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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 H. McInish
and
Nareerat Taechapiroontong
July 2003
JEL classification: G10; G14; G15
Key words: Anonymity; Informed trading; Price impact; Multi-market trading; London
Stock Exchange
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Informed Trading in Parallel Auction and Dealer Markets:
An Analysis on the London Stock Exchange
Abstract
In this paper, we examine whether there is more informed trading in an
anonymous market than in a non-anonymous market. It is widely believed that
institutional traders prefer to trade in an anonymous environment, whereas market
markers in a non-anonymous setting protect themselves by diverting informed trades to
other trading venues. We conduct our analysis by using the unique institutional
characteristics of the London Stock Exchange where an anonymous electronic order book
market (SETS) operates alongside a non-anonymous voluntarily dealer market. We
measure asymmetric information using metrics based on both trade flow (PIN) and priceimpact. Whereas PIN is equal on the markets, our evidence regarding the price impact of
trades shows strong support for the ability of dealers to identify informed order flow. The
permanent price impact of trades is significantly lower on the Dealer market than on
SETS suggesting that informed traders do not benefit a lot from dealers.
We also find that the Dealer market can compete effectively with SETS for
uninformed order flow. Institutions also appear to prefer trading with dealers as most
large trades are routed to the Dealer market and such sizes are not obtainable on SETS.
Finally, we show that the temporary price impact of trades on the Dealer market is
significantly larger than on SETS, indicating that liquidity for large trades, can be
acquired on the Dealer market, but it comes at a considerable cost.
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Introduction
The existence of alternative trading venues is common in major equity markets.
Each trading venue may cater to different types of traders. Institutional traders prefer to
trade in an anonymous environment where their identities are not revealed to the public.
In turn, market markers in a non-anonymous setting protect themselves by either
diverting informed trades to other trading venues or charging a bigger transaction cost
premium from them, which inflates the temporary price impact.
The main purpose of this study is to test whether there is more informed trading in
an anonymous market than in a non-anonymous market and how each market deals with
informed traders. Our study is distinguished from previous work in that we use the
unique market structure of the London Stock Exchange (LSE) where an anonymous
electronic order book market (SETS) operates concurrently with a non-anonymous
voluntarily dealer market. There is no requirement for interaction between the two
markets. The LSE differs from other major markets in a number of ways. On the New
York Stock Exchange (NYSE), a specialist has an obligation to maintain narrow spreads
and stabilize prices. Upstairs brokers must expose negotiated trades to the downstairs
floor and to the order book. In Paris, only trades over a given size threshold can be
executed off-book, and only at or within the weighted average book price. The hybrid
structure of the LSE has not yet been examined in the context of informed trading. In
addition, other existing studies on the LSE using comparable data only analyze the
interaction of order flow between two markets.1 This study is made possible by using the
unique characteristics of a rich dataset provided by the LSE’s London Transaction Data
1
See Friederich and Payne (2001) and Ellul (2001)
3
Service. These data contain details on location of order arrival and trade execution. Such
details enrich the research design of our study on multiple markets.
This study contributes to the literature in several ways. First, we test the trader
anonymity hypothesis by using the unique structure of the LSE. In particular, unlike a
number of studies on auction and dealer markets that compare a set of matched sample
stocks when each market stands in isolation, our research design compares the risk of
trading with informed traders between two parallel markets, an anonymous auction and a
non-anonymous voluntarily multiple dealer market. Our total sample comprises 149
stocks that trade simultaneously on the SETS and Dealer markets, which results in apples
to apples comparison. Second, we use two alternative approaches to understand the
dynamics and treatment of informed order flow on the two markets. One approach is the
Easley, Kiefer, and O’Hara’s (1996) (hereafter EKO) model to estimate the probabilities
of informed trading (hereafter PIN) in the two parallel markets from trade flows and
another approach is to use prices to calculate the price impact of trades as suggested by
Keim and Madhavan (1996), and Booth, Lin, Martikainen, and Tse (2002), among
others.2 Third, we investigate the cross-sectional determinations of the probability of
informed trading, spread, depth, and the price impact of trades, in turn.
We provide a number of findings. For the sample as a whole, the PIN on SETS is
approximately equal to the PIN on the Dealer market. However, the permanent price
impact of trades is not only significantly lower on the Dealer market than on SETS, but is
actually negative on the Dealer market. This provides strong evidence that dealers on the
2
Holthausen, Leftwich, and Mayer (1987, 1990)
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Dealer market are able to effectively identify informed trades as predicted by the
theoretical models of EKO, Seppi (1990), Pagano and Roell (1992), Benveniste, Marcus,
and Wilhelm (1992) and the empirical results of Gramming, Schiereck, and Theissen
(2001) and Heidle and Huang (2002).
We also find that the Dealer market can compete effectively with SETS for
uninformed order flow. Institutions may prefer to trade with dealers who are able to
execute large trades that are not obtainable on SETS. We find that most large trades are
routed to the Dealer market. The level of β , which is the probability that uninformed
trades occur on SETS, is about 0.5, indicating that SETS and the Dealer market execute
uninformed trades with about equal probability. The temporary price impact of trades is
significantly larger on the Dealer market than on SETS, indicating that liquidity for large
trade sizes can be purchased on the Dealer market, but at a significant cost.
The remainder of this paper is organized as follows. Section I reviews the
literature dealing with information-based model, market fragmentation, multiple markets,
off-exchange trading, upstairs markets, market transparency, and comparison of dealer
and auction markets with respect to the intensity of trader anonymity. This review leads
into development of six testable hypotheses. In Section II, a background of the London
Stock Exchange is provided, followed by the details of the data selection and processing.
In section III, we explain the model of EKO, methods to test our hypotheses, and
methods to measure the price impact of trades. In Section IV, we present the results and
provide possible explanations. Section V concludes with a summary and directions for
further research.
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I. Literature review and testable hypotheses
A. Information-based models and competition between trading systems
A series of papers starting from Copeland and Galai (1983), Glosten and Milgrom
(1985), Kyle (1985), Easley and O’Hara (1987), Admati and Pfeiderer (1988), and
Glosten (1989) develop information based-models focusing on strategic behavior of
traders. The gist of these models is a pricing problem, which a rational market maker
faces given a fraction of informed traders. A risk neutral market maker sets bid and ask
prices to maximize profit by balancing gains from trading with liquidity traders and
losses from trading with informed traders. Spreads are wider when more or better
information enters the market or when the ratio of informed traders to liquidity traders
increases.
The dynamics of this interaction between market makers, informed traders, and
liquidity traders can be affected by the degree of transparency, which differs significantly
between the centralized SETS and the fragmented dealer market in London. Biais (1993)
develops a model comparing centralized and fragmented markets based on market
transparency when there is no private information. He shows that the equilibrium bid-ask
spread is equal in the two markets, but more volatile in centralized markets than in
fragmented markets. Madhavan (1995) proposes a model providing a rationale for the
existence of fragmented markets, focusing on the impact of disclosing trading
information to market participants. He shows that informed traders and large traders who
place multiple trades obtain lower expected trading costs in fragmented markets where
their trades are not disclosed. On the other hand, dealers benefit from nondisclosure by
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decreasing price competition. Therefore, trading in a fragmented market will not
necessarily integrate into one market.
