This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbalance to control a limit order. We emphasis the exposure to adverse selection, of paramount importance for limit orders. For a participant buying using a limit order: if the price has chances to go down the probability to be filled is high but it is better to wait a little more before the trade to obtain a better price. In a third part we show how the added value of exploiting a knowledge on liquidity imbalance is eroded by latency: being able to predict future liquidity consuming flows is of less use if you have not enough time to cancel and reinsert your limit orders. There is thus a rational for market makers to be as fast as possible as a protection to adverse selection. Thanks to our optimal framework we can measure the added value of latency to limit orders placement.
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