Hans Degryse , University of Leuven and CentER Mark Van Achter , University of Leuven

1. Frontiers of Finance Bonaire, January 13-16, 2005 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

2. 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))

3. 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"

4. 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

5. 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

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

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

8. Related Literature & Contributions • 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.

9. 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 C2 on day 2 • Trading takes place during the first day, claims to the asset are exchanged for C1

10. 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

11. 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

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

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

14. Setup of the Model • Informational Settings Transparency Partial Opaqueness Complete Opaqueness

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

16. 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

17. Equilibrium: DM in Isolation • Equilibrium order submission strategies:

18. Equilibrium: CN in Isolation • Equilibrium order submission strategies:

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

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

21. 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 ?

22. 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”

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

24. 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

25. 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

26. Uncertainty • We now introduce uncertainty and time variation in the value of the asset V Assume Vt follows a random walk: • Dealers set each period At and Bt around Vt • Traders forecast the final value of the asset VT, and the price of the cross (AT+BT)/2

27. Uncertainty • New cut-off betas:

28. 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