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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