Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

The flash crash:A cautionary tale about highly fragmented markets

A breakdown of cross-market arbitrage activity could make markets more fragile and result in price crashes. We provide suggestive evidence for this novel channel based on a high-frequency analysis of the most salient crash in recent history: The Flash Crash. We further show that such an event can be extremely costly for a large seller trading in a particular venue as the seller effectively relies on local liquidity supply only. These findings highlight the vulnerability of today’s highly fragmented markets

Shades of darkness: A pecking order of trading venues

We characterize the dynamic fragmentation of U.S. equity markets using a unique data set that disaggregates dark transactions by venue types. The “pecking order” hypothesis of trading venues states that investors “sort” various venue types, putting low-cost-low-immediacy venues on top and high-cost-high-immediacy venues at the bottom. Hence, midpoint dark pools on top, non-midpoint dark pools in the middle, and lit markets at the bottom. As predicted, following VIX shocks, macroeconomic news, and firms’ earnings surprises, changes in venue market shares become progressively more positive (or less negative) down the pecking order. We further document heterogeneity across dark venue types and stock size groups. Keywords: dark pool, pecking order, fragmentatio

Shades of darkness: A pecking order of trading venues

We characterize the dynamic fragmentation of U.S. equity markets using a unique data set that disaggregates dark transactions by venue types. The “pecking order” hypothesis of trading venues states that investors “sort” various venue types, putting low-cost-low-immediacy venues on top and high-cost-high-immediacy venues at the bottom. Hence, midpoint dark pools on top, non-midpoint dark pools in the middle, and lit markets at the bottom. As predicted, following VIX shocks, macroeconomic news, and firms’ earnings surprises, changes in venue market shares become progressively more positive (or less negative) down the pecking order. We further document heterogeneity across dark venue types and stock size groups

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants