The Cost of FX Liquidity

BRANDEIS UNIVERSITY The Cost of FX Liquidity Empirical Tests of Competing Theories Geir Høidal Bjønnes Norwegian Business School Neophytos Kathitziotis University of Hamburg Carol Osler Brandeis International Business School Abstract: This paper examines the forces that determine variation across customers in the foreign exchange spreads set by dealing banks. We focus on four factors highlighted in the literature. The first, fixed order-processing costs, might explain why spreads vary inversely with trade size. The other three concern the nature of the bank s counterparty: its average trading activity, the extent to which it tends to be informed, and its market sophistication. Using extensive, highly disaggregated transaction data from a top-20 dealing bank we show that all these factors are relevant and that they help explain why liquidity is generally less costly for financial customers than non-financial customers in currency markets. First draft: January 11, 2014 This draft: September 16, 2014

The Cost of FX Liquidity Empirical Tests of Competing Theories This paper tests competing hypotheses about the determinants of liquidity costs in foreign exchange markets. The puzzle that prompts our research is the negative relation between bid-ask spreads and the information content of trades in the OTC foreign exchange trading (Osler et al., 2011). Spreads are narrower for customers who are considered better informed by their dealers, specifically financial customers, and narrower for larger trades. This is inconsistent with the predictions of adverse selection theory (Glosten and Milgrom, 1985; Easley and O Hara, 1987), which dominates as a theory of spreads. Osler et al. (2011) highlight four key factors that could potentially explain this pattern. The first factor, fixed order-processing costs, could explain the inverse relation between spreads and trade size, since a given cost can be covered by a smaller spread on a larger trade. The three other factors all concern the nature of a given customer. The second factor, a customer s average trading activity, could be important if dealers provide volume discounts (Bernhart and Hughson, 2002). The third factor, asymmetric information, could influence markups if dealers attempt strategic dealing (Naik et al. 1999), which can be rational in two-tier markets like foreign exchange. When an active interdealer market is available dealers can gather information by trading with customers and then exploit that information in subsequent interdealer trades (Bjønnes et al., 2011). The potential losses to informed customers that motivate adverse selection could be more than offset by gains in trading against other dealers. This could motivate dealers to strategically attempt to attract informed customers by quoting them narrower spreads. The fourth factor, a customer s market sophistication, could influence the customer s negotiating leverage vis-à-vis their dealers. Dealers can charge unsophisticated customers wider spreads 1

without much risk of losing future business. Green et al. (2004), who first identified the relevance of this factor, refer to it as market power but since the phrase has strong associations with unrelated concepts like monopoly and oligopoly we refer to it as the market sophistication hypothesis. It is common in the literature to assume that one or another factor must dominate. Reitz et al. (2012), for example, claim that market sophistication matters while information asymmetries do not. But distinguishing among these hypotheses has been essentially impossible because the available transaction data only distinguish between financial and corporate customers. Since financial customers trade more actively than corporate customers, are generally more sophisticated about the market, and are often better informed than corporate customers, the volume discount, strategic dealing, and market sophistication hypotheses were observationally equivalent. This paper examines the complete foreign-exchange trading record for a top-20 dealing bank during over three months of 2012. For each trade this extremely detailed dataset includes the currencies, size, direction, time, trading venue, a counterparty ID, the customer category assigned by the bank, the outright transaction price, and the markup over the relevant interbank price (the bid for customer sales, ask for customer purchases). If the customer was trading through a prime broker the dataset includes an ID for the underlying customer. These data are superior to others available for identifying how currency spreads are influenced of the four factors outlined above -- fixed costs, volume discounts, asymmetric information, and market sophistication. The investigation requires transactions-level data but many currency datasets include only interdealer trades (e.g., Berger et al., 2008; Chinn and Moore, 2009; Killeen et al. 2006; Daníelsson and Love, 2006; Breedon and Vitale, 2010). Of the 2

datasets that do include customer trades, many aggregate such trades to the daily horizon (e.g., Evans and Lyons, 2005, Froot and Ramadorai, 2005, Gyntelberg et al., 2009, March and O Rourke, 2005, and Bjønnes et al. 2005a). Our investigation also requires extensive information about individual customers, but most transactions datasets group customers into a few types, typically financial and non-financial (e.g. Evans and Lyons, 2005; Osler et al., 2011). Finally, our investigation requires trades from the full spectrum of customer types, which cannot be achieved with datasets that include only one particular customers group (e.g. Ramadorai, 2005). We focus on the markup paid by individual customers of our bank, meaning the difference between the customer s price and a representative interbank bid or ask quote (as appropriate) at the time of the trade. We regress each trade s markup on variables that capture to each of the four key factors. To capture the influence of order-processing costs we include each trade s own size, since a smaller proportionate spread will cover a given fixed cost on a larger trade. To capture any tendency to give volume discounts, we include each customer s average trade size and each customer s frequency of trading with the bank. To capture the influence of asymmetric information about fundamentals we include each customer s average signed posttrade return. To capture the influence of a customer s market sophistication we consider the frequency with which a customer trades over a given type of venue, with venues divided into four categories of sophistication. The results indicate that all four of our key factors are important determinants of forex customer spreads. Order-processing costs matter. Clients definitely get volume discounts, and dealers consider both the frequency and the average size of their trades. Asymmetric information matters, but in different ways according to counterparty. Dealers attempt to attract trades from informed non-bank customers, consistent with strategically dealing. But dealers also attempt to 3

