SLICE ORDER IN TASE STRATEGY TO HIDE?

Transcription

1 SLICE ORDER IN TASE STRATEGY TO HIDE? Isabel Tkatch and Zinat Shaila Alam* Department of Finance, J. Mack Robinson College of Business Georgia State University Atlanta, GA August 21, 2007 ABSTRACT We identify a group of limit orders in an electronic limit order book and define them as slice orders. Slice orders are of equal size and price submitted consecutively within an interval of two minutes. These orders are priced aggressively, have shorter life span and hit the transaction most of the time. We find an increasing intra-day pattern in slice order submission, more prevalent at the end of continuous trading session. We argue that these orders arrive from the same trader and are used as a strategic tool to achieve best execution or to reduce price impact of their large orders. Order imbalance in the short run and a higher percentage of impatient traders from the opposite site in the long run increase the probability of observing a slice order. Our result is also consistent with the empirical findings that traders perceived price impact of a large buy order is greater than that of a large sell order. JEL Classification: G15; G23; N20 Keywords: Market microstructure, trading strategies * Corresponding author: Isabel Tkatch, Department of Finance, Georgia State University. Phone: , itkatch@gsu.edu. We are grateful to Harley E. Ryan, Jr., Reza Mahani and Vikas Agarwal for useful suggestions and comments. We thank Tel-Aviv Stock Exchange for intra-day data.

2 SLICE ORDER IN TASE STRATEGY TO HIDE? I. INTRODUCTION According to the traditional view of order choice under information asymmetry, informed (impatient) traders use market orders to take advantage of better information before it is revealed to others. On the other hand, uninformed (patient) traders use limit orders. From liquidity standpoint, informed (impatient) traders are the consumer of liquidity while uninformed (patient) traders are the supplier of liquidity. Rapid fragmentation in the US market and around the world, advances in technology and regulation-driven changes in market structure have transformed trading process. Now the order choice is much more complex beyond the oversimplified classification of limit and market order due to changing market structure, specially in a pure limit order context. When an institutional trader trading for a liquidity reason expose his bulk order in an open limit order book, a quote matcher can potentially extract some profit by front running. Or he might be suspected to have some private information and receive a worse price. As a result, traders do not have an incentive to expose their order position. Therefore, a much larger proportion of liquidity is hidden in the desk of broker-dealer, floor brokers, and orders at upstairs markets, in the reserve order pool of ECNs, in the dark books of the crossing networks and ATSs (alternative trading systems). Large brokerage houses, are aggressively devising algorithms to trap hidden liquidity in the electronic limit order markets for their clients and claim to offer best execution. Many electronic trading platforms have introduced iceberg orders that allow market participants to submit the orders only with a certain portion of the orders publicly disclosed. Given the changing market structure, we would like to see what trading scheme would like to be adopted by a large trader in an electronic anonymous limit order market which does not allow an iceberg order. Empirical evidence suggests that large institutional split their orders to ensure better execution and reduce price impact. Now a days, algorithmic trading is well skilled to the task of breaking larger trades into smaller pieces. We identify a group of limit orders in an electronic limit order book. These limit orders are of equal size and price submitted consecutively within an interval of two minutes. We name them slice orders. These slice orders may be a portion of large orders broken to smaller pieces. We argue that these orders arrive from the same trader and are used as a strategic tool to achieve best execution or to reduce price impact of their large orders. The anonymity of a trader in a pure electronic limit order market of TASE helps him to hide his large order volume by submitting slice orders. 2

3 In the spirit of Hasbrouck and Saar (2004) paper, we then extend the classification of market and limit orders in an order driven market. We classify two groups of limit orders - regular and slice orders. Slice order, as defined before, is a group of limit orders of equal size and price submitted consecutively within an interval of two minutes. Each group is recognized as a single slice order. The rest of the orders in the order book are considered as regular limit order. We argue that slice orders are used as a hiding (trading) strategy for a large trader and have different characteristics and implications as opposed to regular limit orders. If order slicing is a hiding mechanism as we argued, then the next series of questions are what characterizes a slice order? Does it exhibit an intra-day pattern? What market conditions encourage traders to hide? Who wants to hide? and so on. We investigate first three questions in this paper. First we provide a partial characterization of slice order. The dramatic proliferation of ECNs in the United States and the transition of many other exchanges such as AMEX, Chicago, Toronto, Euronext, and Paris Bourse to an electronic format and a billion dollar algorithimic trading industry tell us that this characterization is not trivial. It will help us to understand how liquidity emerges endogenously through the strategic interaction of informed and large uninformed traders through their placement of slice and regular limit order in an open limit order book. Second, we explore the market conditions that encourage traders to submit slice orders. We examine the limit order book and transaction data of TelAviv Stock Exchange (TASE) for a period of two months during the year Choosing TASE for our investigation is important for several reasons. First of all, due to intense competition and huge investment on proprietary software, large brokerage houses are unwilling to disclose order flow information and trading strategies applied by these algorithmic softwares. ECNs only display aggregate order flow information in the Book Viewer. Therefore, order slicing strategy on the electronic limit order markets in the US is unobservable. On the other hand, TASE allows us to see uncompiled individual order flows as they arrive. As a result, every order coming to the exchange can unambiguously be identified as individual order and viewed in a real time. This allows us to see the order slicing pattern in the limit order book. Secondly, we know that both informed and large traders disguise their true order size by (a) partially exposing their order (iceberg order) (b) frequently canceling and resubmitting their order and (c) slicing their order into pieces (Harris, 1996). When traders use order slicing and iceberg orders simultaneously to hide e.g., as in ECNs, it is difficult to separate the impact of order slicing from that of iceberg orders. At TASE limit order market, every trader must expose his/her order size, this unique setting gives us an opportunity to assess the impact of order slicing independent of iceberg orders. 3

