Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We compare trade size to the prevailing market depth at the best level in the limit order book to detect and account for zero impact trades in an immediate price impact model. Our model also incorporates standard trade attributes (trade size, market capitalization and volatility) in a dynamic setting. The incorporation of market depth information reduces the mean absolute/squared forecast error of an immediate price impact prediction by about 60%. After controlling for trade attributes, market depth, price impact dynamics and intra-and inter- day periodicities (in order of relative importance) all improve the prediction of a trade’s price impact. We demonstrate the value of our model by showing that splitting a big order into a series of smaller trades results in a reduction of between 60% and 82% of the immediate price impact cost of the big order. We also find that our depth indicator helps with the prediction of order flow and permanent price impact.