Examining liquidity, Chowdhry and Nanda (1991) provide a theoretical model of
multi-market trading with informed and liquidity traders by extending the frameworks of
Kyle (1985) and Admati and Pfleiderer (1988). Their models predict that if more than
one market for a security exists, one market will emerge as the dominant market, a
“winner takes most” phenomenon. This prediction occurs as liquidity traders seek thick
markets with the lowest execution costs and informed traders maximize their profits by
hiding trades in the most liquid markets. Glosten (1994) suggests that the open electronic
limit order book is inevitable because it provides as much liquidity as possible in extreme
situations and does not invite competition from third market dealers, while other trading
institutions do. We test these predictions in the following hypothesis:
Hypothesis 1: When exchanges are allowed to compete, open electronic limit
order books such as SETS dominate and any other market such as the Dealer Market is
unable to compete effectively.
B.Informed trading in co-existing upstairs and downstairs markets
Another branch of literature on market fragmentation allows for coexistence of
parallel markets and the discussion centers around trading between upstairs and
downstairs markets. Easley and O’Hara (1987) develop the information-based trading
model to explain why large (block) trades are made at less favorable prices than small
trades. In addition, these block trades have persistent price effects. In particular,
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transaction prices are lower after block sales and higher after block buys, with only a
partial price reversion to their previous levels.
Seppi (1990), extending the work of Easley and O’Hara (1987), develops a
framework in which dealers (upstairs block traders) are able to differentiate uninformed
traders from informed traders based on reputation signals or other implicit commitments.
This non-anonymous feature in upstairs markets enables the dealers to screen out
informed traders from the upstairs market, and, as a result, lowers adverse selection costs
for large liquidity traders. He shows that there is a separating equilibrium in which the
large trader uses blocks to rebalance portfolios and uses specialists to trade on
information. He argues that the lack of anonymity in off-exchange block trading enables
investors and the dealers to make “no bagging the street” commitments and face penalties
on any subsequent trades if they fail to divulge information. In contrast, this type of
agreement is not possible in an anonymous exchange trading. Thus, the upstairs markets
serve as a screening device to eliminate information motivated trades.
Grossman (1992) also dwells upon the information role of upstairs (fragmented)
and downstairs (centralized) markets. He suggests that many large traders do not want to
expose their orders to the public since such large trades may adversely impact the market
price, may invite front running by other traders, and may introduce a free option problem
(the risk of being picked off if market conditions change). A large order sent to the
upstairs market is less exposed than one sent to the downstairs market and may be
matched with other unexpressed liquidity. As a consequence, upstairs dealers serve as a
repository of information on large investors’ hidden trading interest. To sum up,
Grossman provides a testable prediction in that since upstairs dealers offer higher
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effective liquidity, the total price impact of the upstairs market is lower than that of the
downstairs market.
Another model focusing on asymmetric information is proposed by Easley,
Kiefer, and O’Hara’s (1996), hereafter, EKO. The authors show that the practice of
“cream skimming” by dealers or trading locales supports the existence of off-exchange
trading or market fragmentation. Dealers in off-exchange locales mitigate losses from
trading with informed traders by purchasing retail order flow3 or seeking only
uninformed trades, and, as a consequence, diverting the remaining informed trades to the
primary market. Thus, cream skimming permits uninformed traders in off-exchange
markets to benefit from lower costs. EKO focus on information in trade flow rather than
prices to infer any variations in information contents between trading locales. The model
relies on the intuition that the number of trades arriving at the start of each day reflects
good news or bad news.
Keim and Madhavan (1996), extending the works of Burdett and O’Hara (1987),
Grossman (1992), and Seppi (1990), develop a theoretical framework of the upstairs
market where order size, beliefs, and prices are determined endogenously. They show
that information sharing and risk sharing among traders in the upstairs market can reduce
price impact. They also suggest that the benchmark price to calculate the permanent price
impact should include the period prior to trade as a result of the information leakage.
Empirical support to these predictions are provided by Madhavan and Cheng (1997) and
Bessembinder and Venkataraman (2001).
3
Purchased order flow refers to the practice of dealers or trading locales paying broker for retails order
flow. In general, this purchased order is guaranteed to be executed at the best prevailing price.
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C. Informed trading in co-existing auction and dealer markets: Role of trader anonymity
The dynamics of strategic behavior by market makers, informed traders, and
liquidity traders can also be affected by trader anonymity on the limit order and the dealer
markets. Economides and Schwartz (1995) and Schwartz and Steil (1996) surveyed
institutional traders in North America, Europe, and Australia, and found that institutional
investors prefer anonymous automated execution systems that provide low disclosure of
identity of the trader submitting the order. Pagano and Roell (1992) provide a discussion
of the relative benefits of auction and dealership markets with respect to the degree of
transparency. Similar to the upstairs markets literature, they argue, negotiated dealership
markets offer opportunities for screening of informed traders. This implies that dealers
would trade with those uninformed while informed traders would trade in the order book
or pay a bigger premium for trading large quantities with dealers. Pagano and Roell
(1996) propose that increasing the transparency of the trading system can decrease
trading costs. Similarly, Forster and George (1992) show that disclosure of the direction
and size of liquidity trades in advance of trading can reduce the expected transaction
costs of liquidity motivated traders, providing a motivation for so-called ‘sunshine
trading’. Benveniste, Marcus, and Wilhelm (1992) conjecture that in non-anonymous
specialist market structure the floor brokers have to repeatedly interact with the specialist
who is able to ex-post identify the brokers with information-based trades. The specialist
also has the means to sanction those brokers who fail to reveal information-based trades
by not providing better trade prices, refusing to fill orders above the quoted depth, or
unwillingness to help “work” a large order.
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The predictions of the theoretical models of Seppi (1990), Grossman (1992),
Easley, Kiefer, and O’Hara (1996), Pagano and Roell (1992, 1996), and Benveniste,
Marcus, and Wilhelm (1992) have been empirically verified by De Jong, Nijman, and
Roell (1996), Smith, Turnbull, and White (2001), Fong, Madhavan and Swan (2001), and
Booth, Lin, Martikainen, and Tse (2002) in the context of upstairs trading on the Paris
Bourse, the Toronto Stock Exchange, the Australian Stock Exchange, and the Helsinki
Stock Exchange, respectively. These studies find that the permanent price impact and
adverse selection costs in negotiated upstairs markets is very low. They explain that offexchange trading is not anonymous, and that asymmetric information plays less of a role
in that market because of its ability to screen out the information motivated trades. In
contrast, the downstairs market is an anonymous electronic order book, and, therefore,
vulnerable to adverse selection problems. Nevertheless, the downstairs markets provide
lower total execution cost for small trades. The existence of off-exchange and upstairs
market provides a market with more efficient trading in terms of liquidity and do not
harm the anonymous downstairs market.
Franke and Hess (2000) propose that the information differential between an
anonymous screen-based trading system and a non-anonymous floor trading system
should increase the attractiveness of the latter in the times of high information intensity,
which is measured by high volatility, high volume, and high trading frequency.
Consistent with their hypothesis, they show that the order book market’s market share is
decreasing in trading volume and price volatility.
Two recent empirical studies are closely related to ours. Heidle and Huang (2002)
investigate whether auction markets (NYSE, AMEX) or dealer markets (NASDAQ) are
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better able to identify informed traders. Gramming, Schiereck, and Theissen (2001)
examine the relation of degree of trader anonymity and the probability of informed
trading on the two parallel markets at the Frankfurt Stock Exchange. Both these studies
are based on the concept that the non-anonymous environment permits market makers to
draw inference about the motives behind trades. These authors implement and extend a
classical model of Easley, Kiefer, O’Hara, and Paperman (1996) (hereafter EKOP) in
their studies to estimate the PIN. The analysis in these studies shows that traders in
multiple dealer markets are more anonymous than those in auction markets. They also
find that the probability of informed trading is lower on non-anonymous floor-based
trading markets and directly related to variables that proxy for the degree of anonymity
such as spread and adverse selection components. The decrease in spread is greater for
firms with higher PIN prior to transferring from a dealer to an auction market. They
conclude that the differences in the market structure result in the differences in risk of
informed trading as informed traders prefer pre-trade anonymity.