protect themselves from adverse selection when trading with other banks. Finally, market sophistication matters, too: less sophisticated customers pay wider spreads. The rest of this paper has four sections and a conclusion. Section I describes our data. Section II outlines in greater depth the four key factors that potentially influence the cost of liquidity in foreign exchange markets and how we measure the variables used to identify their actual influence. Section III provides our main econometric results. Section IV presents robustness tests. Section V Concludes. I. DATA We utilize a dataset containing all spot deals of a top-20 forex dealing bank over the 68 trading days from 2 January 2012 to 20 April 2012. The sample includes over 2 million transactions in 22 currency pairs. Such richness is costly to process, however, and for our purposes a smaller sample will provide ample precision, so we focus on the most liquid currency pair, EUR/USD (B.I.S. 2013). The paper is about end-user trades so we naturally exclude internal trades and all trades with other top 50 Euromoney dealing banks: the latter have a high likelihood of being pure interbank trades. For each transaction the data provide the following information: Currency pair; date and time stamp of the trade (to the second); transaction price; quantity traded; sign of trade (buy or sell); initiating party; portfolio within the bank to which each trade is assigned; counterparty ID; trading venue. For the customer trading examined in this paper there are no trading fees per se. A customer s bid-ask spread thus captures the entire transaction cost for a given trade. 4

To keep a tight focus on how the price of liquidity varies cross-sectionally, we exclude any trades where our bank is a price taker, since our focus is how dealers set prices. 1 Our bank is major FX market maker so it sets prices in the vast majority (over 90%) of customer trades. After exclusions our final regression sample includes 265,374 transactions. We partition customers into the following six categories: Real-money funds, hedge funds, multinational corporations and large non-financial firms ( MNCs ), small and medium enterprises ( SMEs ), brokers, and customer banks. This partition follows the business logic of big dealing banks and is familiar in currency market microstructure. Real-money funds and leveraged asset managers primarily use currencies as a store of value. Real-money funds include mutual and pension funds, insurance companies, and a few endowments and public institutions (ministries, central banks) that behave similarly to asset managers. Our hedge funds category includes not only hedge funds but also commodity trading advisors, algorithmic trading companies, and a few active currency managers. MNCs and SMEs are commercial firms from services, manufacturing and trade industries. They primarily use foreign currencies as a medium of exchange for regular business operations related to payments, international trade activity, and investments. Our category of MNCs includes multinational enterprises, non-financial customers with specialized treasury units, and non-financial firms with over million euros in annual sales. Our category of SMEs includes firms with annual sales below million euros and private clients. 1 In the traditional OTC market our bank by definition is the price-setter when dealing with customers, but for at least a decade customers have had the option of trading with their banks in limit-order market settings such as Reuters and EBS. We exclude trades when our bank serves as prime broker for a customer who trades against other banks on limit-order trading platforms. For these trades, our bank earns a fixed fee per million of base currency but is not, in any meaningful economic sense, either the liquidity maker or the liquidity taker, so the markup would not reflect this bank s pricing strategies. 5

Our broker category includes retail foreign exchange brokers plus more traditional broker dealers. Retail brokers provide access to the foreign exchange market for private investors and institutional clients. These electronic marketplaces are regulated as exchanges. By traditional broker dealers we refer to financial institutions engaging in any kind of brokerage activities, executing buy or sell orders for customers and receiving a commission, or dealer activities related to market making in certain financial instruments. Our customer bank category is meant to represent banks that are not active in the interdealer market. We define these as banks that are not among the top 50 dealing banks of the 2012 Euromoney survey of the foreign exchange market. Customer banks are typically smaller banks from developed economies or medium-to-large financial institutions from emerging economies. They engage in EUR/USD trading mainly for transactions and hedging needed for their own operations as well as to provide liquidity to customers in their small, niche markets. Unlike major dealing banks, customer banks do not employ dealers devoted exclusively to trading euro-dollar. In our data the markup is calculated as the difference between the customer s price and a representative interbank bid or ask quote (as appropriate) at the time of the trade. The customer s quoted price is provided by the bank s salesperson in one of two ways. For a trade carried out by traditional approaches like the telephone, the salesperson consults the interbank trader for a representative interdealer price and then chooses the most appropriate markup for that particular trade. In choosing a representative quote, the interbank dealer could take one directly from an interbank brokerage or provide a synthesis of different interbank prices available at that time. Alternatively, the markup could be programmed into an automated quoting algorithm. In this setting the computer is programmed to pick the representative interbank quote, and the 6

