Speed, Distance, and Electronic Trading: New Evidence on Why Location Matters. Ryan Garvey and Fei Wu *

Transcription

1 Speed, Distance, and Electronic Trading: New Evidence on Why Location Matters Ryan Garvey and Fei Wu * Abstract We examine the execution quality of electronic stock traders who are geographically dispersed throughout the U.S. Traders who are located near market central computers in the New York City area experience faster order execution. Moreover, the time to execute orders rises as a trader s actual distance (mileage) to NYC widens. In electronic market settings, data transfer limitations and transmission slowdowns result in geographically dispersed electronic traders having different access to trading speed. We find that speed advantaged traders experience lower transaction costs and engage in strategies that are more conducive to speed. * Ryan Garvey is with Duquesne University, Pittsburgh, PA Telephone , fax , Garvey@duq.edu. Fei Wu is with Massey University, Palmerston North, New Zealand. Telephone , fax , F.Wu@massey.ac.nz. We are grateful to seminar participants at the 2008 Financial Management Association Conference in Dallas, Texas, the U.S. securities firm for providing proprietary data, and the Security Industry Research Centre of Asia Pacific Limited (SIRCA) for providing computing support. 1

2 1. Introduction Our customers outside of New York are at a big disadvantage. Matthew Anderson, former President and CEO of Island ECN 1 The financial literature has uncovered differences in market participant performance and behavior across geographic locations (see, for example, Christoffersen and Sarkissian 2009, Hau 2001, and Ivkovich and Weisbenner 2005). In our paper, we examine if execution quality differences exist across geographic locations. Our study is motivated by recent trends in the marketplace, which suggest location matters for execution quality. For example, the sharp increase in electronic trading in recent years is leading many securities firms to engage in a practice known as co location, where they house their computers in the same location as electronic exchange computers. By doing so, firms can move closer to a markets central computer, increase trading speed, and gain an edge in highly competitive and fast moving securities markets. The Wall Street Journal (Lucchetti, 2006) reports that major securities firms, including Goldman Sachs, Merrill Lynch, Deutsche Bank, JP Morgan Chase, etc., have all recently moved their computers to the same buildings (neighborhoods) that house exchange mainframe computers. And the two largest stock markets in the U.S. have also reported large increases in their colocation businesses. 2 Does moving closer to a markets central computer really increase trading speed? And, in turn, does it create an advantage for trading U.S. equities? In our paper, we provide some insight for answering this question. If some locations provide market participants with access to faster trading speed, then this has important policy and economic implications. For example, if investors are at a disadvantage simply due to their trading location or where they submit their order from, then this raises 1 The Wall Street Journal (Lucchetti, 2006), Firms Seek Edge Through Speed As Computer Trading Expands December 15, For example, Nasdaq s co location business has risen 25% over the last two years. Approximately 100 firms are now co locating with Nasdaq s servers. Nasdaq charges co locating firms approximately $3,500 per (computer) rack per month (Martin, 2007). At the NYSE, co location demand is reportedly growing by leaps and bounds. The NYSE is building a new data center that will have a huge amount of co location space in order to help meet rising demand (Crosman, 2008). 2

3 questions about the fairness of trading in the marketplace, which is an area of utmost concern to securities regulators. 3 Moreover, if some locations are better than others for trading U.S. stocks then this will influence where firms locate their trading operations and how they conduct their order submission strategies. Access to faster trading speed is important because it allows market participants to better take advantage of information, which can result in lower transaction costs. There is a large and growing academic literature examining issues related to trading speed in financial markets. For example, Battalio, Hatch, and Jennings (2003) and Boehmer (2005) examine differences in execution speed across market centers and Garvey and Wu (2009) examine differences in speed across the trading day. These studies document a negative relation (trade off) between execution speed and costs. Boehmer, Jennings, and Wei (2007) examine the influence of execution speed and costs on order routing decisions. They find market centers reporting faster executions and lower costs subsequently attract more order flow. Hendershott and Moulton (2009) and Easley, Hendershott, and Ramadorai (2008) study the effects of changing trading speed within a market. Hendershott and Moulton find that increasing trading speed raises bid ask spreads and results in prices becoming more efficient, while Easley et al. find that increases in speed are accompanied by price appreciations and enhanced liquidity. Although no prior studies in the financial literature examine if, or to what extent, trading speed (and cost) differences exist across geographic locations, the financial literature examines differences in trader performance and choices across geography. For example, Hau (2001) examines the location and performance of proprietary stock traders who trade on the German electronic exchange Xetra. He finds traders in non German speaking countries experience lower trading profits than traders in German speaking countries. Christoffersen and Sarkissian (2009) examine mutual fund performance in relation to city size. They find that U.S. fund performance increases in accordance with the size of the city where 3 The Securities and Exchange Commission currently permits the practice of co location as long as electronic marketplaces allow competing firms equal access to their most valuable computer locations. 3

