Dynamic Order Submission Strategies with Competition between a Dealer Market and a Crossing Network

Abstract

We present a dynamic microstructure model where a dealer market (DM) and a crossing network (CN) interact. We consider sequentially arriving agents having different valuations for an asset. Agents maximize their profits by either trading at a DM or by submitting an order for (possibly) uncertain execution at a CN. We develop the analysis for three different informational settings: transparency, "complete" opaqueness of all order flow, and "partial" opaqueness (with observable DM trades). We find that a CN and a DM cater for different types of traders. Investors with a high eagerness to trade are more likely to prefer a DM. The introduction of a CN increases overall order flow by attracting traders who would not otherwise submit orders ("order creation"). It also diverts trades from the DM. The transparency and "partial" opaqueness settings generate systematic patterns in order flow. With transparency, the probability of observing a CN order at the same side of the market is smaller after such an order than if it was not. Buy (sell) orders at a CN are also less likely to attract subsequent sell (buy) orders at the DM.JEL Codes: G10, G20

Two Shades of Opacity: Hidden Orders versus Dark Trading

“…However, we find the opposite, i.e. dark trading is increasing in the size of the consolidated visible spread, which is more in line with the predictions of Hendershott and Mendelson (2000) and Degryse, Van Achter, and Wuyts (2009). The latter is also in line with results of Garvey, Huang, and Wu (2014), who use proprietary order-level data on the submission of orders to dark and lit venues.…”

Do Dark Pools Harm Price Discovery?

“…(If two venues give equal payoff to a trader, sending the entire order to one venue is also optimal.) While this result relies on the assumption of finite order size per capita, a finite order size is a standard assumption in existing models of dark pools, such as Hendershott and Mendelson (2000), Degryse, Van Achter, andWuyts (2009), andWerner (2011a), as well as the classic model of Glosten and Milgrom (1985) and extensions. A finite order size can also be viewed as a substitute for (unmodeled) credit limit or capital constraint.…”

“…Degryse, Van Achter, and Wuyts (2009) build a dynamic model of a dark pool and analyze how various transparency requirements for dark-pool orders affect traders' behavior and welfare. Buti, Rindi, and Werner (2011a) model the competition between an open limit order book and a dark pool, and focus on the interaction between darkpool trading and characteristics of the limit order book, such as quote depths.…”

Optimal liquidation in dark pools

“…8 Note that in our model dark pool liquidity is given exogenously. In Hendershott and Mendelson (2000), Dönges and Heinemann (2006) and Degryse et al (2009a), buyers and sellers are chosen randomly according to some probability distribution. The liquidity in the dark pool (and therefore the probability of dark pool execution) is then solely dependent on the chosen traders' decisions of where to trade (in a traditional market or in a dark pool) and is thus given endogenously.…”

“…In a similar setting, Dönges and Heinemann (2006) focus on game-theoretic refinements in order to remove the multiplicity of equilibria in Hendershott and Mendelson (2000). Degryse et al (2009a) introduce a dynamic multi-period framework and compare the effect of different levels of transparency of the dark pool. Contrary to these endogenous trading models, we exogenously specify the liquidity properties.…”