appropriate markup is determined by either the salesperson or a dedicated team of financial engineers. In either the human or the automated settings, markups can vary according to properties of the trade, such as its size; they can vary according to properties of the market, such as current volatility; and they can vary by properties of the customer, such as average trading activity. Table 1 provides basic descriptive statistics. Average markups are highest for SMEs; their mean markup is 22.7 pips. (A pip or price improvement point is $0.0001 in eurodollar.) MNCs pay far smaller markups: their mean is 3.09 pips. Markups for real-money funds are a bit smaller than those for MNCs, with a mean of 2.6 pips. This confirms the banks oftrepeated assertion that large, sophisticated corporate clients are treated more like financial customers than like smaller corporate firms. Markups for hedge funds, brokers, and customer banks all average less than 0.5 pips. II. COMPETING HYPOTHESES The primary goal of this paper is to distinguish among four competing hypotheses regarding the determinants of the cost of forex liquidity. This section discusses the variables we include to identify each hypothesis. Most of these variables describe individual agents that trade with the bank. This information is potentially relevant because much of the bank s foreign exchange trades are done over the counter, so the bank knows the identity of the customer when providing quotes in all trades examined in this paper. Information on customer identity, quotes and trades are private information and not shared with other banks. This means that our bank may take into account all the hypotheses discussed above when quoting. In addition to order processing costs and volume discounts they may consider strategic dealing and adverse selection. 7

A. Order processing costs Many of the costs incurred by dealing rooms are fixed. These include the costs of providing the required physical infrastructure, the costs of providing the information and information processing infrastructure including Reuters news feeds and trading software; and the compensation of traders and related staff. A large trade can cover a given fixed cost with a smaller proportionate spread, so operating costs contribute a negative relation between markups and trade size. We include trade size as our first independent variable, measured as the absolute value of the traded amount measured in euros. We experimented with many functional forms for this variable. Econometric performance is clearly superior when trade size is partitioned into four segments at the familiar benchmarks of 1 million, 5 million, and 25 million. 1 million is the minimum trade size on the interdealer market; 5 million is the upper range for most interdealer trades; and 25 million is the level at which customer trades are typically handled with human intervention. Though fixed operating costs are relevant in any trading operation, the relation between markups and trade size need not be negative. It could be positive, according to Easley and O Hara (1987), who show that informed traders have an incentive to trade larger amounts. In this case adverse selection concerns would rise with trade size, and if these concerns dominate markups will rise with trade size. Bertsimas and Lo (1998) show that agents trading large amounts could rationally break trades into smaller quantities, and this has been standard practice in the foreign exchange market for decades and it is now programmed into dedicated execution algorithms. In this case the information embedded in a trade could have a non-monotonic relation to trade size. If the associated adverse selection concerns dominate markups could have a non-monotonic relation with trade size. 8

B. Volume discounts Dealers have multiple incentives to set smaller spreads for customers that trade with them the most. The literature on currency markets shows that order flow moves exchange rates, so dealers gain from knowing how much is flowing through the market and in which direction. Volume discounts encourage high-volume customers to trade through their bank instead of through their competitors. Volume discounts may also be a way to avoid losing customers. Volume discounts are common throughout economic life. Customers could expect volume discounts in their foreign exchange trading and choose to trade elsewhere if a bank does not offer them. To capture the possibility of volume discounts we calculate both the number of trades and the median trade size. The product of these would give a customer s total trading volume, which could be included on its own. However, our results indicate that trade frequency and trade size contribute independently to the cost of liquidity. To the extent that the dealer provides volume discounts, these variables should be negatively related to markups. Table 1 shows that SMEs trade least frequently (mean 70 trades per customer), while brokers trade by far the most frequently (mean 15,180 trades per customer). With mean trade size of about 0.02 sm, SMEs and brokers are both at the same end of the trade-size spectrum. MNCs have the largest mean trade size, 2.38 mn. C. Asymmetric information The foreign exchange market s two-tiered structure enables dealers to benefit from knowing the direction in which their informed customers are trading. They may thus narrow spreads to the best-informed customers to attract more of their deal flow. Since this possibility is directly contrary to the prediction of adverse-selection theory, we pause to provide an example. 9