4 the fund is located and that manager experience plays an important role in explaining performance differences for funds located in key mutual fund financial centers (e.g., Boston, Chicago, Los Angeles, New York, Philadelphia, and San Francisco) versus funds located elsewhere. 4 A related strand of the location literature focuses on investor behavior and documents a strong bias towards local investing. While some studies attribute investors local bias to behavioral phenomena, such as investors familiarity with nearby firms (e.g., Grinblatt and Keloharju 2001, Huberman 2001, and Zhu 2002) others suggest that it is information driven due to locals ability to gather private (superior) information more efficiently on nearby firms (e.g., Coval and Moskowitz 1999, 2001, and Ivkovich and Weisbenner 2005). In our study, we analyze unique data on 3.6 million orders submitted by more than 2,000 geographically dispersed electronic stock traders in the U.S. to examine if, or to what extent, execution quality differences exist across geographic locations. Controlling for various factors that influence execution quality such as order characteristics, market conditions, stock characteristics, trader characteristics, etc., we find that traders who are located closest to market central computers in the NYC area execute their market orders 2.8% faster than traders who are located outside the NYC area. Moreover, market order execution times rise as trader s actual distance (mileage) to NYC widens. When traders act off of private information, a longer wait time will, on average, result in a cost. This occurs because competitive market forces ensure that new information quickly gets incorporated into security prices, and traders with access to faster trading speed can act before price changes occur or before they are completed. Moreover, when information asymmetries exist in the marketplace bidask spreads widen as limit order traders seek to protect themselves from trading with informed traders. Thus, more distant (informed) traders should experience higher trading costs (i.e. effective spreads). We find that market order effective spreads are 75 basis points lower when they originate from a trader who is located in the NYC area. And similar to our execution speed results, execution costs increase as 4 These cities, with the exception of Boston, are also the largest cities (based on population) in the U.S. 4

5 trader s actual distance (mileage) to NYC widens. On average, we find market orders exhibit a positive price impact, which suggests these orders contain information. Orders routed for immediate execution (i.e. market orders) provide an ideal opportunity for directly measuring if execution quality differences exist between geographically dispersed traders. Price contingent orders are more complex for testing execution quality differences because execution is not guaranteed and how the order is priced in relation to the inside price is (also) an important determinant of how fast (if at all) it will execute and at what cost. We control for how an order is priced in relation to the inside spread (along with our other controls), and then examine price contingent order execution quality in relation to trader location. We find similar geographic patterns with price contingent orders. For example, execution times rise and adverse selection costs increase as trader distance to NYC widens. If more distant electronic traders experience greater delays with placing, updating and or canceling their limit orders, then they are more likely to experience longer execution times and higher adverse selection costs. For example, if traders are careful about where they place their limit orders in relation to the inside spread (as our sample traders are), then the limit orders of more distant traders will be slower to reach the market and more likely to be near the bottom of the queue at various price levels, which would result in longer wait times and higher adverse selection costs. Moreover, when unexpected news arrives and prices change quickly, the "stale" limit orders of more distant traders will be "picked off" more easily resulting in greater adverse selection costs (as in Liu 2009 and Foucault, Roell, and Sandas 2003). If traders located closer to NYC have access to faster trading speed, then geographic patterns in trader behavior will emerge. For example, traders who are closer to NYC will engage in strategies that are more conducive to speed. We find that traders who are located within a 100 mile radius of NYC engage in more frequent trading, cancel more often, hold their positions for shorter periods of time, and submit more market orders. More specifically, NYC traders submit 11% more orders than D traders on a 5

6 typical (average) trading day and hold their positions for a 9% shorter period of time. In addition, NYC traders are, on average, 12% more likely to cancel their orders and 18% more likely to submit a market order. Prior research suggests location advantages arise in financial market settings due to cultural and or linguistic factors, due to the better learning environment present in larger metropolitan areas, or due to local traders obtaining private (more) information on nearby firms. Our research differs in that we find evidence suggesting that location advantages (also) arise in financial market settings due to physical issues. For example, data transfers are moved by electricity, which is limited by the speed of light (i.e. 186,000 miles per second). 5 Thus, a trader located in New York will always be able to execute their orders faster than, say, a trader located in Los Angeles. Los Angeles is approximately 3,000 miles away from New York, so it will take 3/186ths of a second, or approximately 16 milliseconds for light to travel that distance. 6 Given that many U.S. markets now operate in milliseconds, or even microseconds, the speed of light has become a limiting factor for many U.S. market participants (especially for those operating abroad). The limitation brought on by the speed of light is not the only factor, though, that will cause a disadvantage for traders who operate out of more distant locations. For example, electronic data transfers typically occur over multiple networks (i.e. the Internet). The number of network switches that data incurs along its travels will bring on additional time delays for more distant traders. Data transmission time delays are likely to be far greater than data transfer limitations, and they can potentially add seconds (or even minutes) to the time it takes to execute a stock order. 7 5 Electricity is also limited by the material it must travel through (e.g. a copper wire). 6 A millisecond is 1/1000ths of a second. 7 Electronic communication devices are typically not connected point to point. Instead, a transmission device (e.g. a trader s computer) usually first connects to a local area network (LAN). Data then travels from a LAN, or a metropolitan area network (MAN), across wide area networks (WANs). A WAN covers a large geographical area and is used to connect local area networks together (e.g. a computer in Los Angeles with a computer in New York). The Internet is a collection of communication networks (e.g. LANs, MANs, and WANs). Typically, a WAN consists of a number of interconnected switching nodes. A transmission from any one device is routed through these internal nodes to the specified destination device. Data are transmitted in blocks called packets. At each node en route, a data packet is received, stored briefly, and passed on to the next node. This packet switching process results in 6

