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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 1 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. 2 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) 4 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. 5 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 6 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, 7 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 8 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. 9 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. 10 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 11 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. 12 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 13 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. 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Market architecture: limit order books versus dealership markets. Journal of Financial Markets, 5, 127-167. 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