Dynamic Order Submission Strategies with Competition between a Dealer Market and a Crossing Network Hans Degryse, University of Leuven and CentER Mark Van Achter, University of Leuven Gunther Wuyts University of Leuven Frontiers of Finance Bonaire, January 13-16, 2005

Motivation Recently : “new trading platforms” coexist with “traditional markets” New trading platforms - Alternative trading systems: – Electronic Communication Network (ECN) – Crossing Network (CN): “a system that allows participants to enter unpriced orders to buy and sell securities. Orders are crossed at a prespecified time at a price derived from another market.” (SEC (1998))

Motivation Crossing Network: –Lower costs (no spread), Anonymity –Uncertain execution –No price discovery –Examples: Instinet Crossing Network, ITG Posit, E-Crossnet "A survey of fund managers shows an expected 90% increase in crossing volume over the next two years"

Motivation Goal of this paper : Investigate impact of interaction of a batch-type CN and a continuous dealer market (DM) on the liquidity and order flow dynamics in both markets

Main Findings DM caters to investors with high willingness to trade whereas CN to those with lower willingness to trade Introduction of CN induces “order creation” Even with random arrival of buyers and sellers and despite the absence of asymmetric information, systematic, non-random patterns in order flow arise

Outline Related Literature & Contributions Setup of the Model Equilibrium –Markets in Isolation –Equilibrium: DM and CN Empirical Predictions Different Informational Settings Concluding Remarks

Related Literature & Contributions Static Dynamic One marketInteraction CN Parlour (RFS 1998) a.o. H&M (JF 2000) Dönges et al. (2001) Many papers X

We construct a dynamic model analyzing the interaction between a CN and a DM We add a CN to the dynamic models analyzing an individual trading system. Related Literature & Contributions

Setup of the Model Based on Parlour (RFS, 1998) 2 days in the economy Agents decide upon consumption on both days:  is the subjective preference or type Asset which pays out V units of C 2 on day 2 Trading takes place during the first day, claims to the asset are exchanged for C 1

Setup of the Model Trading day: –Consists of 1,…,T periods –One agent arrives each period (= trader) –Traders are characterized by Trading orientation: Buyer or Seller (probability  b and  s ) Type: Willingness to trade –Traders choose between submitting an order to the DM, an order to the CN (both have order size = 1) or no order –Orders cannot be modified or cancelled

Setup of the Model Dealer Market: –One-tick market with ask A and bid B => A-B=1 –Dealers stand ready to trade at these quotes Crossing Network: –Orders are stored in book (b=buy, s=sell): –Cross takes place at T –Price of the cross is midprice of quotes at DM

Setup of the Model Orders in CN- book |sell|buy

Setup of the Model Orders in CN- book |sell|buy matched at T (time priority)

Setup of the Model Informational Settings Transparency Partial Opaqueness Complete Opaqueness

Setup of the Model Informational Settings Transparency full information benchmark case Partial Opaqueness Complete Opaqueness

Equilibrium: Solution Strategy Determine cutoff values between order submission strategies, taking execution probabilities as given These values are levels of β at which the trader at time t is indifferent between two specific strategies

Equilibrium: DM in Isolation Equilibrium order submission strategies:

Equilibrium: CN in Isolation Equilibrium order submission strategies:

Equilibrium: DM & CN Equilibrium order submission strategies for given probability p :

Equilibrium: DM & CN The cutoff points are dynamic: Buy side CN/DM:

Empirical Predictions Do there exist systematic patterns in order flow ? What is the effect of a DM or a CN order on future order flow ?

Empirical Predictions Order flow after a DM order at time t : “The direction of previous DM trades does not affect subsequent order flow” Effect of a CN order at time t to order flow to CN/DM: "CN buys are more likely to be preceded by CN sells compared to other orders: CN sells ‘invite’ CN buys“ “DM buys are more likely to be preceded by CN buys compared to other orders”

Different Informational Settings Transparency : benchmark >< reality: CN order book = Opaque Different Informational Settings: Complete Opaqueness & Partial Opaqueness

Different Informational Settings Under opaqueness, traders are unable to condition their strategies on CN order book information Complexity of model increases tractable 2-period model to compare cases Main Result Systematic patterns in order flow for transparency and partial opaqueness, but not for complete opaqueness

Concluding Remarks Dynamic model: interaction between a CN and a DM Order creation due to introduction of CN For transparency and partial opaqueness cases: even with random arrival of buyers and sellers and despite the absence of asymmetric information, systematic, non-random patterns in order flow arise Results are robust to introduction of uncertainty

Uncertainty We now introduce uncertainty and time variation in the value of the asset V Assume V t follows a random walk: Dealers set each period A t and B t around V t Traders forecast the final value of the asset V T, and the price of the cross (A T +B T )/2

Uncertainty New cut-off betas:

Uncertainty Cutoff values more time dependent, and reflect also uncertainty about V Using the new cutoff betas, propositions remain valid and systematic patterns in order flow still exist