7 The remainder of our paper is organized as follows. In the next section, we describe our data. Section 3 examines if traders who are located closer to NYC have access to faster trading speed. After finding a negative relation between order execution speed and trader distance to NYC, we discuss some of the economic implications of this result in Section 4. Section 5 examines a potential (alternative) explanation for why results show that traders located closer to NYC experience more favorable order execution quality and Section 6 provides concluding remarks. 2. Data We use three data sources in our study. First, proprietary data are obtained from a large broker dealer with clients geographically dispersed throughout the U.S. The proprietary data enable us to measure execution speed and costs for orders that originate from traders in different U.S. locations. We then obtain historical intraday pricing data from Reuters. 8 We use this data to simultaneously examine market conditions, such as the bid ask spread, bid/ask depth, market volatility, and trading volume when assessing order execution quality and trader location. Finally, we use The Center for Research and Security Price (CRSP) database to analyze summary trading information such as price, market capitalization, and trading activity for each stock. The U.S. broker dealer we used for our sample had several trading operations. In their brokerage operation, they provided their clients with direct access trading and support services. Brokers providing Direct Access services attract more sophisticated traders because they allow their users to control where and how their orders are routed for execution. The orders of Direct Access users time delays for distant traders. See Stallings (2004) for a more detailed description of data and computer communications. 8 For information on Reuters tick history database, see 7

8 are routed directly to exchanges, alternative trading systems, etc. using sophisticated trading software. 9 Direct Access brokers also tend to provide centralized locations for trading in order to better support the needs of their clients. These trading locations are equipped with the latest trading technology, direct high speed connections to the financial markets, and onsite support staff. 10 Our firm is headquartered in the New York City area and they trade in 36 branch office locations in 17 states. The branch offices are located in New York, New Jersey, Connecticut, Pennsylvania, Virginia, Michigan, Tennessee, Illinois, Georgia, Florida, Missouri, Oklahoma, Texas, Arizona, California, Washington, and Oregon. The data are in the form of a transaction database and span the near four year period October 7, 1999 to August 1, For every order request, the data provide the identity of the trader, the location of the trader, the time of submission, the time of execution, the market where the order was sent, the execution size, the execution price(s), the stock symbol, the order type, and various other information concerning the trade execution (or cancellation) and executing account. The most important feature of the data is the identity of each trader s actual trading location. We select data on accounts in which we can determine where a trader is physically operating from in the U.S. (i.e. branch office traders). We then filter the trading activity of these accounts in the following manner. First, we examine Nasdaq listed stock trading only. Nasdaq listed stocks, unlike NYSE listed stocks, trade in a fully electronic trading environment during our sample period. 12 And 9 Direct Access traders account for a sizeable portion of trading volume in U.S. equity markets. For example, Goldberg and Lupercio (2004) find that approximately 40% of Nasdaq and NYSE trading volume is executed by active traders (25+ trades per day) who trade through Direct Access brokers. 10 The branch offices all use the firms direct access trading software. The electronic trading terminals at the firm s branch offices (headquarters) are aligned with the official U.S. time, which ensures a central and comparable measure of time across geographic locations. 11 Our sample period was considered a very challenging market trading environment (GAO, 2005). The GAO cited the sharp decline in stock prices, heightened competition, the switch to a smaller tick size, etc., for the challenging market environment. The revenues of broker dealers with a Nasdaq market making operation fell over 70% during Nasdaq listed stock trading represents a majority of trading activity (more than 95%) originating out of our sample firm. Professional traders are attracted to Nasdaq listed stocks because of their fully electronic trading environment which ensures rapid execution. Direct Access brokers are beneficial because they allow traders to directly route their orders to the various electronic markets (market participants) trading Nasdaq stocks. In 8