The testable implications that arise from the information content and price impact
dimension of trades, as discussed in sub-sections B and C above, are stated in the
following related hypotheses:
Hypothesis 2: There is a significant difference in information content of trades
between an anonymous Auction and a non-anonymous Dealer markets.
Hypothesis 3: The probability of informed trading on Auction markets is greater
than that on Dealer markets.
Hypothesis 4: The permanent price impact of trades on Auction markets is greater
than that on Dealer markets.
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D. Liquidity effects on price impact on auction versus dealer markets
As addressed in Seppi (1990), Burdett and O’Hara (1987), and Grossman (1992),
off-exchange dealer markets involve a process of searching and matching of order flows.
Temporary price concessions are needed to induce counterparties to trade large sizes.
This temporary price impact (liquidity effect) should be larger for an off-exchange dealer
market than for an anonymous market.
Hypothesis 5:The temporary price impact on Auction markets is less than on
Dealer markets.
Grossman (1992) proposes that the off-exchange market is a repository of
information about the unexpressed demand of customers. This information role of dealers
allows them to offer customers better price improvement than on the exchange market.
This argument also relates to hypothesis 3 and hypothesis 4 in that total price impact can
be decomposed into permanent and temporary components. If the magnitude of the
difference in the permanent price impact outweighs the magnitude of the difference in the
temporary price impact, the total price impact will be lower in an off-exchange market.
We test whether the following hypothesis holds.
Hypothesis 6:The total price impact on Auction markets is greater than that on
Dealer markets.
II. Institutional background, data and methodology
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A. The London Stock Exchange
The London Stock Exchange (LSE), which is one of world’s leading stock
exchanges, has experienced significant transformation to maintain and compete for order
flow and to improve price discovery. Before October 1997, the LSE was a pure quotedriven dealer market (SEAQ) that was relatively nontransparent about the order flow.
There were no reports of collusion, but order flow was concentrated among five large
market makers, and, consequently, there was dissatisfaction among traders. Moreover,
retails investors complained that they were subsidizing large traders. These problems
cause order flows to migrate to other European markets.
In 1997, the LSE began to implement a phased introduction of a more transparent
but anonymous order-driven auction market called the Stock Exchange Electronic
Trading Service (SETS) to replace the SEAQ market for most liquid stocks. At first
SETS traded stocks in the FTSE 100 index, but over time the stocks covered increased
and in 2003 roughly 217 stocks from the FTSE 250 index are covered. Thin stocks that
have never been components of these two indices are traded only on an old quote-driven
market (SEAQ) and are not included in our study.4
Dealers on the LSE can compete voluntarily for trades on SETS’ stocks on an offexchange dealer market, but are no longer obliged to post firm bid and ask prices as they
did earlier and their quotes are no longer available to investors through publicly available
price-display mechanism. Trades on the dealer market are not constrained by limit order
prices on SETS or required to be partially executed against the limit order book as
required by other hybrid markets such as the NYSE, Toronto Stock Exchange, Paris
4
A few stocks that have been deleted from these indexes continue to be traded on SETS.
14
Bourse, or Helsinki Stock Exchange. Investors can choose their trading venues depending
on their motivation. Investors, who require prompt and anonymous transactions, may
prefer to execute market orders against the book in SETS. Passive customers may choose
to place limit orders on the book. Large traders, who do not want their trades to create
extensive impact on prices in an order book market, may prefer to trade off-book on the
Dealer market.
Fig. 1 illustrates how an order for SETS stocks is routed on the LSE. All orders
must be submitted to member firms (dealers), previously called market makers, to handle
the orders. Customers can instruct the member firm to execute the order immediately on
SETS at the best available price on the limit order book or to place the order in the limit
order queue on SETS. Alternately, the customer can instruct the broker to execute the
trade immediately against the dealer’s inventory (principal cross) or to cross the trade
against other customers’ orders (agency cross). Dealers also trade with each other. In the
dealership market dealers are free to trade or not trade as they wish. Customer can also
split their order between the two markets.
Table 1 compares the characteristics of the SETS and Dealer market structures in
2000. Access to both SETS and the Dealer market are permitted only to brokers/dealers
who are registered members of the LSE. Being a member enables dealers to connect
directly to the exchange market. In addition, eligible members are exempted from stamp
duty of 0.5% of share purchase value. Nevertheless, members must strictly follow rules
and regulations for trading and reporting. Violations are subject to considerable fines.
The limit order book market of the LSE is very transparent with respect to order
flow and trade execution. Member firms can see all outstanding limit orders on the
15
exchange screen. All trades on SETS are immediately reported for publication. However,
the identity of traders is not displayed ex-ante on SETS. On the contrary, dealers know
the identity of traders ex-ante but the quotes and depths on the Dealer market are not
available to the public. Trades are conducted via telephone and must be reported within
three minutes of execution except for block trades greater than 8 NMS5 that involve a
“Work Principal Agreement.” These must be reported after the entire order is completed.
In most cases, dealers’ trading systems report trades automatically.
Trading in the dealership market does not rely solely on bilateral negotiation, but
also uses the retail service providers (RSPs) system available from three broker-dealer
firms. RSPs provide terminals for execution of retail orders without negotiation. The
execution is also guaranteed to be within the book spread. Thus, this service directly
competes with the limit order market. There is no minimum order size on SETS or in the
dealership market. The standard settlement period for SETS trades in 2000 was T+5.
Settlement periods on the dealership market show considerable variability.
There are a few studies analyzing the hybrid market at the London Stock
Exchange after the introduction of the limit order market (called SETS) in 1997. The
institutional details are described in further details in those studies. Naik and Yadav
(1999) study the effect of the reform and find that trading cost for public investors is
lower than before transformation. Friederich and Payne (2001) examine order flow
interaction between a limit order book and a dealer market focusing mainly on price
volatility and the liquidity role of dealers. They conclude that dealers stabilize the market
5
NMS is normal market size measured from the average institutional trade size in a stock as computed and
regularly updated by the Exchange.
16
by supporting liquidity when trade size is above average or depth is low. Ellul (2001)
investigates patterns in volatility and examines trader choice using the selection model
introduced in Madhavan and Cheng (1997). He reports that dealers subsidize markets.
Ellul, Shin and Tonk (2002) investigate trades during opening and closing call auction
periods and find that call auctions are more likely to be used for trading large stocks.
B. Data selection and processing
This study uses data provided by the London Stock Exchange for stocks that are
components of either the FTSE100 or FTSE250 indices in 2000, designated as SET1 or
SET2. These stocks are traded on both the SETS and the Dealer markets. Normal trading
hours are 8:00 am to 16:30 pm and we exclude trades and quotes outside these hours. All
trades are in GBP.
These data comprise a number of files. For each trade, the Trade Reports File has
the firm symbol, date, time, price, number of shares, whether the trade is buyer- or sellerinitiated, which market was used for the trade (SETS or Dealer), type or order (market,
limit), special designations (such as fill or kill), and the settlement date. We note that
trade direction on the dealer market on the LSE is from the point of view of the dealers
trade, so a dealer buy (sell) is assigned as a sell (buy). We exclude trades with settlement
dates greater than SETS’s standard settlement date of T+5, trades with a price or volume
of zero, and trades with size greater than 8 NMS and trades designated “WT” (which are
≥ 8 NMS and are subject to a Work Principal Agreement), “UT” (occurring during
opening and closing call period), “RO” (resulting from an option exercise), “SW”
(resulting from a stock swap), “CT” (contra trades), and “PN” (work principal portfolio
17
notification). We also exclude trades for which |(pt – pt-1)/pt-1| > 0.5 where pt is the trade
price at time t.