Suppose a Soros fund is buying, and Soros funds are generally considered wellinformed. Not knowing the fund s trade direction, the dealer quotes a 2-pip spread, 1.3004 1.3006, and the fund buys at 1.3006. Foreign exchange dealers usually keep their inventory close to zero, so the dealer now has a short position. In the simple one-tier securities markets familiar from standard theory, the dealer must hold this short position until another customer arrives. If the market rises, as implicitly predicted by the Soros funds trade, the dealer loses money. Standard theory suggests dealers will quote wider spreads on trades with informed customers to compensate from such adverse-selection induced losses. In two-tier markets like foreign exchange, where dealers not only trade with customers but also trade actively with each other, our dealer need not hold the short position but can quickly and cheaply unload it in the interbank market (Naik et al., 1999). Suppose the current interbank quotes show a 4-pip spread, at 1.3003 1.3007. The dealer could unload the long position, buying at 1.3007, and take a 1-pip loss per million in his capacity as market maker. This could still be advantageous if he avoids a larger loss from holding the short position while the market rises. As shown by Naik et al. (1999), a good dealer in an active two-tier market can do even better. Foreign exchange dealers at major banks are expected to make money not simply by providing liquidity but also by taking informed speculative positions. These positions are usually held only a few minutes (Bjonnes et al. 2011), though overnight positions are not uncommon. In our example, a good dealer would buy the amount required to eliminate his short position and then buy more. The dealer s strategy would be to hold this long position and then unload it in the interbank market once the market rises as implicitly predicted by the trade by Soros fund. On balance, good dealers can ensure that the profits from taking speculative positions- more than 10

compensate for their losses in making markets for informed customers. Such dealers thus have an incentive to encourage informed customers to trade with them and may rationally set narrower spreads to such customers. In short, asymmetric information could affect spreads either positively, under adverse selection, or negatively, under strategic dealing. Strategic can be expected to apply for customer trades in two-tier markets; indeed, the concept was originally introduced using a model of a twotier market. Adverse selection, by contrast, could be expected to apply to interbank trades. The concept was originally identified in the context of one-tier markets, meaning markets where after accumulating an inventory position the dealer does not have available any counterparties who can take over the inventory quickly, inexpensively, and anonymously. Consistent with this prediction, the existing evidence suggests strategic dealing dominates for customer trades (Osler et al., 2011) and adverse selection dominates for interdealer trades (Osler and Simon, 2011). To identify the influence of asymmetric information we include a standard measure of the extent to which a customer is informed, his/her average post-trade return. An informed customer will, on average, buy before the market rises and sell before the market falls. Since the informed customer earns positive returns in both cases, this measure will be positive. For customers with zero information this measure will be zero; for mis-informed customers this measure could be negative, as is typically found for retail customers in foreign exchange (Heimer and Simon, 2014; Heimer, 2014) and equities (Barber and Odean, 2000). We calculate post-trade returns using midquotes from Reuters Dealing 3000 a highly liquid interbank limit-order market. This dataset of tick-by-tick bid and ask quotes includes eight million transactions. We calculate the post-trade return as the difference between the midquote at the time of the trade and the midquote a certain amount of time later, signed to reflect whether 11

the trade was a purchase or a sale. In robustness tests we examine shorter time horizons, which may be appropriate since dealers own speculative positions are usually closed by the end of a given trading day. Previous research, supported by anecdotal evidence from dealers, suggests that hedge funds and banks might be best informed while real-money funds and SMEs might be least informed. The averages for post-trade returns do not follow this pattern. This could reflect data-related difficulties including the relatively short horizon of our sample. D. Market sophistication Not all customers are equally familiar with the way a given market functions. Some are deeply knowledgeable about what to expect in terms of normal spreads for a given time of day or given day of the week, how spreads vary according to market volatility and other market conditions, and how hard they might have to negotiate to get a better price. Now that trading is fragmented across different trading venues a sophisticated trader will also be familiar with all the venues. The market knowledge of a sophisticated customer helps them get better prices when negotiating with their dealers. One might say, equivalently, some customers lack of market savvy enables dealers to extract wider spreads. The possibility that customers gain negotiating leverage from market sophistication, was originally noted in Green et al. (2007), who refer to it as the market power hypothesis. We test this hypothesis by exploiting our dataset s information on execution methods. We divide the venues into five categories that could potentially differ in terms of market sophistication. For each client we calculate the share of trades handled through each category. Venue 1. Direct trading. The least sophisticated trading channel, the telephone, is also the most traditional, having been in use since the telephone was invented. These trades are manually 12

entered by bank employees into the bank record-keeping system. More recent innovations in technology that operate similarly include email, fax, and direct sales through bank branches. Venue 2. Single-bank platforms and voice brokers. When trading among themselves, banks have for decades relied on voice brokers, such as Carl Kliem and BCG. Voice brokers lost substantial market share after electronic interbank brokerages emerged in the early 1990s, but remain important for trading around the 4:00 pm London fix. These trading venues evidently require more trader sophistication than trading by telephone or fax. Customers gained access to more sophisticated trading approaches in the late 1990s, when banks created trading platforms through which their customers could interact with them electronically. These single-bank platforms come in varieties tailored to the needs of specific customers. Some customers are content to simply list the amounts they would like traded at daily fixing prices. Other customers demand click-and-deal trading on a graphical user interface with executable streaming prices. Single-bank platforms are attractive to many customers because they permit straight-through processing (STP), meaning the electronic interface automatically takes care of every aspect of the trade life-cycle: initiation, confirmation, netting, settlement and reconciliation. The bid and ask quotes available to customers through single-bank platforms are set as a markup relative to the interdealer market. The markup, which is not directly revealed to the customers, is programmed by the salespeople directly into the firm s electronic execution algorithms. As such, the markups can vary by customer and according to market conditions just like markups decided on-the-spot by humans. Venue 3. Multibank platforms. Relatively sophisticated traders also trade via platforms that include multiple dealing banks, of which there are two types. Request for quote (RFQ) platforms 13