9 whether or not execution quality differs across geography in electronic market settings is the focus of our research. Second, we exclude orders executed through negotiation means or algorithmic type orders in which a trader does not route their order to a particular trading venue. The execution speed (and costs) associated with these two order types can be driven by many unobservable factors and our ability to adequately control for these factors and properly link speed to distance is severely limited. 13 Finally, we exclude orders on stocks in which we could not obtain relevant summary trading data from the CRSP database or intraday data from the Reuters tick history database. We link trading information from these two publicly available data sources to our proprietary data when conducting our empirical analysis. In total, we find matching trade data on Reuters and CRSP for more than 98% of the sample data. Table 1 provides some summary trading information for sample traders. In total, 2,215 geographically dispersed electronic stock traders execute 6.6 billion shares, 5.6 million trades, and 3.7 million orders (accounting for $65.3 billion in trade value) over the near four year sample period. While the traders execute orders on 3,414 stocks, they tend to concentrate their trading activity on certain stocks at certain times, often representing a sizeable portion of daily trading volume. The traders are also quite active in setting market prices. We discuss how often they set market prices in Section 3. Trading activity in our sample tends to mirror overall trading activity patterns in the Nasdaq Stock Market. For example, trading is heavy around the opening and closing hours of the day (more so around the open) and lighter during the middle of the day. Also, the most actively traded stocks of our sample traders are also the most actively traded stocks in the Nasdaq market. Although this is recognizable through casual observations of the data, we examine the issue more formally. We collect contrast, NYSE trading predominantly occurred (approximately 80%) on the floor of the NYSE during our sample period, which made these stocks less appealing. 13 For example, some negotiated orders may take longer to execute, not because of distance, but because traders have a strong desire to execute at a certain price. Some algorithmic orders may take longer to execute, not because of distance, but because traders set wider parameters that allow them to search, route, and execute over multiple trading venues. 9

10 data from CRSP on overall trading volume on Nasdaq listed stocks over our entire sample period. We then select the top 30%, top 10%, and top 100 traded stocks from this pool. We calculate the percentage of these stocks volume relative to overall Nasdaq volume. And we calculate the percentage of trading activity these top heavily traded stock account for in our sample data. The top 30% most heavily traded stocks in Nasdaq account for 94% of trading activity in Nasdaq and 99.4% in our sample, the top 10% account for 80% of trading activity in Nasdaq and 96.3% in our sample, and the top 100 stocks account for 54% of trading activity in Nasdaq and 84% in our sample. 3. Proximity to New York and trading speed 3.1. Empirical design One way to directly measure if, or to what extent, trading speed differences exist across geographic locations is to measure the execution time difference between geographically dispersed traders when they submit the same order (e.g. market order) on the same stock (e.g. MSFT) to the same market (Nasdaq) at the exact same time (e.g. 10:00:00 a.m.). Such precise matching of orders can be quite challenging. For example, even if we could locate two geographically dispersed traders within our sample who trade the same stock at the same time, traders can use multiple order types and/or routing procedures which can impact trading speed in various ways and make direct comparisons difficult. During our sample period, nearly all trading on Nasdaq stocks occurred on either Nasdaq or on Electronic Communication Networks (ECNs). 14 ECNs are electronic trading systems that automatically match buy and sell orders at specified prices. ECNs are predominately used for price contingent orders and all ECN orders in our sample are price contingent. Traders submitted their market orders to the Nasdaq stock market and their limit orders to ECNs. Limit orders placed in ECN books are typically displayed on the consolidated quote stream through exchanges. Market orders routed to Nasdaq for 14 For a more detailed discussion of the competition between Nasdaq and alternative trading venues during our sample period see Goldstein et al. (2008) and Barclay et al. (2003). 10

11 immediate execution provide an ideal opportunity for directly measuring if an execution speed difference exists between geographically dispersed traders (Nasdaq market makers are required to maintain two sided quotes throughout the trading day). Price contingent orders routed to electronic limit order books are more complex for testing speed differences because execution is not guaranteed and how the order is priced in relation to the inside price is (also) an important determinant of how fast (if at all) it will execute and at what cost. However, we attempt to control for how an order is priced in relation to the inside spread, and then we examine price contingent order execution quality. Because Nasdaq and the other electronic exchange central computers are located in various locations around the NYC area in New York, New Jersey, and Connecticut, 15 we use a NYC center mileage point as a reference point for all trader distance calculations. The NYC center mileage point is at the corner of Canal and Centre Streets in lower Manhattan. Mapquest is used ( to determine all mileage distances between the traders actual locations and NYC. We compute execution speed for each order as the difference between the order submission time and the order execution time (share weighted for multiple trade orders). 16 While execution quality is a frequent topic of research in academic studies, our analysis provides a different and more robust measure of execution quality than most studies in the financial literature. 17 Prior studies in the finance literature often examine execution quality using transaction data, such as the NYSE Trade and Quote (TAQ) database. However, transaction data cannot assess execution speed, which has become an increasingly important dimension of execution quality and execution cost is evaluated in terms of the parts (the trades) rather than the sum of the parts (the originating order). Some studies examine 15 For example, during our sample period Nasdaq s main data center was located in Trumbull, CT. The Island ECN, a popular alternative to Nasdaq for electronic traders, had their central computers located in lower Manhattan. Using more specific mileage points (e.g. Trumbull) for individual markets (data centers) located within the NYC area does not change our results. 16 We also compute execution speed from a trader s original submission to their first (partial) execution. The results are qualitatively similar (See Section Robustness Checks). 17 Execution quality or best execution is often difficult to define because there are multiple dimensions of execution quality and traders often have different preferences for the various dimensions (see Macey and O Hara 1997). 11