All quote data are from SETS; no quote data are provided by the Dealer market.
The Best Prices File includes the time and price (but not the depth) of all quote updates
that are better than an existing bid or ask on SETS. We exclude quotes with either the
ask, bid, ask size or bid size less than or equal to zero, and for which |(at – at-1)/at-1| > 0.5 or
|(bt – bt-1)/bt-1| > 0.5, where at is the ask quote and bt is the bid quote.
For all orders submitted to SETS, the Order History File contains details about the
date and time when the order is entered, deleted, cancelled, or executed, along with its
order type, quantity, and limit price. We use these details to obtain aggregate depth at
each best limit price.
Due to mergers, new listings, and delistings, stocks leave and join the index
during the year and to ensure a sufficient sample period, we use only stocks that are
members of either index for at least eighty days during the year 2000. The final sample
comprises 177 firms after deleting 16 stocks not meeting the requirements enumerated
above. An additional 28 stocks were subsequently deleted due to methodological
considerations6. The average market capitalization for these firms, obtained from the
Compustat global file, is 7.5 billion pounds and the average stock price is 6.59 pounds.
C. Model of informed trading and estimation procedure
We utilize the sequential trade model developed by EKO to estimate the
probability of informed trading in two parallel markets. A benefit of this model is that we
6
EKO model cannot generate reasonable estimates for very high volume stocks. (Easley, Ohara, and Saar
(2001))
18
can compute the PIN from parameters that are estimated entirely from trade data rather
than focusing on prices, news, or specific events. More importantly, these parameters are
estimated at once for different markets, not separately as most studies using EKOP. The
model aggregates all trades in each market during one day and relies on buy and sell trade
imbalances to identify informed trading. Trade arises from the actions of informed and
uninformed traders who participate in the market with a risk neutral and competitive
market maker at prices set by the market maker. The market maker sets the trading price
based on his belief about the underlying true value of the asset, which is formed and
updated conditional on his estimate of the occurrence of an information event. Parameters
for information-based trading are derived from the ability of market makers to identify
trades that arise as the result of private information held by informed traders as opposed
to those that arise from the liquidity needs of uninformed traders.
In the model, there are two markets in which the security trades, denoted S for the
limit order book and D for the off-book dealer market. There is a market maker in each
market. Market makers see trades in each market. EKO assume that nature determines the
occurrence of a news event only before trading begins each day. The probability of a
news event or information event is given by α . In the event of new information, nature
further determines whether the information signal was bad news with probability δ or
good news with probability of (1 − δ ) . After the information signals have been
determined, trading begins with two types of traders. Informed traders arrive at the rate of
µ on either buy or sell side or don’t arrive at all depending on a good, bad, or no private
information signal. Uninformed traders arrive according to an independent Poisson
19
processes on both the buy side and sell side of the market, each with an arrival rates of ε b
and ε s , respectively. The arrival rates ε b , ε s and µ are defined as the number of trades
per trading day.
Orders can be executed in either the limit order book on SETS (S) or the Dealer
market (D). Let β denote the probability that an uninformed order will execute on market
S, and 1 − β the probability that it executes on market D. Likewise, let γ denote the
probability that an informed order is executed on market S, and 1 − γ , the probability that
it does so on market D. Fig. 2 illustrates EKO’s model of the trading process.
The information process of the model assumes that order arrivals follow one of
the three independent Poisson processes such that more buys are anticipated on good
event days, more sells are anticipated on bad event days, and fewer trades in general are
anticipated on no event days as there are no informed traders in the market. Since an
imbalance in the numbers of buys and sells arises from the arrival of informed traders
who participate only on one side of the market, this imbalance indicates informationbased trading. The parameters θ = (α , δ , ε b , ε s , µ , β , γ ) are not directly observable, but
the arrival of buys and sells is observable. Since we are unable to observe which process
is operating on any given day, the likelihood of order arrivals is a weighted average of the
likelihood of observing a particular number of buy and sell orders on a good, bad, and no
event days, respectively. The weights are probabilities of each type of day occurring and
are given by αδ for bad event days, α (1 − δ ) for a good event days, and (1 − α ) for a no
event days. Therefore, the likelihood function for a single trading day for a given stock is:
20
L(BS , SS , BD , SD | θ ) =
αδe−ε βT
b
(ε b βT ) BS −( µγ +εsβ )T [(µγ + ε s β )T ] S
*e
BS !
SS !
S
+ (1 − α )e−εbβT
(ε b (1 − β )T ) BD −( µ(1−γ )+εs (1−β ))T [(µ(1 − γ ) + ε s (1 − β ))T ] D
*e
SD !
BD!
S
(ε b βT ) BS −εsβT (ε s βT ) SS
(ε (1− β )T ) BD −εs (1−β )T (ε s (1− β )T ) SD
*e
* e−εb (1−β )T b
*e
SD !
BS !
SS !
BD!
+ α(1 − δ )e−(εbβ +µγ )T
*e
*e−εb (1−β )T
[(ε b β + µγ )T )]B
−ε s (1−β )T
S
BS !
* e−εsβT
(ε s βT ) SS −(εb (1−β )+µ (1−γ ))T [(ε b (1 − β ) + µ(1− γ )T )] D
*e
SS !
BD!
(ε s (1− β )T ) SD
SD !
B
(1)
where T, b and s are total trading minutes in one day (510 minutes for the LSE), the total
number of buys and sells per day, respectively, and θ = (α , δ , ε b , ε s , µ , β , γ ) is the
parameter vector. Assuming that days are independent, the likelihood of observing the
data M = ( Bi , S i ) iI=i over I days is simply the production of the daily likelihood:
I
L( M | θ ) = ∏ Li ( BSi , S Si , BDi , S Di | θ )
i =1
(2)
By maximizing equation (2) we obtain direct estimates of the rate of informed
trading ( µ ) from imbalances and uninformed buys and sells ( ε b , and ε s ) from balanced
days as well as the probability that a particular type of information event occurs ( α and
δ ) for each particular stock. For a particular day the maximum likelihood estimates of
the information event parameters α and δ are either 0 or 1, showing that an information
event either occurred only once a day and conditional on its occurring that it is either
good (δ = 0) or bad news (δ = 1) . Over a number of trading days, however, the
probabilities α and δ are estimated by the number of days with unbalanced buys and
sells. The probabilities α , δ , γ and β are restricted to [0,1] by a logit transformation of
the unrestricted parameters, and the arrival rates ε b , ε s , and µ are restricted to (0, ∞) by
21
a logarithmic transformation. The parameter estimates from maximization of the
likelihood function in equation (2), using the quadratic hill-climbing algorithm GRADX
from the GQOPT package, are then used to determine the probability of a trade being
information based in market S (PIS) and market D (PID) as follows
PIS =
PID =
αµ
β
αµ + (ε b + ε s ) 
γ 
and
(3)
αµ
(4)
1− β 

αµ + (ε b + ε s )
 1−γ 
From the above equation, the probability of information based trading is
increasing in the frequency of information events (captured by α ), increasing in the
number of traders that receive private information (captured by µ ), and decreasing in the
number of uninformed traders (captured by ε b and ε s ).
D. Likelihood ratio tests
We use restriction of the general model to test for the differences in
information content between markets as stated in hypothesis 3. Testing whether fractions
of informed trading and uninformed trading are the same in each market is equivalent to
the restriction that γ = β or ( 1 − γ ) = ( 1 − β ). However, if market D is receiving more
uninformed orders than it is informed, then it follows that ( 1 − γ ) < ( 1 − β ) or γ > β .
