate movements and industry competition for stock returns. Common shocks to industries across countries are more important than competitive shocks due to changes in exchange rates. Weekly exchange rate shocks explain almost nothing of the relative performance of industries. Using returns measured over longer horizons, the importance of exchange rate shocks increases slightly and the importance of industry common shocks increases more substantially. Both industry and exchange rate shocks are more important for industries that produce internationally traded goods, but the importance of these shocks is economically small for these industries as well.

We construct novel measures of financial constraints using textual analysis of firms’ annual reports and investigate their impact on stock returns. Our three measures capture access to equity markets, debt markets, and external financial markets in general. In all cases, constrained firms earn higher returns, which move together and cannot be explained by the Fama and French (2015) factor model. A trading strategy based on financial constraints is most profitable for large, liquid stocks. Our results are strongest when we consider debt constraints. A portfolio based on this measure earns an annualized risk-adjusted excess return of 6.5%. Received April 4, 2016; editorial decision December 17, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

[I present a methodology that uses the mixed jump-diffusion model for stock returns to estimate the separate effects of information surprises and strategic trading around corporate events. Using simulation techniques, I show that for events with multiple announcements spread over a long time, the estimators derived from the mixed jump-diffusion model are more powerful compared to the traditional cumulative abnormal return estimators. The new methodology is used to study the separate effects of information surprises and strategic trading associated with blockholdings and subsequent targeted repurchases. I find that for more than 93 percent of the firms in our sample the mixed jump-diffusion model is statistically superior to the pure diffusion model in describing stock returns. More important, I find a statistically significant negative effect due to trading, while the average effect around announcements is statistically insignificant. In contrast, the standard cumulative abnormal return is not statistically different from zero.]

We investigate why only some banks use regulatory arbitrage. We predict that banks wanting to be riskier than allowed by capital regulations (constrained banks) use regulatory arbitrage, while others do not. We find support for this hypothesis using trust-preferred securities issuance, a form of regulatory arbitrage available to almost all U.S. banks from 1996 to Dodd-Frank. We also find support for predictions that constrained banks are riskier, perform worse during the crisis, and use multiple forms of regulatory arbitrage. We show that neither too-big-to-fail incentives nor misaligned managerial incentives are first-order determinants of this type of regulatory arbitrage.

[This article investigates the dynamic relation between market-wide trading activity and returns in 46 markets. Many stock markets exhibit a strong positive relation between turnover and past returns. These findings stand up in the face of various controls for volatility, alternative definitions of turnover, differing sample periods, and are present at both the weekly and daily frequency. The relation is more statistically and economically significant in countries with high levels of corruption, with short-sale restrictions, and in which market volatility is high.]

Abstract 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

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. Authors have furnished an Internet Appendix, which is available on the Oxford University PressWeb site next to the link to the final published paper online.

I document a new stylized fact about how investors trade assets: individuals are more likely to sell the extreme winning and extreme losing positions in their portfolio ("the rank effect"). This effect is not driven by firm-specific information, holding period or the level of returns itself, but is associated with the salience of extreme portfolio positions. The rank effect is exhibited by both retail traders and mutual fund managers. The effect indicates that trades in a given stock depend on how the stock compares to other positions in an investor's portfolio.

This paper characterizes how firms' strategic interaction in product markets affects the industry dynamics of investment and expected returns. In imperfectly competitive industries, a firm's exposure to systematic risk is affected by both its own investment strategy and the investment strategies of its peers, so that the dynamics of its expected returns depend on the intraindustry value spread. In the model and the data, firms' betas and returns correlate more positively in industries with low value spread, low dispersion in operating markups, and low concentration.

I document a new stylized fact about how investors trade assets: individuals are more likely to sell the extreme winning and extreme losing positions in their portfolio ("the rank effect"). This effect is not driven by firm-specific information, holding period or the level of returns itself, but is associated with the salience of extreme portfolio positions. The rank effect is exhibited by both retail traders and mutual fund managers. The effect indicates that trades in a given stock depend on how the stock compares to other positions in an investor's portfolio.