12 execution quality using various order level information databases (e.g., Boehmer 2005 and Harris and Hasbrouck 1996), however the data used in these studies are usually not linked to specific traders. 18 Consequently, execution quality is not measured from a trader s originating order, which is an important omission because most individual traders assess execution quality from their original order submission Distance and order execution speed In Fig. 1, we highlight market order execution speed differences using different distance classifications. First, we use a 100 mile radius from the center of NYC to classify Financial Center (FC) traders. We classify traders located outside of the 100 mile radius as Distant (D) traders. Fig. 1A shows FC traders execute their market orders, on average, considerably faster than D traders when comparing the average speed difference across all orders (4.90 vs seconds). Our overall market order execution times on Nasdaq are similar to those reported by other studies examining market center execution quality reports. For example, Goldstein et al. (2008) use Dash 5 data to examine market order execution times, for stocks in the Nasdaq 100 market index, during the second quarter of They find an average market order execution speed on Nasdaq of 7.7 seconds. In Fig. 1B, we further classify traders into three mileage band classifications. The mileage band classifications are based on the mileage distance between a trader s actual location and the center of NYC. The three mileage band classifications are: 1) 0 1,000 miles, 2) 1,001 2,000 miles, and 3) > 2,000 miles. Market order execution clearly takes longer as a trader s distance to NYC increases. For example, traders who are located within 1,000 miles of NYC have an average execution speed for market orders of 18 Boehmer (2005) uses market center execution quality reports initiated by SEC Rule 11Ac1 5 (Dash 5). Dash 5 reports are increasingly used by researchers to measure execution quality. Dash 5 reports are published monthly by market centers for each stock, in four order size categories: , 500 1,999, 2,000 4,999, and 5,000 9,999 shares. Harris and Hasbrouck (1996) use the NYSE Trade, Order, Record, and Quote (TORQ) database. The TORQ data has been analyzed in several studies in the finance literature. The TORQ and Dash 5 data do not identify individual traders. 12

13 5 seconds, traders who are located between 1,001 and 2,000 miles from NYC have an average execution speed of 7.05 seconds, and traders who are located more than 2,000 miles from NYC have an average execution speed of seconds. All of the differences for speed are significantly different from zero at the 1% level using a t test (for both an average and share weighted basis). The initial results indicate that trading speed differences exist across geographically dispersed traders and those traders who are located closer to NYC experience faster trading speed. There are multiple factors other than distance that might have an impact on market order execution speed. For example, the size of an order, the depth in the market, and/or market volatility could all reasonably impact the time it takes to execute an order. Therefore, we are interested in examining the relation (if any) between speed and distance while controlling for various factors that might (also) influence market order execution speed. We are also interested in examining if execution quality differences exist across geography for price contingent orders. Traders are much more likely to submit a price contingent order than a market order. While market orders submitted to Nasdaq are ideal for measuring if a speed difference exists between geographically dispersed traders, price contingent orders are more complex for measuring speed differences because how an order is priced in relation to the inside spread is an important determinant of how fast it will execute. Faster executions for price contingent orders could be driven by a speed advantage or also (partly) driven by how aggressive a trader is with pricing their order. Thus, it is important to control for how a trader prices their order when examining the execution time of pricecontingent orders. If the limit orders of more distant traders are slower to reach the market, and if traders are careful about where they place their limit orders in relation to the national best bid or offer, then the limit orders of more distant traders are more likely to be near the bottom of the (time priority) queue at various price levels resulting in longer wait times. Using Reuters tick data, we examine how traders 13