Therefore, these restrictions allow a direct test of the hypotheses. If dealers do not
tend to screen out informed trades in a stock, then restricting the model to have γ = β
for that stock should have no effect on goodness of fit of the model as measured by the
likelihood ratio statistics. If we reject this restriction, there are two possibilities to
22
consider. First, it is possible that the information content of market D is less than that on
market S. Thus, restriction γ > β should have no effect on goodness of fit, and we will be
unable to reject the restriction. Second, if orders on market D have more information
content than those on market S, then the model restricted to γ < β can not be rejected.
The appropriate test for the restriction that each stock’s information content is the same in
both markets ( γ = β ) is a chi-square test based on the difference in log likelihoods with
and without restriction.
Moreover, we provide tests for differences between the two markets, instead of
any particular stock. To test for entire sample of stocks γ i < β i , γ i > β i for all stocks i,
against γ i = β i , we sum the maximized log likelihood for the sample of stocks given that
information events and trades are independent across stocks. Test statistics for
γ ≤ β ( γ ≥ β ) test equal information relative to more information content in Dealer
(SETS). The appropriate test is the mixture of chi-squares χ 2 (i), i= 0, 1 ,…, n with
 n
mixing probabilities of  i 2 − n , where n is the number of sample stocks7.
 
E. Measurement of price impact
To measure whether there is a discrepancy in the effect of trades on prices
between the SETS and the Dealer markets, we use the method suggested by Keim and
Madhavan (1996), Booth, Lin, Martikainen, and Tse (2002), and among others.8 The
advantage of this method is that all measures use trade prices instead of quotes. We
7
8
See Self and Laing (1987) for appropriate test.
See Holthausen, Leftwich, and Mayer (1987, 1990)
23
exploit this feature, as there are no quotes provided on the Dealer market. The total price
impact can be decomposed into the permanent price impact and the temporary price
impact. The permanent price impact reflects changes in the belief about a security’s value
due to new information conveyed by the trades. The temporary price impact measures
liquidity effects from transitory price reversals. The total price impact reflects the extent
of price concession or the difference between the trade price and the previous equilibrium
price required to absorb the trade.
We assume that a trade occurs at time t with price PT. The equilibrium price
observed at time t-b before trade at time t is PB and the equilibrium price observed at
time t+a after trade at time t is PA. The sequence of trades is b < t < a. We measure price
impact as
Permanent price impact (%)
=
BS*ln (PA/PB)*100
(4)
Temporary price impact (%)
=
BS*ln (PT/PA)*100
(5)
Total price impact (%)
=
BS*ln (PT/PB)*100
(6)
where BS equals plus (minus) one for buyer (seller) initiated trades. This estimation is a
conventional method for studying the price impact of large block trades. We recognize
that not all trades in two markets are large. We investigate trade-by-trade price movement
by trade value groups classified based on percentiles of all trades for each firm. Thus, this
trade size cut-off varies across firms. Fig. 3 illustrates the price movement around large
GBP trades. We identify the 5% of trades that have the greatest GBP value. We calculate
cumulative returns as follows. Each trade is given as trade 0. The previous twenty trades
(regardless of trade location, size, buy/sell) executed prior to trade 0 (-1, -2, -3,…) and 20
trades executed after trades 0 (+1,+2,+3,…) are obtained. Then trade-to-trade returns
24
calculated as the difference in log prices are estimated from each trade from trade –20 to
trade +20. These returns are averaged and cumulated.
Fig. 3 shows that there are large price movements prior to trade 0 for trades on
SETS for both seller-initiated trades and buyer initiated trades. This indicates that there is
information leakage before trade 0. Conversely, price movements before trades on the
Dealer market started immediately prior to trade 0. Prices after trade 0 seem to stay high
for later trades for limit order trades, but after dealer trades prices reverse back. We also
extend our analysis to price movements for 30 trades and 10 trades. The results show
similar patterns. Following Booth, Lin, Martikainen, and Tse’s (2002) analysis, , we
choose equilibrium price PB before trade t at t-12 and equilibrium price after trade t at
t+3 where PB is P-12 and PA is P+3.9 Price movements in other trade value groups show
similar patterns.
IV. Empirical results
A. Competition between trading systems
Table 2 shows that trade size and GBP trade size are larger on the Dealer market.
Hence, the orders that require greater liquidity are going to this market. The daily volume
of trades is actually larger on the Dealer market. Trades greater than 8 NMS on Dealer
market, which are not included in the sample, are executed more frequently than and are
triple daily volume of trades on SETS. Thus, the Dealer market is able to compete with
the electronic market Table 3, shows that the level of β , which is the probability that
9
Booth, Lin, Martikainen, and Tse (2002) find insignificant price movement 5 trades before and 3 trades
after trade t.
25
uninformed trades occur on SETS, is about 0.5, indicating that SETS and the Dealer
market execute uninformed trades with about equal probability. This is more evidence
that the dealers are able to compete effectively. This leads us to reject the first hypothesis
that SETS dominates and invites no competition.
Table 2 also presents the summary statistics for the 149 firms in our sample. On
average, there are approximately 294 trades per day for each firm, of which about 45%
take place on the Dealer market. However, most of the large trades are routed to the
Dealer market, so that the Dealer market has more volume than SETS. Higher relative
spreads and lower depths on the Dealer market indicate that trades are primarily executed
at the Dealer market when SETS is illiquid. All of these results reveal that dealers supply
liquidity to the market.
B. Information content parameter estimates and tests of significance
Table 4 presents the summary statistics of the information content parameter
estimates and the probability of information trading on the SETS (PIS) and the Dealer
(PID) markets. The summary statistics show that all parameter estimates vary extensively
across sample stocks. The probability of an information event, α, ranges from 0.0291 to
0.7828, with a mean and median of 0.3212 and 0.3270, respectively. The probability that
the information is bad news, δ , ranges from 0 to 1. The mean and median of 0.4544 and
0.4283 indicate that the bad news days and good news days are relatively equal on
average across firms.
The number of orders per minute arising from informed traders given that an
information event has occurred, µ , ranges from 0.0352 to1.2441 with mean of 0.2631.
26
The number of buys, ε b , and sells, ε s , per minute arising from uninformed traders are
both about 0.25. This implies that the number of orders arising from uninformed traders
is approximately 0.50 per minute. Thus, on average, the results imply that uninformed
traders trade more than informed traders (0.50 >0.2631).
The mean probability that an informed trade, γ , (uninformed, β ) is executed on
SETS is 0.5293 (0.5360) and on the Dealer market is 0.4707 (0.4640). Surprisingly, the
probability of informed trading occurring on SETS (PIS) and on the Dealer (PID)
markets are approximately the same with means of 0.1565 and 0.1524, respectively. The
highest PIS and PID are 0.4524 and 0.5734, respectively. This preliminary observation
shows that, inconsistent with hypothesis 2, average probability of informed trading in the
anonymous SETS market is not significantly higher than that in the non-anonymous
Dealer market. Tests of equality of means (t-test) and medians (Wilcoxon-test) do not
support the view that there is a difference between PIS and PID.
Table 4 provides tests for hypothesis 3. The log likelihood ratio statistics are
computed as twice the difference of log likelihood under the specified restriction and the
unrestricted model. For the149 sample stocks, the p-value in column 7 shows that 137
stocks reject the restriction model for equality of information γ = β at the 10% level.
Hence, most stocks have a significance difference in the information content of trades
between the two markets. We estimate the statistics in Table 3 separately for each stock
in the sample and find that there are 65 stocks that have probability of informed trading
on SETS larger than on the Dealer market, which accounts for 45% of 149 sample stocks.