allow customers to request quotes on a given amount from multiple dealer banks simultaneously. Request-for-quote platforms are most popular with real-money funds and hedge funds. The other multibank platforms are essentially electronic limit-order markets. Electronic limit-order markets like Reuters dealing and EBS have long dominated the interbank market, and some hedge funds now have been granted access to them. Meanwhile, other multibank platforms have been created tailored to specific groups of customers. Currenex and Hotspot are favoured by hedge funds and CTAs; FX Connect is favoured by real-money funds; FXAll and 360T are favoured by corporate customers. Venues 4 & 5. Application Programming Interface (API) connections used by brokers (Venue 4) and other traders (Venue 5). An API enables the customer to connect their programmed trading systems directly to the dealers trading station. We can identify customer trading through an API for all trades executed through the bank s single bank platform. API connections provide greater trading efficiency but involve substantial set-up costs. Thus they are most popular among customers who trade frequently, most notably retail brokers, hedge funds, and customer banks, since they can spread those fixed costs across many trades. The technological sophistication of API connections is not necessarily matched by the market sophistication of the customers involved. The individual traders at retail brokerages generally have little market sophistication, as indicated by their consistent record of trading losses (Heimer and Simon, 2014). Retail aggregators that simply pass every customer trade directly onto the bank (a business model known as no-dealing-desk ) will therefore not appear to be informed. Other retail aggregators, however, manage their positions actively and could potentially be quite sophisticated. Most hedge funds are also considered quite sophisticated by dealers. We are un able to identify which brokers follow the no-dealing-desk model so we group 14

all brokers together and consider their API reliance (Venue 5) as a separate venue from the API reliance of other traders bank counterparties (Venue 4). Table 1 shows that direct trading, the least sophisticated trading venue, is used most heavily by SMEs and also by MNCs. Our bank s single-bank platform and voice/electronic brokerages are used most heavily by customer banks and as a secondary venue by SMEs and hedge funds. Multibank platforms are used most heavily by real-money managers, hedge funds, and MNCs. API connections are used most heavily by brokers and hedge funds. We pause to consider why the data indicate multiple venues for each customer category. There are a variety of reasons why individual customers might rationally trade on multiple venues. Hedge funds may trade across venues to exploit arbitrage opportunities. Customers needing to trade large amounts may wish to access liquidity from every available source. The data also probably reflect variation across customers. Within a given customer category sophistication will vary. Some SMEs might rely exclusively on single-bank platforms while others rely exclusively on direct trading. In this case our shares will be non-zero for both venues. III. METHODOLOGY AND MAIN FINDINGS To disentangle the many determinants of the price of liquidity in currency markets we apply a relatively straightforward methodology. We regress each trade s markup, Markup t, on a set of explanatory variables that permit identification among the relevant hypotheses. Standard errors are allowed to cluster by day. Markup t = α + βsz t + γntrds c + δmdnsz c + πinfo c + k k k µ Ven c + x x x θ + ε t. (1) Time t Size t is the (absolute) size of trade t; as noted above we allow four separate size categories to have different effects on markups. NTrds ct is the number of trades by customer c in 15

our sample; MdnSz ct is the median trade size of customer c; Info ct is customer c s average posttrade return; k Ven ct, k [1,4], are the shares of customer c s trades that are handled in venue groups 1, 2, 3, and 4, respectively (Venue 5 is excluded to avoid collinearity). As discussed earlier, fixed order-processing costs suggest a negative influence of Size t while adverse selection might suggest a positive influence; volume discounts suggest a negative coefficient on NTrds ct and/or MdnSz ct ; the coefficient on Info ct would be positive under adverse selection and negative under strategic dealing. The market sophistication hypothesis suggests that the coefficients on trading venues should decline for venues used by customers with more market sophistication; we take the API as used by non-broker counterparties as the excluded category. To control for other influences on spreads such as volatility and trading volume we also include time-of-day dummies and the interbank spread at the time of a trade. Our methodology for evaluating spread determinants differs markedly from the methodology most common in the literature. There it is standard to regress each trade s price on functions of the current and lagged trade direction to extract inventory and adverse-selection components of the spread (Huang and Stoll, 1997; Leach and Madhavan, 1993; Glosten and Harris, 1988). The standard approach is often adopted when the data present trades rather than quotes, but our data provide direct error-free measures of the true markup. In addition, our approach allows us to go beyond inventory and adverse-selection and identify the influence of customer properties such as trading activity, information, and market sophistication. A. First test We first examine spread determinants in a sample that is directly comparable to those used in research that originally identified the puzzle negative relation between markups and the extent to which a customer is informed (Osler et al. 2011; Reitz et al. 2012). Those earlier 16