4 Lastly, in a limit order market with market makers, order flows are continuously scrutinized by them and they make money by smoothing the order imbalance. Therefore, any order submission strategy to hide the order size is likely to be revealed and adjusted into price at a faster speed in those markets. No market maker environment of TASE provides a controlled environment to observe the price impact of order slicing and information adjustment process which is difficult to observe in some other markets with market makers like Paris Bourse or Euronext. Tel Aviv Stock Exchange (TASE) data set gives us a unique opportunity to identify a group of orders defined as slice order which may well be used to hide the order volume of large traders. Having a successive flow of three or more orders of same size and price within an interval of two minute is unlikely to arrive from different traders. We argue that these orders arrive from the same trader and are used as a strategic tool to achieve best execution or to reduce price impact of their large orders. We find support that slice orders indeed have some special characteristics. Slice orders are observed in most liquid stocks of TA100. We identify that the slice orders are aggressively priced and hit the transaction most of the time. The slice orders have shorter life span. Specially, the duration of unfulfilled slice orders is 28 minutes 34 seconds, much lower than the duration of regular unexecuted limit orders. Slice orders also improve the fill rate of partially executed orders. We find an increasing intra-day pattern in slice order submission, more prevalent at the end of continuous trading session. This type of pattern may emerge when institutional traders strategically hide at the end of the day by using slice orders. Since the professional money managers performance is evaluated at the closing price, they may also like to delay their trade till the end of the day. Order imbalance in the short run and a higher percentage of impatient traders from the opposite site in the long run increase the probability of observing a slice order. The depth at the same and opposite side increases the probability of a slice order incident. On the other hand, trade volume has an inverse effect. The probability of observing a buy slice order is on an average 33% higher than that of a sell order. The probability of observing a buy slice order is significantly influenced by more market condition variables than that of a sell slice order. The average execution price of a buy slice order compared to a hypothetical market order is also much lower. Our result is also consistent with the empirical findings that traders perceived price impact of a large buy order is greater than that of a large sell order. This paper continues with a literature overview in Section II. A brief description of TASE market is included in section III. Data and sample construction to identify slice orders are provided in section IV. The characterization of slice orders and descriptive statistics are provided in Section V. Section VI develops 4

5 predictions about the market conditions that encourage traders to place slice orders and presents the results. Section VII concludes the paper. II. LITERATURE REVIEW The earlier literature argues that informed traders would prefer market orders because benefit of immediate execution outweighs the cost of a worse price. By contrast, liquidity traders are more patient and wait for an opportunity to trade at a better price (Rock, 1990, Glosten, 1994, Seppi, 1997). However, informed traders do prefer limit order when prices deviation from fundamentals is wide (Angel, 1994, Harris, 1998), when transitory volatility is higher and spread is wider (Handa and Schwartz, 1996) and when private information is long-lived (Kaniel and Liu, 2004). Thus, limit order choice by informed traders may be influenced by other market parameters. However, those studies are set up (except Kaniel and Liu, 2004) in a traditional market structure. There is a new strand of literature on electronic limit order books that does not take traditional roles of order types and trader groups as given but examines how their respective roles change in pure electronic markets (Bloomfield, O'Hara and Saar, 2005; Hasbrouck and Saar,2004; Kaniel and Liu, 2004). Those studies show that role of limit order traders in the market place can no longer be characterized as uninformed and patient suppliers of liquidity. While Hasbrouck and Saar (2004) finds that limit order can be aggressive, Bloomfield, O'Hara and Saar (2005) investigates the role of informed traders in providing liquidity. Some of the recent models on a limit order market also support the notion that limit orders are not homogeneous. An experimental study by Bloomfield, O'Hara and Saar (2005) in a pure order driven market provides strong evidence that informed traders actively use limit orders and their limit order choice depends on several market conditions. This result is certainly in contrast with traditional view. With the advantage of the experimental setting (controls the degree of information each trader posses), their study includes both informed and uninformed traders and finds that use of limit order by informed traders is also time-varying. It implies that limit orders may carry information. Hasbrouck and Saar (2004) identify a particular type of limit order called fleeting orders that seek for immediacy of execution in different trading venues. In that sense, fleeting limit orders are closer substitutes to market orders. So the question that arises is what purpose a limit order serves beyond the traditional role as a provider of liquidity? We argued earlier that slice orders are used as a as a strategic tool by the traders in a pure order driven market. A large strand of theoretical literature investigates strategic behavior of the traders. A lot of models suggest that traders incorporate strategy by changing trade size and splitting their orders. However, none of 5

