Realtime price impact detection

Abstract

An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so.