samples included corporate and financial customers but they excluded retail aggregators and trades with other banks. After restricting the data accordingly our sample includes 12,692 observations. As shown in Table 2, when we apply this sample to regression equation (1) the adjusted R 2 is 0.55, indicating high explanatory power. More importantly, the results indicate that three of the four key factors we investigate are important determinants of markups in currency markets: fixed operating costs, volume discounts, and market sophistication. We review the implications of these results for all four factors in turn. The relevance of fixed order-processing costs is indicated by consistently negative and significant coefficients on trade size. The effect of an increase in trade size on markups declines sharply with trade size and beyond 25 mn the coefficient is no longer statistically significant, but for trades below 5 mn the influence of this factor is substantial. If trade size rises from the average for SMEs, 0.2 mn, to the average for MNCs, 2.38 mn, the markup is estimated to fall by 11.6 pips, which exceeds half the difference in average markups between these two customer categories. Since SMEs and brokers make roughly equal-sized trades, on average, this variable cannot explain why brokers pay spreads that are tiny (0.01 pip, on average) while SMEs pay wide spreads (22.74 pips, on average). The difference in spreads paid by brokers and SMEs can be explained by our second key factor, a customer s trading volume. The negative coefficients on both trading activity variables indicate that dealers provide volume discounts and that they are sensitive to a customer s average trade size as well as how frequently a customer trades. If the number of trades rises from 70, the average for SMEs, to 15,180, the average number for brokers, the markup is estimated to decline by 16.7 pips, or roughly three-quarters of the difference in average markups between these two customer groups. 17

These regression results provide no evidence that the third key potential determinant of spreads, asymmetric information, is important. However, would not be surprising if adverse selection matters for some counterparties and strategic dealing matters for others, as hypothesized above. Later regressions provide strong evidence for this mixed influence. The regression results do provide support for our fourth key potential determinant of markups, a customer s market sophistication. This is most apparent with respect to direct trading, the least sophisticated trading venue. Its large positive coefficient indicates that a rise in the frequency with which a customer relies on direct trading brings a big increase in average markups. If the direct-trading share rises from 2.0%, the hedge fund average, to 68.6%, the SME average, the markup is estimated to widen by 14.9 pips. This represents roughly two-thirds of the 20-pip difference in average markups between these customers. Clients that rely on single-bank platforms are quoted far lower spreads than clients that rely on direct trading, and clients that rely on multibank platforms pay spreads that are even a bit lower. B. Full-sample tests The results so far indicate that three of the four key potential determinants of currency spreads are indeed important: fixed operating costs, a customer s average trading volume with the bank, and a customer s market sophistication. We next expand the sample to include customer banks, retail aggregators, and other brokers. The sample size grows by many multiples, reaching 255,386 trades, due primarily to the brokers heavy trading. As shown in Table 2, the regression results continue to provide strong evidence for the influence of these same three factors: fixed operating costs, volume discounts, and market sophistication. The magnitudes of these effects change, nonetheless: the economic effects of trade size and trading activity are much smaller while the economic effects of market 18

sophistication are larger. The results continue to suggest that asymmetric information is unimportant for currency spreads but, as noted above, this could emerge if is primarily concerned about adverse selection for some counterparties and primarily concerned with strategic dealing for other counterparties. We examine this possibility more closely in Section III.D. C. Customer and venue direct effects Our baseline regression implicitly assumes that the markup will be the same for customers with a given set of characteristics (trading activity, etc.), regardless of venue and customer type that is, regardless of whether it is a hedge fund or an SME and regardless of whether that trade is taking place via the telephone or multi-bank platform. Since this could be questioned, we introduce dummies for customer type and venue. The results of this regression, shown in Table 3, indicate that dealers do generalize across customer types and venue types. Nonetheless, the three factors identified previously as important for spreads fixed operating costs, customer trading activity, and customer market sophistication are still important. D. A closer look at information effects Our results so far confirm the importance of fixed operating costs, customer trading activity, and customer market sophistication, but they suggest that asymmetric information is not important. This results is not necessarily reliable, however, since it could emerge naturally if information has mixed effects across customer groups. We look more closely at the potential influence of asymmetric information by interacting our measure of a customer s information with customer-group dummies. The results, reported in Table 4, show that information is in fact very important and that its influence is indeed dependent on the nature of the counterparty. 19