14 price their orders in relation to the national best bid and offer (NBBO) prevailing at the time of order submission. We segregate orders into three classifications: 1) Marketable limit orders have a price for buy (sell) orders set greater (less) than or equal to the national best offer (bid) at the time a trader submits their order. Price setting orders have a limit price set at the best price or within the inside spread at the time a trader submits their order. Away from the inside quote represents buy (sell) orders that a trader prices below (above) the best bid (offer) at the time of order submission. In Fig. 2, we report summary information for the percentage of orders under each classification. Overall, traders are more likely to submit price setting orders. For example, approximately 65% of the orders are priced at or within the inside spread at the time of order submission. Approximately 17% of the price contingent orders are marketable and 18% are priced away from the inside quotes. To statistically examine the relation (if any) between distance and order execution time, we first select some common determinants of marketable/non marketable order execution quality used in the financial literature. We then use the survival analysis approach following Lo, MacKinlay, and Zhang (2002) to estimate a model that takes factors such as skewness and censoring into account (e.g. the cancellation of an order censors the duration of execution). Specifically, we use an accelerated failure time (AFT) model that assumes execution speed follows a Weibull distribution. Our model takes the format: log _ 1 where execution speed represent the time to execute for each order i. The main independent variable of interest is the log mileage distance between a trader s location and the center of NYC. The other independent variables in both regressions include limit order aggressiveness (non marketable orders only), which is computed for buy orders by subtracting the NBBO midpoint at the time a trader 14

15 submits their order from the limit price and for sell orders by subtracting the limit price from the NBBO midpoint; a dummy variable that takes the value of 1, or 0 otherwise, if a trader submits a buy order; order size (shares); NBBO percentage spread at the time of order submission (100*(ask price bid price )/midpoint price); depth at the inside price (offer (bid) depth for marketable buy (sell) orders and vice versa for non marketable orders) at the time a trader submits their order; total trading volume on the stock within the half hour interval; price volatility within the half hour interval, which is computed by subtracting the minimum execution price from the maximum execution price and dividing the difference by the average execution price within the half hour interval; a dummy variable that takes the value of 1, or 0 otherwise, if the order is executed after decimalization; dummy variables for four intraday time intervals; the prior year average daily turnover (volume / shares outstanding) for the stock; the prior year end market capitalization for the stock; and the prior year end price for the stock. The model also controls for trader specific effects. We classify traders into groups and use category dummies. 19 We sort the traders into 25 groups based on two trader characteristics: 1) the average order size and 2) the average daily number of orders executed. The size in which a trader trades in, or how active a trader is, may serve as good proxies for the financial sophistication level of a trader, and the financial sophistication level of a trader might have an influence on order execution quality. In addition to estimating results using a continuous measure of distance, we also estimate results using a financial center dummy. The financial center dummy takes the value of 1, or 0 otherwise, if an order is submitted by a trader located within a 100 mile radius of NYC. The use of a FC dummy variable is motivated by the fact that our sample firm is headquartered in the NYC area and many clients 19 The trader dummies for the various classifications are not reported in the Tables for brevity. We run our results both with and without these trader dummies and the results are qualitatively similar. 15

16 are located in or around the NYC area. For example, 1,039 traders (16 branches) are located within a 100 mile radius of NYC and 1,176 traders (20 branches) are located outside of the area. 20 The results are reported in Table 2. The dummy coefficient representing FC traders is negative and the coefficient representing trader distance to NYC is positive. These results are highly significant for all order types, which indicates that orders originating from FC traders execute faster and that as the distance between a trader and NYC widens (measured in miles) order execution times correspondingly rise. To interpret economic significance, consider the FC dummy coefficient. The FC dummy coefficient for market orders is , which suggests that after controlling for order characteristics (order size and order type), market conditions (spread, depth, volume, and volatility), tick size (decimalization), stock characteristics (market capitalization, price, and turnover), and trader characteristics (trading activity and trading size), market orders submitted by FC traders execute 2.80% (e ) faster than market orders submitted by D traders. Similarly, FC trader marketable limit orders, price setting limit orders, and away from the inside quote orders have a shorter time to execution of 15.01%, 7.51% and 10.07%, respectively The control variable coefficients reveal the influence of other factors on order execution time. For example, the sign and magnitude of certain market condition coefficient estimates suggest that as liquidity deteriorates in the marketplace, so too does market order execution speed. When the bid ask spread widens, market order execution takes longer. The bid ask spread coefficient is positive and statistically significant at the 1% level. When depth at the inside price increases, market order execution becomes faster. The bid/ask depth coefficient is negative and statistically significant at the 1% level. When trading activity increases (half hour volume), market order execution also becomes faster. The half hour volume coefficient is negative and statistically significant at the 1% level. The bid ask spread, bid/ask depth, and trading activity are often used as proxies for liquidity in financial studies. 20 We also use two other mileage band classifications and classify traders who are located within a 50 mile and 250 mile radius of NYC as financial center (FC) traders. The results are qualitatively unchanged. 16