6 theoretical models suits our purpose. We cannot generate predictions from those models. Therefore, we set forward to characterize slice order to improve our understanding of this type of order. III. MARKET STRUCTURE AND STATISTICS ON TEL-AVIV STOCK EXCHANGE Tel Aviv Stock Exchange (TASE) is an electronic order driven market that supports anonymous trading. This market operates without any designated market maker or floor traders. A limit order allows a trader to trade a certain amount at a given price. Traders post limit (buy and sell) orders to an electronic trading system. Limit orders are executed according to time and price priority. At a given time, a buy (sell) orders with highest (lowest) quote receives first priority in execution. If two or more limit orders are at the same price, time priority is in effect. A priority can only change if a trade occurs. Every trader has equal access to trading opportunities in the market and can view three levels of best bid and ask prices and quantities of the book and last few transactions. The status of the book - order arrival, cancellation or execution- is updated instantaneously on the screen. TASE does not allow any hidden order. Traders can also place market orders. However, market orders comprise a very insignificant portion of total daily order flow of the market. Trading on TASE is composed of three phases. First phase is call auction which starts at 9:30 am in order to facilitate the price discovery process. Continuous trading phase covers from 9:45 am to 16:45 pm. Then there is a closing phase between 16:45 pm to 17:00 pm. Exchange opens at 8:30 am with an empty order book. It allows traders to submit their market or limit orders till 9:45 am. Three best bid and ask prices on each side are displayed from 9:00 am. At 9:45 am all the orders in the book are crossed using call auction. Then the market moves on to continuous phase, which is a pure limit order market. The orders line up in sequence as they arrive in the market. Transaction occurs when a trader from opposite side hits the quote. In the closing phase, market orders are executed, if possible at the closing price. All unexecuted orders are cancelled at the end of the trading day. In addition to that, some orders are cancelled actively by the traders. The next morning again begins with an empty order book. Investors can place limit order at any price within a prespecified price range defined by the tick size. For prices below 5, 50, 500 Shekel (the currency), tick size is 0.001, 0.01 and 0.1 Shekel respectively. For prices above 500 Shakel, tick size is 1 shakel. There is no round lot restriction; however, there is minimum order volume requirement. TA100 is the major index in the market. TA25 is the index for largest 25 companies. Some of the stocks are cross listed in NASDAQ. Last one hour of TASE trading session overlaps with NASDAQ trading everyday except for Sunday and Friday. 6

7 Table 1 reports cross-sectional average of market capitalization, turnover, order flow and trading statistics of largest 100 and 40 stocks respectively for October and November, The stocks are ranked by their average trading volume in the sample period. We first compute the daily estimates for each stock and then report summary statistics across firms. The average daily volume of limit orders is NIS million thousands and NIS million for TA100 and top 40 stocks respectively. Average daily order and trading volume is higher for top 40 stocks as expected. Order volume is on an average 3.5 times higher than trading volume shown both in Panel A and B. The ratio of order volume to total number of orders is much higher compared to the ratio of trading volume to the number of transactions revealing that TASE is not a very deep market. A. Data Set IV. DATA AND SAMPLE CONSTRUCTION The data set contains the order book and transaction record and three best bid and ask records from the limit order book for TA100 (Tel Aviv) stocks. The sample covers all 43 trading days from October to November For each order, data set provides information about order submission time, price, quantity ordered, order quantity met, status of the order placed, cancellation time if the order is cancelled. For a cancelled and resubmitted orders, it also gives information about resubmission time, new order number, price, quantity, order quantity met from resubmission. Transaction file provides transaction time, price and quantity for each stock on each side. Limit order book provides real time information on first three best bid and ask prices and quantities. This is a unique data set as we directly observe each order placement with price, quantity, side, time and status. Status details the state of execution - if an order quantity is fully executed, partially met, waiting to be filled, cancelled and/or resubmitted. Traders do not observe the information about the order flow. Only exchange can monitor or supervise the order flow to ensure that everything is running smoothly. B. Construction of Slice Order Sample We are interested in identifying order submission strategy, particularly, order slicing in the context of a pure limit order market. Hence, our sample considers order flow information only from continuous phase. We analyze continuous order flow to identify the orders that arrive on the same side one after another with equal size and price within an interval of two minutes. We develop a filter rule to separate those successive orders from the rest of the limit orders. We consider each group of successive order as a single observation or 7