In Table 4, the last row reports the test of the difference in the information content of
trades between the two markets and the test of the direction of the information difference
27
between the two markets. The sum of maximized log likelihoods for the unrestricted
case in column 2 (-1,234,172) is larger than for the restricted case for equal information
content in column3 ( -1,281,537), indicating a better fit for the unrestricted case. The
sum of the likelihood ratio test statistics against equal information content of 94,730 is
strongly significant at 0.001 level (critical value of 208.08), supporting unequal
information content.
The tests for the direction of the information difference between the two parallel
markets are reported in the last row of Table 4 with the sum of the maximized log
likelihoods of 21,424 for more informed trading on the Dealer market and 71,716 for
more informed trading on SETS. The appropriate test statistic is the mixture of chi 149 
squares χ 2 (i), i= 0, 1 ,…, 149 with mixing probabilities of  i 2 −149 . Both test
 
restrictions are rejected significantly at the 0.01 level (critical value of 105.77).
In sum, PIN is not capturing the effectiveness of Dealer markets in identifying
informed traders.There are several possible explanations for this. First, unlike previous
research such as Gramming, Schiereck, and Theissen (2001) comparing the parallel
electronic and floor markets where a specialist observes all order flows, we analyze a
hybrid market operating parallel electronic and voluntarily multiple dealer markets. In
particular, orders are split among the multiple market makers. Second, althought the
multiple dealer market is non-anonymous with respect to traders’ identity, it is very
opaque to the public in terms of price and quantity prior to trades. Informed traders may
prefer to trade with dealers where price and quantity are not revealed to the public before
execution. Third, institutions may prefer to trade with dealers who are able to execute
28
large trades that are not obtainable on SETS. More importantly, as addressed in Franke
and Hess (2000), the high trading frequency tends to smooth the flow of information,
reducing adverse selection problems. In the next section, we consider additional
explanations.
C. Price Impact Measures
Table 5 reports and compares the permanent, temporary, and total price impact of
trades between the two parallel trading markets. Consistent with Seppi (1990), Burdett
and O’Hara (1987), and Grossman (1992), the results show that there is a significantly
greater permanent price impact and a significantly lower temporary price impact of trades
on the anonymous SETS market than on the non-anonymous Dealer market. The larger
difference of the permanent price impact than difference of the temporary impact results
in a significantly greater total price impact for the SETS than for the Dealer market.
While the permanent price impact on SETS is positive, the permanent price impact on the
Dealer market is negative. As expected in a negotiated trading market, the temporary
price impact on the Dealer market is higher than on SETS, implying that trading on the
Dealer market requires higher liquidity provision cost. These observations support
hypotheses 3, 4, and 5.
We continue our investigation of the price impact of trades on SETS and the
Dealer markets based on GBP trade value. In Table 6, the permanent price impact is
lower on the Dealer market for all trade value groups. The permanent price impact is
negative for all groups below the 50th percentile. We also find that the permanent price
impact increases with trade value and the temporary price impact decreases with trade
29
value, consistent with Keim and Madhavan (1996). Overall, the results are consistent
with hypotheses 3, 4 and 5. The permanent price impact is higher on SETS. The
temporary price impact is higher on the Dealer market. Also, the total price impact is
lower on the Dealer market. These findings show that dealers provide liquidity to the
market.
D. Multivariate analysis on probability of informed trading, spread, depth, and price
impact of trade
In this section, we investigate whether the probability of informed trading is
significantly related to firm characteristics. EKOP show that the probability of informed
trading is negatively related to firm size, trading volume, and the number of trades.
Easley and O’Hara (1987) propose that informed traders prefer to trade in large quantity.
The arrival of new information is also reflected in return volatility. Moreover, we
examine whether the probability of informed trading is correlated with spread and depth,
proxies for market liquidity. Lee, Mucklow and Ready (1993) show that market makers
response to informed trading by either widening bid-ask spreads or decreasing depths, or
both. Thus, we expect that the probability of informed trading is positively related to
spreads and negatively related to depths. In addition, we analyze whether the permanent,
temporary, and total price impact can be explained by the probability of informed trading.
Therefore, we estimate two cross-sectional regressions.
PI = α 0 + α1DSETS + α 2 LnCap + α 3 LnInprice + α 4 LnVola + α 5 LnFreq + α 6 LnSize + ε
(7)
30
where DSETS is a dummy variable equal to 1 if the observation is aggregated from the
SETS market. LnCap is the natural log of the market capitalization (millions). LnInprice
is the natural log of the inverse price. LnVola is the natural log of the hourly return
volatility (%) from the SETS or Dealer markets. LnFreq is the natural log of the daily
number of trade (000s) from the SETS or Dealer markets. LnSize is the natural log of
share trade size from the SETS or Dealer markets.
Y = α 0 + α1DSETS + α 2 LnCap + α 3 LnInprice + α 4 LnVola + α 5 LnFreq + α 6 LnSize
+ α 7 PI + α 8 DSETS * PI + ε
(8)
where Y is the Permanent, Temporary, and Total price impact of trade, in turn. .
Table 6 provides the results of the cross-sectional determinants of the probability
of informed trading (PI). Our sample comprises 298 observations (149 firms X 2
markets). As predicted, the coefficients of firm size (LnCap) and the number of trades
(LnFreq) are negatively related to PI, but their coefficients are not statistically
significant. Consistent with EKOP and Franke and Hess (2000), high trading frequency
on SETS is likely to smooth the flow of informed trading so that the adverse selection
problem becomes less severe. In other words, dealers do not add much value in detecting
informed trading in highly liquid stocks. The inverse price is positively and significantly
related to PI, whereas trade size is negatively and significantly related to PI. The
coefficient of return volatility is positive as expected, but it is insignificantly related to
PI. Surprisingly, the coefficient of trade size is negative and significant. This implies that
large trades are not perceived as information motivated trades. The regression shows that
only price and trade size is significantly related to PI. The lack of significance of the
31
coefficient of DSETS shows that the relation between PI and firm characteristics is not
statistically different for the two markets.
Table 6, also presents the results of regressions with the Permanent, and
Temporary, in turn, as dependent variables. The regression with the permanent price
impact as the dependent variable shows that the high probability of informed trading
results in high adverse selection cost on SETS, but the negative coefficient of PI shows
that dealers are able to screen out informed trading.
V. Conclusion
We empirically examine whether there is more informed trading in an anonymous
market than in a non-anonymous market. Different from previous studies on market
performance comparisons, we conduct our analysis by using a unique institutional
characteristic of the London Stock Exchange in which an anonymous electronic order
book market operates alongside a non-anonymous voluntarily dealer market. Our sample
comprises 149 stocks that trade on the SETS and Dealer markets. We analyze both trade
flow and price metrics to measure asymmetric information. We use Easley, Kiefer, and
O’Hara’s (1996) model that enables us to simultaneously estimate the probability of
informed trading from trade flows in the two parallel markets. In addition, we use prices
to calculate the price impact of trades as proposed by Keim and Madhavan (1996), and
Booth, Lin, Martikainen, and Tse (2002). Finally, we investigate the cross-sectional
determinations of the probability of informed trading, spread, depth, and price impact of
trades.
32
We provide several findings. We find that the probability of informed trading on
the anonymous SETS market is not greater than on the non-anonymous Dealer market.
Instead, the probability of informed trading is approximately the same. On the other hand,
our evidence regarding the price impact of trades shows strong support for the ability of
dealers to screen informed trades as suggested by the theoretical models of EKO, Seppi
(1990), Pagano and Roell (1992), Benveniste, Marcus, and Wilhelm (1992), and the
empirical evidence of Gramming, Schiereck, and Theissen (2001) and Heidle and Huang
(2002). The permanent price of trades is not only significantly lower on the Dealer
market than on SETS, but also the permanent impact is negative on the Dealer market.
