informed trading in parallel auction and dealer markets the case of the london stock exchange

7/29/2019 Informed Trading in Parallel Auction and Dealer Markets the Case of the London Stock Exchange

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Informed Trading in Parallel Auction and Dealer Markets:

An Analysis on The London Stock Exchange

Pankaj K. Jain

Christine Jiang

Thomas McInish

andNareerat Taechapiroontong

Department of Finance, Insurance, and Real Estate

Fogelman College of Business and Economics

The University of Memphis


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Objectives and Contributions

Test whether intensity of trader anonymityis correlated with trading with informedtraders (adverse selection)Use permanent price impact (PPI) of trades to

gauge information content of orders in 2

parallel markets Provide evidence based on unique structure of the

LSE. Compare adverse selection problem between parallel

anonymous Auction market and non-anonymousvoluntarily Dealer market.

Fully time-synchronized markets and no firm-specificdifferences (same firms)


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Fragmented vs consolidated markets

Market Dominance HypothesisChowdry and Nanda (1991 RFS)

Winner takes all. Migration by both uninformed andinformed to the most liquid market.

Glosten (1994 JF) Is the electronic limit order book inevitable?

Market Co-existence HypothesisMadhavan (1995 RFS)

Consolidation with full disclosure of trading information

Fragmentation without disclosure of trading information3


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Previous Studies on Trader Anonymity

Survey: Institutional investors prefer to trade in anonymousautomated execution systems that provide low disclosure ofidentity of the company submitting the orders.Economides and Schwartz (1995) and Schwartz and Steil (1996)

Theory: Negotiated dealer market serves as a screening deviceto eliminate informed trades.

Seppi (1990) and Pagano and Roell (1992)

Professional non-anonymous relationship between specialistand brokers reduces the adverse selection problem.Benveniste et al. (1992)

Off-exchange dealers are likely to cream skim order flow anddivert informed orders to on-exchange market.Easley, Kiefer and OHara (1996)

Upstairs dealer market facilitates searching and matching oforder flow.Seppi (1990), Burdett and OHara (1987) and Grossman (1992)


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Previous Studies on LSE & other markets Friederich and Payne (2007 EJ)

execution and information risks govern the choice of order executionvenue between SETS and dealer markets

market wide liquidity shocks generate commonality off book liquidity suppliers perform stabilization like specialists

Ellul, Shin, and Tonks (2005 JFQA) Opening and closing call auctions

call market dominates the dealership system in terms of price discovery call suffers from a high failure rate to open and close trading, especiallyon days characterized by difficult trading conditions

call's trading costs increase significantly when (a) asymmetricinformation is high, (b) trading is expected to be slow, (c) order flow isunbalanced, and (d) uncertainty is high

Other parallel market studies Heidle and Huang (2002) NYSE/AMEX vs Nasdaq Gramming, Schiereck, and Theissen (2001) Frankfurt systems Booth et al. (2002) upstairs price impact lower in Helsinki

Smith et al. (2001) upstairs price impact lower in Toronto


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Customer submits order to member firms with/without trading venues

Member firm handle order in one of three ways according to customers instructions

Dealership systemexecutes entire order against

his own inventory (principal

cross) or matches order with

other customers order(agency cross)

Mixpartially executed in dealer

systems and work the rest

in limit order system.

SETS limit order booksystem

submits as market order that

executes immediately or as a

new limit order

Member must report all trades from dealer

systems within 3 minutes, except WorkPrincipal Agreement orders.

All orders executed in SETS limit

order system are automatically

reported.

Order flow of SETS stocks on the London Stock Exchange


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Trading mechanism Order-driven electronic limit order book

market

Quote-driven multiple dealer

telephone market

Liquidity providedby Public limit orders and voluntarily dealers Dealers

Access Members only Members only

Trader Anonymity Pre-trade but not post-trade Non-anonymous

Pre-tradetransparency

All outstanding limit order book pricesand sizes are available to member firms. A

member firm can observe the entire limit

order book and the ID number of the

broker placing the limit order.

No pre-trade information is availableto public. Quotes are provided based

on bilateral inquiry.

Post-trade

transparency

Immediate trade report. Identity of the

counterparties are fully revealed when

transaction is confirmed.

Trade report delay up to 3 minutes and

incomplete for Work Principal

Agreements

Minimum order size No minimum No minimum; Smaller orders are

generally routed to retail service

providers (RSPs) for immediate

execution

Settlement period T+5 No standard settlement

Comparison ofSETS and Dealer markets in 2000


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Data selection and processing

Main data source: London transaction data service Compustat global file used for Market capitalization

SETS stocks: FTSE 100 or FTSE 250 in 2000 Trading days >=80 days in 2000 Sample stocks: 177

Delete 28 stocks for methodological problem Final sample: 149

Trade Reports File contains: Trade direction (buy or sell) Trade location (SETS or Dealer) Code that identifies each counterparty, but there no

information concerning their actual identity

Standard trade and quote filters are applied


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Methodology

Keim and Madhavan (1996) and Booth et al. (2002)sprice impact measures:

Permanent price impact = BS*ln (PA/PB) =inform. content Temporary price impact= BS*ln (PT/PA) =liquidity cost Total price impact = BS*ln (PT/PB)

Note: BS is buy/sell indicator; PB,PA,PT are before, after, and trade prices


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Fig. 1. Cumulative average returns around large GBP trades. We identify the 5% of trades that have the greatest GBP

value. We label each of these trades, in turn, as trade 0. For each trade 0, we identify the twenty previous trades, trades -1

through -21, and the subsequent 21 trades, trades +1 through +21. We calculate the return for each trade from -20 to +20 as

the difference in the log of the trade price minus the log of the previous trade price. These returns are averaged and

cumulated beginning with trade -20. Mean values of cumulative average returns are plotted above.

-0.0003

-0.0002

-0.0001

0

0.0001

0.0002

0.0003

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

Trade relative to trade at tim e 0

PercentageCumulativeReturns

SETS buy Dealer buy SETS sell Dealer sell


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Table 4. Information Differences on SETS and Dealer

Permanent Price Impact

Temporary Price

Impact

Independentvariables Coefficient t-statistics Coefficient t-statistics

Intercept -0.2224 -1.74 1.3699* 12.19

SETS 0.2849* 21.62 -0.1956* -16.92

Cap 0.0008 0.08 -0.0024 -0.26

Price 0.0274* 2.00 -0.1060* -8.82

Volatility 0.1031* 4.90 0.1176* 6.37

Freq -0.0399* -4.77 -0.0180* -2.45Size 0.0311* 2.28 -0.0948* -7.93

dj. R2 0.7062 0.7836

F-value 119.97 175.58


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Conclusions

Regulators of the London Stock Exchangeaccomplished their goals of providing efficientmarkets by offering alternative trading venues.

Dealers compete effectively with SETS

More number of trades on SETS Larger trade size on dealer market Price impact measures suggest that SETs trades

have larger information content

Dealers effectively screen out informed tradersor charge them more for providing liquidity

informed trading in parallel auction and dealer markets the case of the london stock exchange

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