For customer banks, the coefficient on information is positive and significant, indicating that our dealing bank behaves according to adverse selection when trading with other banks. The effect of information on a bank s markup is economically significant, as well..a one standard deviation rise in a bank s information variable raises its predicted markup by 0.16 pips, or roughly two-thirds the 0.26-pip average markup for customer banks. For hedge funds, whom dealers generally view as their best-informed customers, dealers appear to use strategic dealing given the negative and significant coefficient on their information. This effect, too, is economically meaningful. A one standard deviation decline in a hedge fund s information raises its predicted markup by 0.21 pips. This is comparable in absolute magnitude to the effect of a one-standard-deviation rise in bank information, but seven times the 0.03 pip average hedge-fund markup. Information does not appear to matter for all other counterparty categories, specifically real-money funds, MNCs, SMEs, and brokers. While this might seem surprising, it could be logical if dealers consider such agents as generally uninformed about short-horizon returns. If so, the dealers would view variation in 5-minute post-trade returns as just random noise. It is, in fact, a common view among dealers that hedge funds are the best informed customers. The dealers view, in turn, is consistent with the incentives these agents face in with respect to currency trading. Hedge-fund managers tend to trade at short horizons and they participate handsomely in any profit they earn for their underlying investors. Real-money fund managers generally fact weaker incentives to find profitable investments; in addition, they tend to devote little time and effort to forecasting exchange rates prior to investing. This could even be rational, since the major exchange rates closely approximate a random walk and exchange-rate analysts are very expensive. Real-side corporations generally use currencies as a medium of exchange and so do 20

not hold foreign currency balances for long. Since they can benefit little from forecasting exchange rates they have little incentive to do so and there is little reason to expect their trades to carry much information. Dealers consistently assert that some MNCs are informed, generally those with their own active trading operations, but not all do so. The other MNCs, like other realside firms, have little incentive to invest in gathering information about exchange rates. Turning finally to brokers, trades from no-dealing-desk retail brokerages, which definitely represent a significant portion of our sample, are unlikely to be informed given the well-documented lack of information among retail traders (Heimer and Simon, 2014; Heimer 2014). The results presented in this section indicate that all four of the potential determinants of markups we investigate are important: fixed costs, volume discounts, asymmetric information, and market sophistication. We finish our analysis by verifying the robustness of these findings. IV. ROBUSTNESS TESTS This section verifies the robustness of our results for different approaches to measuring key variables and different subsamples. We use the regression equation presented of Column 1 of Table 3, in which information effects are allowed to differ across customer types, as our baseline. We first examine whether all four of our key factors remain significant if we change the time horizon over which we take returns in measuring a customer s information. In the baseline regressions of Table 3 this time horizon is five minutes, so in our alternative we examine a time horizon of thirty minutes. As shown in Table 4, this regression confirms the relevance of all four factors: order processing costs, volume discounts, and a customer s market sophistication. The information coefficients continue to show strategic dealing with respect to hedge funds and now suggest strategic dealing with respect to brokers, as well. This could 21

potentially reflect the inclusion of brokers that actively manage their positions rather than passing them on automatically to the bank. The results no longer show adverse selection with respect to customer banks. This could reflect the fact that banks usually trade over horizons that are extremely short, such as a few minutes (Bjønnes and Rime, 2005). We next examine whether our conclusions are sensitive to the exclusion of counterparty credit risk from the list of potentially important factors. Since the data do not include risk measures we use the country risk for the trade initiator s country taken from the Economist Intelligence Unit in 2012. It is not clear this variable will properly capture the relevant credit risk, since country risk is just one source of credit risk and most counterparties in our sample come from developed countries for which credit risk is low. When included by itself, country risk has a significantly negative coefficient. This seems counterintuitive because it suggests that the riskiest counterparties pay the narrowest spreads. Upon closer analysis it appears that this coefficient reflects an entirely different phenomenon: the bank s ability to set wider markups for customers from the bank s country of origin. Since this country has an AAA rating, the wider markups are associated with a low level of country risk. When a dummy for the bank s country of origin is added to the regression, country risk itself is no longer significant and the dummy has a significant positive coefficient of 5.0. This suggests that the bank s market power is substantial: after controlling for other determinants of the cost of liquidity, firms from the bank s country of origin pay an extra 5 pips on average per trade. We conclude these robustness tests by examining whether all four of our key factors are important when we consider only trades priced directly in real time by the dealers. This human approach, though the norm as recently as 2000, is now fairly rare: the relevant subsample includes only 3,035 trades. The results suggest that the same forces are at work when humans 22