8 incident. We keep slice order with 3 or more pieces for our analysis. The median number of pieces in an average slice observation is 4. The highest number of successive pieces observed in the sample is 78 with partial execution. Median slice order size ranges from 490 to 2000 units in shares cross-sectionally. Since the traders are anonymous, we cannot claim for sure that the orders are coming from the same trader. However, observing the types of incidents are likely to be non-coincidental. Characterization of slice order in the subsequent section also provides some interesting findings suggesting that slice order might be of a special type of orders. 1. Issues with Order Cancellation The observations applying filter rule have 20.00% orders that are cancelled at some point in time without execution. We want to see if the cancellation is voluntary by the trader or due to some system fault. We assume that if the cancellation appears to be between 2 to 10 second of submission, then it is due to system fault. The rationale is that traders are not as quick as the system to cancel their order and they do not have any apparent incentive to do that. Also if the cancellation is due to system fault, then orders are more likely to be resubmitted. About 0.21% of cancelled slice orders are cancelled within 2 seconds and only 0.01% of them are resubmitted. About 0.67% of orders are cancelled and resubmitted within 10 seconds. We also look at the orders that are cancelled and resubmitted within 30 seconds and 1 minute respectively and find cancelled and resubmitted orders increase in proportion of total observations 1.96% and 3.20% respectively. The statistics suggests that most of the cancelled orders are cancelled by the trader and can play an important role in their order submission strategy. Therefore, we include cancelled slice orders in our sample. 2. Selection of Sample Stocks Initially we run some descriptive statistics on 96 stocks that are listed in TA100 index during the period from October to November 2006 to see if slice orders are more prevalent in some categories of stock. We find wide presence of those types of orders cross-sectionally. Then we rank sample stocks based on aggregate trading volume and order volume for the sample period and calculate total slice order incidents for each stock. Panel A and B of Table 2 present the statistics. Each group of slice order is viewed as a single incident. The table shows that the raking of the stock and the occurrence of slice order incidents are correlated. Most of the incidents are observed for stocks with high trading and order volume suggesting that slice order is a phenomenon mostly observed in liquid stocks. Some of the stocks that are ranked between 45 and 65 show relatively high incidents of order slicing. Panel 3 reports distribution of slice order incidents for each stock in this category. For most stocks the number of slicing at third quartile is 4. The maximum figure is an extreme incident for those stocks. It suggests that there is no systematic order slicing for stocks in this 8

9 category. Therefore, we decide to confine our sample to top 40 stocks with the advantage that the sample provides reasonable variability in the trading volume. V. PRELIMINARY CHARACTERIZATION OF SLICE ORDER A. Comparative Statistics on Regular vs Slice Order As defined earlier, slice order is a group of limit orders of equal size and price submitted consecutively within an interval of two minutes. Each group is treated as a single slice order observation. Rest of the limit orders are classified as regular orders. Each order, given that it is a slice or a regular order, is again classified as a limit and marketable limit order. Marketable limit order is a buy/sell order whose limit price is equal to or higher/lower than the best ask/bid in the market. Table 3 presents the statistics on the number, size in shares and volume of each category of orders. The description of variables is as follows. No of Lmt is the average daily volume of limit orders, Lsize is the average daily size of limit orders in shares, Lvol is average daily limit order volume in NIS (New Israeli Shekel), No of Mkt is the average daily number of marketable limit orders, Msize is the average daily size of marketable limit orders in shares, Mvol is average daily marketable limit order volume in NIS. All the statistics in this subsection are computed for 40 sample stocks for 43 trading days in October and November There are total 2516 slice order observations in the sample. On an average, the number of slice orders that are submitted daily is much lower than the number of regular orders both in the limit (defined as LMT) and marketable limit category (defined as MKT). However, the volume of slice order is phenomenally higher than that of a regular order. The average daily LMT slice order volume is NIS 238,943 compared to LMT regular order volume of NIS 60, 013. The pattern is similar for marketable orders. Though large enough, the order size of a typical MKT slice order is less disproportionate to that of a MKT regular order. On the other hand, the order size of an average LMT slice order is 10,339 shares compared to 1,998 shares for a LMT regular order. B. Aggressiveness of Slice Order Table 4 reports hit statistics and success rate of regular and slice order. Slice orders constitute only 0.35% of total order flow in the period from October to November A higher proportion of slice order (0.56%) is submitted as MKT orders compared to as LMT orders (0.23%). A large percent of slice order are priced inside the quote and reflects urgency for execution. About 63.72% of all orders hit the transaction. Slice orders comprise about 17% (0.27% of 63.72%) of hit orders. Noticeably, a very high percentage of slice 9