We conjecture that the PIN model under-estimates the effectiveness of dealers in
screening informed trades due to its assumption that informed trades can be inferred just
from daily signed imbalances in number of trades. In reality, informed traders may split
their orders across multiple dealers, or dealers who play the role of upstairs markets for
bloc trades might work on orders. Furthermore, repeated interactions and heavy interdealer trading can inflate PIN. However, further research is needed to probe into the exact
reasons for not finding a lower PIN on dealer markets.
We also find that the Dealer market can compete effectively with SETS for
uninformed order flow. The level of β , which is the probability that uninformed trades
occur on SETS, is about 0.5, indicating that SETS and the Dealer market execute
uninformed trades with about equal probability. Institutions may prefer to trade with
dealers who are able to execute large trades that are not obtainable on SETS. We find that
most of the large trades are routed to the Dealer market.
33
Consistent with Seppi (1990), Burdett and O’Hara (1987), and Grossman (1992),
the temporary price impact of trades on the Dealer market is significantly larger than on
SETS, implying that the Dealer market supplies liquidity, but at a considerable cost.
Moreover, the greater difference of the permanent price impact than difference of the
temporary impact results in a significantly greater total price impact on the SETS than on
the Dealer market.
Our multivariate analysis shows that firm size and daily number of trades are
inversely related to the probability of informed trading, but not statistically significant.
The results are consistent with EKOP and Franke and Hess (2000) in which high trading
frequency on SETS is likely to smooth flow of informed trading, so that the asymmetric
information problem becomes less severe. Further, we find the inverse price is positively
and significantly related to the probability of informed trading. However, trade size is
negatively and significantly related to the probability of informed trading. This implies
that large trades are not perceived as information motivated trades. Moreover, our
analysis shows that the relation between the probability of informed trading and firm
characteristics is not statistically different for the two markets.
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38
Table 1
Comparison of SETS and Dealer market structure in 200010
This table compares the characteristics of the SETS and Dealer markets on the London Stock Exchange
in 2000.
SETS
Dealer
Trading mechanism
Order-driven electronic limit order
book market
Quote-driven multiple dealer
telephone market
Liquidity provided by
Public limit orders and voluntarily
dealers
Dealers
Access
Members only
Members only
Trader Anonymity
Pre-trade, but not post-trade
Non-anonymous
Pre-trade transparency
All outstanding limit order book
prices and 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 available
to public. Quotes are provided based
on bilateral inquiry.
Post-trade transparency
Immediate reporting of trades.
Identity of the counterparties are
fully revealed when transaction is
confirmed.
Reporting of trades is delayed by up
to 3 minutes and is 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
10
See Board and Wells (2000)
39
Table 2
Summary statistics on firm characteristics of the SETS and Dealer markets
This table contains summary statistics on firm characteristics for a set of SETS stocks trading on both the
SETS and Dealer markets on the London Stock Exchange during 2000. Our sample comprises 149 stocks.
Return volatility is the standard deviation of hourly return. Number of market makers is aggregated from
identified party code on each side of the market. Trade size is the average number of shares and the average
value in GBP per trade. SETS’S Relative quoted-spread (%) is computed as (ask-bid)/(ask+bid)/2*100
immediately prior to the trade. Depth at best limit order quotes immediately prior to the trade is aggregated
from the order history file. Depth is always from SETS; level at the time of SETS trades is reported under
SETS column and level at the time of dealer transactions is reported under dealer column. The last column
presents the test of the mean difference between the SETS and Dealer markets. * denotes significance at the
1% level.
All
Daily number of trades
SETS
Dealer
t-test of Mean
Difference between
SETS and Dealer
294
163
131
32 *
Daily trading volume (shares)
3,201
1,496
1,706
-210 *
Daily trading volume (GBP)
19,520
9,266
10,256
-990 *
0.02
1.20
1.58
-0.38 *
Trade size
10,696
8,536
14,539
-6,003 *
GBP trade size
SETS’ Relative quotedspread (%)
50,560
40,347
68,901
-28,554 *
0.987
0.933
1.046
-0.114 *
0.705
0.201
0.617
-0.416 *
48,693
55,669
40,298
15,371 *
239,295
273,024
189,550
83,475 *
Return volatility (%)
Effective –spread (%)
SETS’ Depth (000s)
GBP depth (000s)
40
Table 3
Summary statistics of estimated information content parameters of stocks traded on two parallel markets
We examine a set of SETS stocks trading on both the SETS and Dealer markets on the London Stock
Exchange during 2000. This table reports summary statistics for the parameters needed to calculate the
probability of informed trading. We maximize the log likelihood function developed by Easley, Kiefer and
O’Hara (1996). Our sample comprises 149 stocks. The probability of informed trading on the SETS
market (PIS) and the Dealer market (PID) are calculated, respectively, as
αµ
PIS =
β 

γ 
αµ + (ε b + ε s )
αµ
PID =
1− β
 1−γ
αµ + (ε b + ε s )



The variables are:α, the probability of an information event; δ, the probability that the information is bad
news; µ, the arrival rate per minute of informed trade if new information exists; εb, the arrival rate per
minute of uninformed buys; εs, the arrival rate per minute of uninformed sells; γ, the probability that an
informed trade is executed on SETS; and β, the probability that an uninformed trade is executed on SETS
market.
α
δ
µ
εs
εb
γ
β
PIS
PID
Mean
0.3212
0.4544
0.2631
0.2575
0.2431
0.5291
0.5360
0.1565
0.1524
Std Dev
0.1159
0.2657
0.2077
0.2403
0.2489
0.2684
0.1212
0.0843
0.0922
Min
0.0291
0.0000
0.0352
0.0213
0.0173
0.0236
0.2597
0.0033
0.0000
Q1
0.2494
0.2312
0.1057
0.0690
0.0634
0.2954
0.4474
0.0880
0.0828
Median
0.3270
0.4283
0.1961
0.1903
0.1767
0.4695
0.5358
0.1523
0.1526
Q3
0.3899
0.6358
0.3464
0.3688
0.3189
0.7854
0.6205
0.2119
0.2058
Max
0.7828
1.0000
1.2441
1.2948
1.3265
1.0000
0.8178
0.4524
0.5734
41
Table 4
Testing for informational differences for stocks trading on the SETS and the Dealer markets
This table contains maximized log likelihood values for various parameter restrictions related to the information content in order flow.
Easley, Kiefer and O’Hara (1996) developed the log likelihood function. Our sample comprises 149 stocks traded on the SETS and Dealer markets on the
London Stock Exchange during 2000. Restricting γ = β assumes no informational differences. Restricting γ >β assumes more informed trading on SETS,
while γ < β assumes more informed trading on the Dealer market. The reported test statistics are twice the difference of log likelihoods under the specified
restriction and the unrestricted model. Test statistics for γ ≤ β (γ ≥ β) test equal information relative to more information content in Dealer (SETS) market.
