Why is order flow so persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure

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

Why is order flow so persistent?

Equity order flow is persistent in the sense that buy orders tend to be followed by buy orders and sell orders tend to be followed by sell orders. For equity order flow this persistence is extremely long-ranged, with positive correlations spanning thousands of orders, over time intervals of up to several days. Such persistence in supply and demand is economically important because it influences the market impact as a function of both time and size and because it indicates that the market is in a sense out of equilibrium. Persistence can be caused by two types of behavior: (1) Order splitting, in which a single investor repeatedly places an order of the same sign, or (2) herding, in which different investors place orders of the same sign. We develop a method to decompose the autocorrelation function into splitting and herding components and apply this to order flow data from the London Stock Exchange containing exchange membership identifiers. Members typically act as brokers for other investors, so that it is not clear whether patterns we observe in brokerage data also reflect patterns in the behavior of single investors. To address this problem we develop models for the distortion caused by brokerage and demonstrate that persistence in order flow is overwhelmingly due to order splitting by single investors. At longer time scales we observe that different investors' behavior is anti-correlated. We show that this is due to differences in the response to price-changing vs. non-price-changing market orders.

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