10 orders hit the transaction as shown by the success rate. Success rate is measured as a conditional probability. For example, the percentage of hit is 78.94% given the order is a slice order. On the other hand, the success rate of a regular order is 62.65%. The success rate for a sliced order is 26% higher than that of a regular order due to the fact that a higher proportion of slice orders arrive as MKT orders. Total limit order flow that hit the transaction is 41.93%. LMT slice comprises (0.11% of 41.93%) about 4% of that flow. MKT slice orders that hit the transaction comprise 0.60% of the total marketable order flow. They hit the transaction all the time. The success rate for LMT slice is also higher (50.04%) than that of a LMT regular order (41.91%). Slice orders seem to be more aggressive and have higher success rate compared to a regular order. The magnitude of successful hit by slice orders compared to their presence in the order flow is distinguishable from Table 4. Another interesting phenomenon is the cancellation statistics of slice order. About 98% of the unfulfilled slice orders are cancelled within one hour and 91% of them are cancelled within three minutes of order submission (not reported in the table). Unfulfilled and cancelled slice orders also reflect upon the aggressive nature of slice orders. C. Fill Rate of Slice Order The fill rate for an order is the ratio of total order volume fulfilled to total order volume submitted. The classification scheme is as follows. Completely executed order has a fill rate of 90% or above. The execution ratio for a partially executed order is below 90%. Unexecuted orders are placed and eventually cancelled without any execution. The execution ratio is 0% for an unexecuted order. Table 5 reports the fill rate for slice and regular order. The fill rate of completely executed orders is almost same for slice and regular order. However, slice orders seem to be more effective in filling a partially executed order. The average fill rate of a slice order is % compared to 40.18% fill rate of a regular order as shown in Panel A. Panel B also reveals that fill rate of partially executed slice orders, either LMT or MKT orders, is higher than that of a regular order. Specially, the average fill rate of partially executed LMT slice order is strikingly high (77.02%) compared to 41.99% for LMT regular order. Slice orders improves the fill rate of a LMT order. D. Execution Duration of Slice Order The execution duration is the average time between order submission and execution or cancellation. We can directly measure the duration of each order from the data set. The duration for a slice order is measured as the time difference between the first order (of the group) submitted and the last order (of the group) executed 10

11 or cancelled. Full, partial and non-execution are defined as before. Panel A of table 6 reports the cross sectional average duration for each order category. The mean duration of a fully filled slice order is 3 min 29 seconds while the mean duration is 11 min 54 seconds for a regular order. Full execution of a slice order takes one fourth less time compared to an average regular order. The median value for a fully executed regular order is lower than that of a slice order. This is because regular order duration is calculated per order basis while slice duration is calculated from the beginning to the end of a slice consisting of several orders. The median for a non-executed regular order is lower as most of them are cancelled by the system. However, the standard deviation of duration of a slice order is much lower suggesting that there is lower variation in cross sectional duration for this type of order. Panel B describes the duration by order type LMT or MKT. Overall pattern is similar to Panel A. Looking at the execution time between entry and complete execution or cancellation of slice order one can state that traders check the status of their slice orders frequently and cancel them if prices move away from the limit price. This observation is also consistent with the fact that 98% of the unfilled slice orders are the cancelled ones. E. Intra Day and Weekly Pattern in Slice Order We analyze the intra-day pattern in regular and slice order flow in terms of number and volume of orders. We measure the flow in each 15-minute (Figure 1) and 90-minute (Figure 2) trading intervals during the continuous trading phase from 9:45 am to 5:15 pm for the sample period for 40 stocks. Frequency of regular order exhibits a U-shape pattern while frequency of slice order depicts an increasing curve reaching at the peak at the end of the continuous trading phase. Intra day pattern of order flow is also observed in Paris Bourse. Biais, et al (1995) suggests that intra day pattern could reflect strategic investors splitting their order during the day and unwinding the remaining exposure at the end of the day in the hidden order market. The pattern might also arise when institutional investors such as mutual fund, who are likely to be evaluated at the closing price, try to execute a large portion of their bulk order at the end of the day. The pattern may also emerge due to the trading of the cross listed stocks at the end of the day both at TASE and NASDAQ. In addition to analyzing order specific characteristics, we also explore intra-day pattern of various market conditions variables such as order volume, trade volume, volatility and spread. We calculate each of the variables for 90-minute and 15-minute trading intervals during the continuous trading phase from 9:45 am to 5:15 pm. Order volume is the log of the average order volume submitted during each interval. Trade volume 11