17 3.2.1 Robustness checks We conduct two robustness checks for our main results. 21 First, we measure execution time from a trader s original submission to their first (partial) execution. We measure execution time from a trader s original submission to their last (partial) trade execution because a trader s distance to NYC can directly result in orders being split up. Multiple trade orders take longer to execute and, in the case of market orders, can result in substantially higher costs as the order moves from one price level to the next. Thus, examining order (rather than trade) execution provides a more complete picture for assessing and comparing execution speed (cost) differences across geography. When we use time tofirst execution for our measure of time, the results are qualitatively similar. All coefficients representing trader distance to NYC (FC dummy coefficient) are positive (negative) and significant at the 1% level. For our second robustness check, we estimate results on an individual stock basis and test whether the cross sectional mean/median coefficient is significantly different from zero. In order to ensure a proper number of observations per stock, we limit the analysis to the top 300 stocks traded which account for more than 90% of trading activity. We know that a large portion of trading activity within our sample occurs on actively traded Nasdaq stocks (see Section 2), and while we control for stock characteristics in our model, there is still the possibility that differences in sample stock composition drive our results. Conducting regressions on an individual stock basis helps to alleviate this concern. The cross sectional mean/median FC dummy coefficients (distance coefficients) are negative (positive) and highly significant for all order types, which indicates that as traders distance to NYC increases, order execution times rise. 3. Implication of Results 3.1. Access to faster trading speed affects market participant trading costs 21 The robustness results are not reported for brevity. They are available upon request. 17

18 Results consistently show that traders who are located further away from market central computers in the New York City area experience slower order execution. A natural question that arises is: what are the implications that arise from market participants having varying access to trading speed? In this Section, we briefly touch upon three of them. First, access to faster trading speed affects market participant trading costs. For example, when traders act off of private information, a longer wait time will, on average, result in an economic cost. This occurs because competitive market forces ensure that new information quickly gets incorporated into security prices, and traders with access to faster trading speed can act before price changes occur or before they are completed. Moreover, when information asymmetries exist in the marketplace bid ask spreads widen as limit order traders seek to protect themselves from trading with informed traders (see, for example, Copeland and Galai 1983 and Glosten and Milgrom 1985). Thus, more distant (informed) traders will experience higher trading costs (i.e. effective spreads) when submitting market orders. Of course, this assumes that orders sent for immediate execution, on average, contain information. This assumption is in line with the financial literature, which indicates informed traders have a preference for executing fast (e.g. Barclay et al. 2003) and that market orders are more informative than limit orders (see, for example, Rock 1996 and Glosten 1994). 22 We examine the information content of market orders and limit orders in our sample by looking at price impacts. Price impacts measure the change in the NBBO midpoint five minutes after a trader s order is executed. The average price impact for market orders is 7.2 cents and the average price impact for limit orders is 2.7 cents (both means are significantly different from zero at the 1% level). If more distant electronic traders experience greater delays with placing, updating and or canceling their price contingent orders, then they are also more likely to experience higher transaction 22 Trader s choice between market and limit orders might be driven by various factors, such as market conditions. For example, Biais, Hillion, and Spatt (1995) find traders are more likely to submit limit (market) orders when the bid ask spread is large (small). 18

19 costs. For example, when unexpected news arrives and prices change quickly to reflect the news, the "stale" limit orders of more distant traders will be "picked off" more easily resulting in greater adverse selection costs (as in Liu 2009 and Foucault, Roell, and Sandas 2003). We estimate that non marketable order execution costs will follow an ex post cost approach used by Harris and Hasbrouck (1996) and Peterson and Sirri (2003). The ex post cost of executing a limit buy (sell) order is computed as the difference between the share weighted execution price and the national best bid (ask) price five minutes after execution. The ex post cost measure attempts to portray the adverse selection cost incurred with limit order execution. In Fig. 3, we highlight some execution cost differences for trader market orders using various distance classifications. As with execution speed, our focus is on market order execution costs because they provide a more robust setting for measuring execution cost differences. FC trader effective spreads are lower than D trader effective spreads ($0.08 vs. $0.19). Effective spreads become higher as a trader s distance to NYC increases. For example, traders who are located within 1,000 miles of NYC have an average execution cost for market orders of $0.10, traders who are located between 1,001 and 2,000 miles from NYC have an average execution cost of $0.13, and traders who are located more than 2,000 miles from NYC have an average execution cost of $0.25. All of the differences for costs are significantly different from zero at the 1% level using a t test (for both an average and share weighted basis). Differences in trading costs are economically important when considered in the aggregate. For example, traders execute over 724 million market order shares. Therefore, an eleven cent FC D difference in effective spreads represents an execution cost differential of over $79.6 million in our sample alone. Similar to our analysis with execution speed, we examine trader distance and execution costs while controlling for various factors that might (also) influence order execution quality. We use an ordinary least square (OLS) regression to measure the determinants of order execution costs. The regression models take the format: 19

