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 WuytsUniversity of Leuven

Frontiers of FinanceBonaire, 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 & ContributionsSetup of the ModelEquilibrium– Markets in Isolation– Equilibrium: DM and CN

Empirical PredictionsDifferent Informational SettingsConcluding Remarks

Related Literature & Contributions

Static

Dynamic

One market Interaction 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 economyAgents decide upon consumption on both days:

is the subjective preference or typeAsset 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

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 & CNEquilibrium 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 ResultSystematic 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 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

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