12 is the log of the average volume traded during each interval. Spread is the log of inside quoted spread before the order is submitted. Volatility is the average of price range scaled by the range midpoint at each trading interval. Intraday order volume, trade volume and volatility exhibits U-shape pattern as depicted in Graph 4 and Graph 6. NIS spread is very high at the beginning of the trading interval and falls to a consistent level as the day passes. Spread slightly increases at the end of the trading interval but is much lower than the spread at the start of the day. The theoretical models suggest that intra-day pattern arises from information asymmetry and/or trading opportunities at closure of the market. Literature also suggests that institutional traders can influence the intraday variation in volume and prices. Obizhaeva and Wang (2005) develop a model for execution strategy for institutional traders in the limit order market. The model implies large trade at the beginning and at the end of trading period. According to their paper, we might observe a U-shape pattern in order and trade volume if institutional traders trade horizons coincide with a trading day. We also look at the weekly pattern of regular and slice order for a better understanding of the purpose of a slice order. Sunday is the first day and Thursday is the last day of the trading week for TASE. Hence we do not observe any simultaneous trading in TASE and NASDAQ during these two days. Figure 3 exhibits that the frequency of slice order is also lower at the beginning and at the end of the week while regular order flow increases in the midweek. Weekly pattern may be due to cross-listed stock trading at the mid week and/or institutional investors trading horizon coinciding with the midweek. The significance of these patterns is tested in the next section by employing statistical models. VI. EMPIRICAL TESTS AND RESULTS The initial characterization of slice order suggests that cross sectional pattern of slice order differ from that of a regular limit order. In this section we employ more formal tests to see what type of market conditions increase/decrease the probability of observing a slice order. We use a binomial Logit model for two categories of order (regular and slice) with indexing i = 0, 1 corresponding to the categories. The probability of event i for stock j is: P log P i, j 0, j = X jβ i for i= 0,1 where X j is the vector of explanatory variables. The probability of slice order incident is modeled relative to the probability of occurrence of a regular order. 12

13 A. Explanatory Variables and Predictions The following explanatory variables are used in the model. The variables are defined for a buy side order. OppMKT and SameMkt are the ratio of the monetary value of sell and buy market orders to the total monetary value of sell and buy orders respectively. These variables proxy for impatient traders in the market. A higher proportion of OppMKT would imply more impatient sellers who would place marketable limit orders. Since slice orders are relatively aggressive in nature and seek immediate execution, higher proportion of impatient trader on the opposite side would increase the likelihood of observing a slice order. DepthSmVol and DepthOppVol are the log of monetary size of the depth at the best bid and ask respectively. According to Parlour (1998), as depth of the opposite side (sell) increases, there are more competition among sellers implying the likelihood of a marketable sell order. Therefore, we predict that DepthOppVol has a positive effect on the probability of observing a sell slice order. A higher depth on the same side forces liquidity traders to price more aggressively and as they also demand immediacy, the occurrence of a slice order should also increase. Imbalance is the difference in the monetary value of the same side and the opposite side orders scaled by the total monetary size of orders. Imbalance is positive if sell side order volume is larger. From a buyer s perspective, if imbalance is positive then average duration of his order should decline. Hence likelihood of a buy slice order should increase. The prediction is symmetric for a sell slice order. DepthNtrd is the sum of net number of newly placed limit order and the orders that are unexecuted in the last interval. The net number of newly placed limit order is the difference between the number of newly placed limit order and the limit orders that are executed at a given interval. Sdepth and Bdepth are sell and buy side depth of DepthNtrd variable. DepthNtrd is an alternative measure of market depth based on the number of transactions, not on volume. Since the slice order is characterized by a number of successive order submissions and transactions, higher DepthNtrd at a given interval may arise because of a large trader splitting his order evenly or unevenly. If this is indeed the case, then this big order will eat up the liquidity and decrease the probability of observing a slice order in the subsequent period. We use two types of volatility measures. Volatility1 is the average of price range scaled by the range midpoint at each trading interval. Volatility2 is the squared return from a transaction of a stock at a given interval summed over all the transactions at that interval. Foucault (1999) develops a model that shows that when volatility increases, the probability of being picked-off by an informed trader also increases. Hence 13