20 _ 2 where execution cost represent the effective spread (market and marketable limit orders) or expost cost (price setting and away from the inside quote orders) for each order i. Independent variables are the same as in Eq (1). Also, similar to our execution speed analysis, we estimate regressions using a financial center dummy rather than a continuous measure of distance. The regression results are reported in Table 3. For market orders, marketable limit orders, price setting orders, and away from the inside quote orders, the coefficients representing FC dummy (trader distance to NYC) are negative (positive) and significant at the 1% level. The results indicate that FC traders, on average, have a lower execution cost and as the distance between a trader and NYC widens (measured in miles), effective spreads and ex post costs increase. To further interpret the economic significance of coefficients, consider the FC dummy coefficient. The FC dummy coefficient is This implies that a FC trader has an average execution cost 0.75% (or 75 basis points) lower than a D trader. A FC trader has an average execution cost 2.25%, 0.77%, and 1.43% lower than a D trader for marketable limit orders, price setting limit orders, and away from the inside quote orders, respectively. We conduct our robustness checks discussed in the prior Section and find qualitatively similar results Access to faster trading speed affects market participant behavior If market participants have varying access to trading speed, then we would expect them to align their trading strategies accordingly. For example, traders who are located closer to NYC will engage in higher frequency or more aggressive trading strategies due to their speed advantage. Thus, access to faster trading speed should affect market participant behavior. We examine the overall trading behavior of FC traders and D traders. We focus on five trader behavior measures 1) daily number of 20

21 orders executed, 2) daily trade value, 3) average daily round trip holding time (seconds), which is shareweighted for multiple round trip trades, 4) percentage of cancellations, which is the daily trade value of cancellations divided by the daily trade value of executions and cancellations, and 5) percentage of market orders, which is the daily percentage of market orders divided by the daily trade value of executions. Our five trading behavior measures are meant to serve as proxies for trading speed. In Table 4, we report the daily mean/median behavior measures across traders for both FC traders and D traders. A t test and a Wilcoxon signed rank test are used to test the null hypothesis that the mean and median differences are equal to zero. Clearly, FC traders engage in strategies that are more reliant on trading speed than D traders. FC traders submit 11% more orders than D traders on a typical (average) trading day and hold their positions for a 9% shorter period of time. In addition, FC traders are, on average, 12% more likely to cancel their orders and 18% more likely to submit a market order. The differences between FC traders and D traders are significantly different from zero at the 1% level Access to faster trading speed affects market participant location decisions If certain geographic area provide market participants with access to faster trading speed than this will naturally influence where firms (traders) choose to locate their operations. Thus, access to faster trading speed affects market participant location decisions. For example, Dave Cummings, CEO of Tradebot Systems Inc. and founder of the Better Alternative Trading System (BATS) 23 noted that by moving his firm s computers from Kansas City to New York, he was able to achieve faster execution and subsequently improve trading performance. If Tradebot did not relocate their computers, Cummings claims that we d be out of business (Lucchetti, 2006). While access to faster trading speed is certainly important to market makers, dealers, certain hedge funds, and other short term traders who operate on a daily basis in the U.S. equity markets, not all professional traders whose trading strategies are heavily 23 In fall 2008, BATS transitioned from an Electronic Communication Network (ECN) to a Stock Exchange. BATS is now the third largest equity market in the U.S. 21

22 built around trading speed are physically located in or around the NYC area. If traders who are located closer to market central computers do, indeed, have access to faster trading speed as our results seem to indicate, and as market operators and financial executives claim, then why would some (or even any) securities traders choose to operate out of more distant locations? One reason may be that the cost of gaining access to faster trading speed, either by paying an exchange for co location rights or simply moving closer to a market s central computer, is not justifiable in all cases. For example, the cost of operating out of the NYC area is obviously much higher than it is in most other U.S. locations. Traders, whose strategies are not heavily reliant upon execution speed, might find it more advantageous to forgo the costs incurred with gaining access to faster trading speed in order to achieve lower operating costs in a more distant location. 4. Additional Results: An Alternative Explanation Our empirical results to this point suggest that a trader s distance to NYC matters for order execution quality. Traders who are located closer to NYC appear to have access to faster trading speed and, in return, lower execution costs. An alternative explanation for why traders located closer to NYC exhibit superior order execution quality is due to their higher level of financial sophistication. The NYC area is home to some of the premier investment banks and trading houses in the world. These firms tend to pay the highest salaries and, at least historically, attract the best talent from both within and outside the tri state area around NYC. Many individuals who are employed by these firms go on to trade for themselves and or start their own (smaller) trading firms. Because these sophisticated individuals are already located in the NYC area, they often choose to stay in their current location to set up their operations. These traders and smaller firms are more likely to use the services of Direct Access brokers for implementing their trading strategies. 22