Test
Test
Test
Log
Statistic
Log
Log
Statistic
Statistic
Log
for
for
for
Likelihood Likelihood Likelihood Likelihood
Statistic
Unrestricted
γ =β
γ >β
γ <β
Mean
-8,283
-8,601
-8,524
-8,355
Median
-7,786
-7,999
-7,976
-7,956
10th
%ile
-13,029
-13,807
-13,794
-13,029
90th
%ile
-4,163
-4,336
-4,163
-4,336
Number of Stocks showing statistical significance @10%
Number of Stocks that show statistically insignificant test result
SETS > Dealer
Overall
-1,234,172
-1,281,537
-1,270,030
-1,244,884
γ =β
p-value
γ ≤β
p-value
γ ≥β
p-value
636
296
0.0416
0.0000
481
6
0.5741
1.0000
144
0
0.4675
0.0047
8
0.0000
0
0.0000
0
0.0000
1,537
0.0066
137
12
65
1,473
1.0000
550
1.0000
94,730
42
71,716
21,424
Table 5. Summary statistics on firm characteristics of the SETS and Dealer markets
This table contains summary statistics on firm characteristics for a set of SETS stocks trading on
both the SETS and Dealer markets on the London Stock Exchange during 2000. Our sample
comprises 149 stocks. The permanent price impact of trade at time t is computed as BS*ln
(PA/PB)*100 where BS equals plus (minus) one for buyer (seller) initiated trade and if a trade
occurs at time t, designate the price of that trade as PT, the price of the third subsequent trade as PA
and the price of the twelfth previous trade as PB. The temporary price impact of trade at time t is
computed as BS*ln (PT/PA)*100, and total price impact of trade at time t is calculated as BS*ln
(PT/PB)*100. The last column presents the test of the mean difference between the SETS and
Dealer markets. * denotes significance at the 1% level.
GBP Trade value
(percentile)
Permanent price impact (%)
All orders sizes
<25%
25-50%
50-75%
75-90%
90-95%
>95%
Temporary price impact (%)
All orders sizes
<25%
25-50%
50-75%
75-90%
90-95%
>95%
Total price impact (%)
All orders sizes
<25%
25-50%
50-75%
75-90%
90-95%
>95%
All
SETS
Dealer
t-test of Mean
Difference between
SETS and Dealer
0.100
0.003
0.055
0.141
0.227
0.204
0.118
0.219
0.122
0.157
0.224
0.296
0.311
0.340
-0.019
-0.055
-0.032
-0.002
0.032
0.025
0.052
0.239 *
0.177 *
0.189 *
0.227 *
0.264 *
0.285 *
0.288 *
0.169
0.277
0.202
0.134
0.067
0.054
0.055
0.073
0.123
0.096
0.067
0.043
0.037
0.036
0.260
0.351
0.288
0.234
0.125
0.080
0.062
-0.187 *
-0.228 *
-0.192 *
-0.167 *
-0.081 *
-0.043 *
-0.026 *
0.262
0.260
0.251
0.274
0.293
0.258
0.174
0.294
0.244
0.254
0.294
0.340
0.349
0.365
0.226
0.267
0.244
0.226
0.155
0.105
0.115
0.068 *
-0.023 *
0.010
0.068 *
0.185 *
0.244 *
0.249 *
43
Table 6
Cross-sectional regression analysis on the probability of informed trading and price impact of
trade, in turn
This table presents the results of cross-sectional regressions of probability of informed trading and the
price impact of trades for both the SETS and the Dealer markets. The sample contains 298 observations
(149 firms X 2 markets). PI is a stock’s probability of informed trading on the SETS or Dealer markets.
The regression equations are:
PI = α 0 + α1DSETS + α 2 LnCap + α 3 LnInprice + α 4 LnVola + α 5 LnFreq + α 6 LnSize + ε
where DSETS is a dummy variable that equals 1 if the observation is aggregated from the SETS market.
LnCap is the natural log of the market capitalization (millions). LnInprice is the natural log of the inverse
price. LnVola is the natural log of the hourly return volatility (%) from the SETS or Dealer markets.
LnFreq is the natural log of the daily number of trade (000s) from the SETS or Dealer markets. LnSize is
the natural log of share trade size from the SETS or Dealer markets.
Y = α 0 + α1DSETS + α 2 LnCap + α 3 LnInprice + α 4 LnVola + α 5 LnFreq + α 6 LnSize
+ α 7 PI + α 8 DSETS * PI + ε
where Y is Permanent and Temporary, in turn. The permanent price impact of a trade at time t is
computed as BS*ln (PA/PB)*100 where BS equals plus (minus) one for buyer (seller) initiated trade and
if a trade occurs at time t, designate the price of that trade as PT, the price of the third subsequent trade as
PA and the price of the twelfth previous trade as PB. The temporary price impact of a trade at time t is
computed as BS*ln (PT/PA)*100, and the total price impact of a trade at time t is calculated as BS*ln
(PT/PB)*100.
Panel A: Regression with the probability of informed trading, the permanent, and
temporary price impact in turn, as dependent variables
Dependent variables
Independent
variables
Intercept
DSETS
LnCap
LnInprice
LnVola
LnFreq
LnSize
PI
DSETS*PI
Adj. R2
F-value
Probability of Informed
Trading
Coeff.
p-value
0.7508
-0.0134
-0.0091
0.0281
0.0068
-0.0101
-0.0468
<.0001
0.2955
0.3707
0.0348
0.7389
0.2156
0.0004
0.1711
11.22
<.0001
Permanent
Price Impact
Coeff.
p-value
Temporary
Price Impact
Coeff.
p-value
-0.2190
0.2180
0.0018
-0.0240
0.1128
-0.0308
0.0286
-0.1746
0.4323
0.1014
<.0001
0.8635
0.0785
<.0001
0.0004
0.0374
0.0269
0.0004
1.4216
-0.1573
-0.0042
0.1053
0.1128
-0.0240
-0.0962
0.0216
-0.2545
<.0001
<.0001
0.6481
<.0001
<.0001
0.0017
<.0001
0.7552
0.0175
0.7174
95.23
<.0001
0.7844
136.05
<.0001
44
Customer submits order to member firms with/without trading venues
Member firm handle order in one of three ways according to customer’s instructions
Dealer market
executes entire order against
his own inventory (principal
cross) or matches order with
other customer’s order
(agency cross)
Mix
Partially executed in
Dealer market and work
the rest in limit order book.
Member must report all trades from Dealer
market within 3 minutes, except “Work
Principal Agreement” orders.
directly submits order to
SETS limit order book
market
executes immediately as
market orders or enters as a
new limit order
All orders executed in SETS are
automatically reported.
Fig. 1. Order flow of SETS stocks on the London Stock Exchange.
45
γ
Informed traders
arrive at rate µ
Bad news
δ
1−γ
SETS buy
1− β
Dealer buy
β
SETS sell
1− β
Information Event
Occurs α
γ
Good news 1 − δ
Informed traders
arrive at rate µ
1−γ
Uninformed buyers
β
ε 1− β
Information Event
Does Not Occur 1 − α
Uninformed seller
arrive at rate ε s
Once per
Day
Uninformed buyers
arrive at rate ε b
Uninformed seller
arrive at rate ε s
Dealer sell
β
Uninformed buyers
arrive at rate ε b
Uninformed seller
arrive at rate ε s
SETS sell
β
1− β
Dealer sell
SETS buy
Dealer buy
SETS buy
Dealer buy
SETS sell
Dealer sell
β
SETS buy
1− β
Dealer buy
β
SETS sell
1− β
Dealer sell
Fig. 2. Tree diagram of the trading process. The variables are: α, the probability of an information event; δ, the
probability that the information is bad news; µ, the arrival rate per minute of informed trade if new information
exists; εb, the arrival rate per minute of uninformed buys; εs, the arrival rate per minute of uninformed sells; γ, the
probability that an informed trade is executed on SETS; and β, the probability that an uninformed trade is executed
on SETS. Nodes to the left of the dotted line occur once per day.
46
0.0003
Percentage Cumulative Returns
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 time 0
SETS buy
Dealer buy
SETS sell
Dealer sell
Fig. 3. 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.
47