14 limit traders post limit price away from the reservation price. Therefore, when the volatility is high limit orders are less price aggressive. On the other hand, when volatility is high, a limit order is likely to be hit shortly after submission. Slice orders are both price aggressive and has a quicker execution. We are not sure which of the two effects will dominate. Hence we do not have a prediction for the effect of volatility on the probability of observing a slice order. Retmmt is the return over the preceding interval for a buy order and negative return over the preceding interval for a sell order. This variable measures the price change in the direction of the order. The variable is also used in Hasbrouck and Saar (2004) in analyzing fleeting orders on Islands. When price is moving in the same direction as the trading intention, we should observe more aggressive orders to avoid the risk of missing the market. Therefore, we expect a positive relation between Retmmt and the probability of a slice order event. TradeVol is the log of the volume traded. Empirical evidence shows that volumes are auto-correlated. High volume in the preceding period increases the likelihood of higher volume in the next period. This pattern is due to the fact that when new information arrives in the market, the adjustment process takes time. Specially, if a trader is splitting his order because he possesses some private information, the adjustment process is even slower. Therefore, if the trading volume is high in the previous period, likelihood of observing a slice order should increase in the next period. OrderVol is the log of the order volume submitted. This variable proxy for the arrival rate of traders (Foucault, Kadan and Kandel, 2004). Higher order volume in the previous period should reduce the duration of an order in the limit order book. So we expect a positive relation between OrderVol and slice order incident. Duration is the time between entry and execution (cancellation) for each order. Duration is positively related to the probability of observing a slice order. Other stock specific control variables are Osize, TickSprd, DailyVol, Logmidpoint, Open_price, daytime and weekday. Osize is the log of order volume submitted. TickSprd is the spread scaled by tick size. DailyVol is the log of average daily volume for each stock. Logmidpoint is the average of best bid and ask. Open_price is the average opening price for each stock calculated for the sample period. Four different daytime intervals are chosen for empirical investigation 15 min, 30 min, 45 min and 60 min. Sell, Tradeside, TA25, Dual, Simult, Rquote, Tk1ind are dummy variables. Sell is 1 if the order is a sell order. Tradeside is 1 if the last transaction and current order is from buy side. Tradeside also proxy for order 14

15 imbalance. TA25 is 1 if the stock is included in TA25. Dual is 1 if the stock is traded in NASDAQ. Simult is 1 for a dual stock in a simultaneous daytime interval. Rquote is 1 if a quoted price is a round number. Tk1ind is 1 if TickSprd is greater than 1. In the Logit regression the reference point for all dummy variables are zero. Last trading interval and the last day of the week are the reference points for daytime and weekday statistics. SameMkt, OppMKT, Imbalance, TradeVol, OrderVol, DepthNtrd, Sdepth, Volatility1, Volatility2, Retmmt are measured in the preceding 15 min, 30 min, 45 min and 60 min intervals before the event interval and reported in Panel A, Panel B, Panel C and Panel D of Table 8. (1) and (2) of each panel report the result from two separate Logit models. Daytime interval variables are not reported if they are not significant. Slice order is grouped as one order. Every order is assumed to be independent. We also assume that there is an equal likelihood of observing a buy and a sell order. All variables are calculated for each of the 40 stocks over the sample period of October and November, 2006 (43 trading days). B. Results Table 8 reports the result of first sets of regressions. The significant variables are DepthSmVol, DepthOppVol, Imbalance, TradeVol, DepthNtrd, Duration and they all have the predicted signs. Volatility and Retmmt have no significant influence on the probability of slice order event. We use two different measures of volatility in two separate regressions and get similar results. Osize and dailyvol are two control variables that affect slice order probability significantly, the former inversely and the later positively. The probability of observing a slice order increases if it is a buy order or initiated by a buyer when the last trade is a buy, is included in TA25, is a simultaneously traded stock, or is not a dual stock. The occurrence of slice order increases for non dual stocks that are partly executed. The probability of a slice order also increases given it is a LMT order and fully executed. Slice order submission is significantly lower at the beginning and higher at the end of the trading day and supports intraday pattern observed in Graph 1 and Graph 2. Probability of observing a slice order is also higher in the midweek. Theory does not guide us in choosing the appropriate length of the preceding time interval for the calculation of explanatory variables. If the time interval is too long, the predictive power of the variables that influence slice order event cannot be captured. On the other hand, if the time interval is too short, there might not have enough depth in the market to induce traders to submit slice orders. Therefore, we explore the impact of the variables on the probability of slice order on different intervals. This also allows us to distinguish short term impact variables from the long term ones. 15

16 Table 8 reveals that the influence of imbalance disappears if this variable is measured in an interval of more than 30 minutes. On the other hand, OppMKT is significant if measured in an interval of more than 30 minutes. The coefficient is also positive as predicted earlier. This result is intuitive. Order imbalance of the near past is relevant to the slice trader if he is impatient. Order imbalance further in the past does not help him in devising a strategy as he has uncertainty about the type and side of orders that will arrive from other traders. On the other hand, OppMKT proxy for an impatient trader from the other side. Observing a higher percentage of impatient traders 45 minutes preceding the event imply that there will be more marketable limit orders in the opposite side in the subsequent intervals. And since those traders are aggressive, they set competitive prices. Once the price moves to a desired direction for the sliced order trader, he submits marketable limit order and achieves faster execution. Table 8 reports that the probability of observing a buy slice order is on an average 33% higher than that of a sell order. Therefore, we run Logit regression on buy and sell orders separately. Panel A of Table 9 reports the results for sell orders. Only significant variables are DepthOppVol, DailyVol and duration with predicted signs. TradeVol seems to influence if measured at preceding 45-min and 60-min interval and this variable also has its predicted sign. Panel B reports the results for buy orders. The result exhibits the same pattern as shown in Table 8. Specially, the switching role of order imbalance and OppMKT in short run and long run is also prevalent for the buy order case. The traders of a buy slice order seem to be very watchful about the market conditions before they submit slice orders. This result is particularly interesting because it is consistent with Keim and Madhavan (1995). In an empirical investigation on institutional trader, they find that buy orders take longer to execute than sell orders suggesting that the traders perceive that price impact of a buy order is greater than large sell order. If that is indeed the case, large traders of buy orders have enough incentive to hide their action by submitting slice orders. C. Average Execution Price and Return of a Slice Order Previous empirical research finds an asymmetric price response for a buyer versus seller initiated trade. The findings in Table 8 and 9 identifies a significant increase in the probability of observing slice order events if the trade is a buy. This result motivates us to explore average execution price of a slice order and compare it with an equivalent hypothetical market order. The intuition is as follows. If a large liquidity trader wants to reduce the price impact on his buy trade, he submits slice order instead of a market order of equivalent size. Therefore, the execution price of the slice order would be lower than the average execution price of a hypothetical market order. 16

