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

Cross-Venue Liquidity Provision: High Frequency Trading and Ghost Liquidity

We measure the extent to which consolidated liquidity in modern fragmented equity markets overstates true liquidity due to a phenomenon that we call Ghost Liquidity (GL). GL exists when traders place duplicate limit orders on competing venues, intending for only one of the orders to execute, and when one does execute, duplicates are cancelled. By employing data from 2013 for 91 stocks trading on their primary exchanges and three alternative platforms where order submitters are identified consistently across venues, we find that simply measured consolidated liquidity exceeds true consolidated liquidity due to the existence of GL. On average, for every 100 shares passively traded by a multi-market liquidity supplier on a given venue, around 19 shares are immediately cancelled by the same liquidity supplier on a different venue. Yet the average weight of GL in total consolidated depth, i.e., slightly more than 4%, does not challenge the liquidity benefits of fragmentation. GL can however reach substantial levels for some categories of stocks, traders, and platforms, namely larger and less volatile stocks, high-frequency traders (HFTs), and non-primary exchanges. The greatest GL is observed for the HFTs who mostly behave as liquidity takers, on more heavily traded and less volatile stocks, across alternative platforms

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

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample 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. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants