No abstract

We study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure. On May 6, 2010, U.S. financial markets experienced a systemic intraday event-the Flash Crash-where a large automated selling program was rapidly executed in the E-mini S&P 500 stock index futures market. Using audit trail transaction-level data for the E-mini on May 6 and the previous three days, we find that the trading pattern of the most active nondesignated intraday intermediaries (classified as High-Frequency Traders) did not change when prices fell during the Flash Crash.

No abstract

Using a novel database on venue short sales and market design characteristics, we ask: Where do short sellers exploit their information advantage? Consistent with the prediction of Zhu (2014), we find that exchange short sales comprise a larger proportion of trading and are more informative about future prices than dark-pool short sales, particularly when there is greater competition among short sellers to trade and in the presence of short-lived information. When examining market design characteristics, we find that dark pools offering volume-weighted average price crossing attract more short sales, whereas those offering block trading attract fewer short sales.

After a natural experiment is first used, other researchers often reuse the setting, examining different outcome variables. We use simulations based on real data to illustrate the multiple hypothesis testing problem that arises when researchers reuse natural experiments. We then provide guidance for future inference based on popular empirical settings including difference-in-differences regressions, instrumental variables regressions, and regression discontinuity designs. When we apply our guidance to two extensively studied natural experiments, business combination laws and the Regulation SHO pilot, we find that many results that were statistically significant using single hypothesis testing do not survive corrections for multiple hypothesis testing.

No abstract

After a natural experiment is first used, other researchers often reuse the setting, examining different outcome variables. We use simulations based on real data to illustrate the multiple hypothesis testing problem that arises when researchers reuse natural experiments. We then provide guidance for future inference based on popular empirical settings including difference‐in‐differences, instrumental variables, and regression discontinuity designs. When we apply our guidance to two extensively studied natural experiments, business combination laws and the Regulation SHO pilot, we find that many results that were statistically significant using single hypothesis testing do not survive corrections for multiple hypothesis testing.

No abstract