17 The hypothetical market order is constructed in the following manner. The size of the hypothetical market order is equal to the size of a slice order. Execution price of a hypothetical market sell/buy order is the prevailing best bid/ ask if the quantity attached to the best bid/ask is sufficient to satisfy the limit order size. Otherwise, execution price is calculated as average price at three best level of bid/ask weighted by order quantity attached to each level. Totally unexecuted orders are excluded from the sample. The execution price of a slice order is the limit price. Panel A of Table 10 reports the average execution price of slice and regular order. The average execution price of a hypothetical market order is NIS , much higher than the actual execution price of a slice order, NIS Supporting the results from Table 9, we find that execution price of a buy slice order is much lower (NIS ) than a hypothetical market buy order (NIS ) while the execution price of a sell order (NIS 209.9) is much closer to the execution price of a hypothetical market sell order (NIS ). We also compare the average return after 15 min and 60 min of the trade for slice and hypothetical market order calculated as a percentage return with reference to best bid/ask at the end of corresponding interval. For hypothetical market order, return is calculated based on average execution price. A negative return is calculated for partially executed slice order in the following way: A = (quantity ordered execution price of a market order quantity executed limit price) B = quantity ordered limit price The negative return due to partial execution = A/B Net return of a partially executed slice order is the difference between return from trade and negative return due to partial execution. Panel B of Table 10 reports the average return of a slice order and a hypothetical market order after 15 minutes and 60 minutes of the trade. The return is negative for both types of orders and do not change much for two different holding periods. However, return of a slice order is lower than that of a hypothetical market order. The result might be due to the crude estimate of negative return for partially executed slice order. Probably we have given more weight to loss than to gain. We plan to explore more on the price impact of a slice order in the later version of the paper. D. Robustness Check We also run Probit regression on table 8 and 9 and get similar results. We also run some tests on dual listed stocks. The intra day flow of slice orders reaches a peak at the end of the day. This may be due to simultaneous trading of dual listed stocks in TASE and NYSE or in TASE and NASDAQ as mentioned before. Dual trading time starts at 4:45 pm TASE time. We check the incidents of slice order only for dual 17

18 listed stocks. Slice order incident is not that dominant in dual listed stock. Nor the incidents of slice order increase for dual trading stocks when US market is open. VII. SUMMARY AND CONCLUSION Tel Aviv Stock Exchange (TASE) data set gives us a unique opportunity to identify a group of orders defined as slice order which may well be used to hide the order volume of large traders. We argue that having a successive flow of three or more orders of same size and price within an interval of two minute is unlikely to arrive from different traders. We argue that these orders arrive from the same trader and are used as a strategic tool to achieve best execution or to reduce price impact of their large orders. We find support that slice orders indeed have some special characters. Slice orders are observed in most liquid stocks of TA100. We identify that the slice orders are aggressively priced and hit the transaction most of the time. The slice orders have shorter life span. Specially, the duration of unfulfilled slice orders is 28 minutes 34 seconds, much lower than the duration of regular unexecuted limit orders. Slice orders also improve the fill rate of partially executed orders. We find an increasing intra-day pattern in slice order submission, more prevalent at the end of continuous trading session. This type of pattern may emerge when institutional traders strategically hide at the end of the day by using slice orders. Since the professional money managers performance is evaluated at the closing price, they may also like to delay their trade till the end of the day. Order imbalance in the short run and a higher percentage of impatient traders from the opposite site in the long run increase the probability of observing a slice order. The depth at the same and opposite side increases the probability of a slice order incident. On the other hand, trade volume has an inverse effect. The probability of observing a buy slice order is on an average 33% higher than that of a sell order. The probability of observing a buy slice order is significantly influenced by more market condition variables than that of a sell slice order. The average execution price of a buy slice order compared to a hypothetical market order is also much lower. Our result is also consistent with the empirical findings that traders perceived price impact of a large buy order is greater than that of a large sell order. 18

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