price each trade as when trades are priced on an automated platform: Markups are inversely related to trade size, suggesting the importance of order-processing costs. Markups are also negatively related to a customer s trading frequency and median trade size, consistent with the existence of volume discounts. Markups are once again higher for customers who tend to trade in the least sophisticated trading venues, providing further evidence for the market sophistication hypothesis. The one major difference that emerges when we focus on human trades is this: asymmetric information appears more influential. The information coefficients remain significant negative for hedge funds and significantly positive for customer banks, but their magnitude rises substantially. The information coefficients remain statistically insignificant for real-money funds and SMEs, but not for brokers and MNCs. Instead, the information coefficients for these customers now suggest that dealers, when they set prices for these agents actively in real time, intentionally seek to maximize the trades of those that are best informed. Indeed, the information coefficient for broker trades is far larger (in absolute value) than the coefficient for hedge funds, and the information for MNCs is roughly the same size. Since retail brokers generally trade via the API, they are likely excluded from this sample. The negative coefficients on broker and MNC information suggests that these agents choose to involve humans when they are best informed. This might be when a broker trades a large amount for a hedge fund that it represents and wants the dealer to manage the trade, or when an MNC needs to purchase a massive amount of foreign currency in order to purchase a foreign firm. Though this last regression largely supports the same set of hypotheses examined earlier, it nonetheless tells us something more. Specifically, it appears that human dealer(s) at this bank react differently to customer features in-the-moment than when programming markup functions 23

into the bank s automated interfaces. The dealer is less sensitive, it appears, to customer sophistication: human-set markups vary less by trading venue than automated markups. The dealer is more sensitive, however, to other customer features. The coefficient on trade size jumps two orders of magnitude relative to our more comprehensive sample, for example, and is no longer larger for MNCs. Time-of-day effects also jump an order of magnitude. Trade size becomes important for all customer categories, and especially so for brokers and customer banks. IV. CONCLUSION This paper runs a horse race among a number of potential determinants of customer spreads on foreign exchange trades. The first is fixed order-processing costs, which provide an incentive for dealers to set narrower spreads on larger trades. The second is a customer s trading volume: dealers might provide volume discounts to the most active customers. The third is information: when contacted by an informed customer a dealer could set a wider spread if he is concerned about adverse selection, or he could set a narrower spread if he wishes to attract gather market intelligence by trading frequently with informed customers. The literature suggests that adverse selection could dominate when dealers trade with other banks while strategic dealing could dominate in trades with end-users, The four potential determinant is a customer s market sophistication, which many enable customers to negotiate narrower spreads. We exploit a new, highly disaggregated transactions dataset from a top-20 dealing bank which allows us to calculate, for each trade, variables that can potentially identify the influence of all four determinants. Fixed order-processing costs are captured by the trade s absolute magnitude. Volume discounts are captured using each customer s number of trades and the average trade size. The extent to which a customer is informed is captured by the customer s average signed post-trade returns, a common measure of information in the microstructure 24

literature. Finally a customer s market sophistication is captured by the extent to which the customer relies on trading venues of varying complexity. We find clear evidence that all four of the potential determinants are important. The cost of liquidity is inversely related to trade size, consistent with the influence of fixed trading costs. The cost of liquidity is likewise inversely related to a customer s trading volume, consistent with the volume discount hypothesis. the cost of liquidity is inversely related to the extent to which a hedge fund is informed, consistent with strategic dealing, and positively related to the extent which other counterparty banks are informed, consistent with adverse selection. Finally, the cost of liquidity is inversely related to market sophistication, consistent with Green et al. (2007) s market power hypothesis. Robustness tests confirm these general findings. Among these the most interesting is our analysis of how pricing differs between electronic pricing algorithms and prices set by humans in real time. The price of liquidity for end-users is far more sensitive to a customer s information when humans are involved in real time. This could indicate that end-users are more likely to rely on human trading when they are most informed. 25

REFERENCES Bank for International Settlements, 2013. Triennial Central Bank Survey, Foreign Exchange and Derivatives Market Activity in 2013. B.I.S., Basel. Barber, B., Odean, T. 2000. Trading is hazardous to your wealth: The common stock investment performance of individual investors, Journal of Finance 55, 773-806. Bernhardt, D., Hughson, E., 2002. Intraday trade in dealership markets. European Economic Review 46, 1697 1732. Bertsimas, Dimitris, and Andrew W. Lo (1998), Optimal control of execution costs, Journal of Financial Markets 1: 1-50. Bjønnes, G.H., Rime, D., 2005. Dealer behavior and trading systems in foreign exchange markets. Journal of Financial Economics 75, 571 605. Bjønnes, G.H., Rime, D., SolheimAa, H.O., 2005. Liquidity provision in the overnight foreign exchange market. Journal of International Money and Finance 24, 175 196. Bjønnes, G.H., Osler, C., Rime, D., 2011. Asymmetric Information in the Interbank Foreign Exchange Market. Brandeis University Working Paper. Ding,L., 2009."Bid-ask spread and order size in the foreign exchange market: an empirical investigation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 98-105 Easley, D., O Hara, M. 1987. Price, trade size, and information in securities markets. Journal of Financial Economics 19, 69-90. Evans, M., Lyons, R.K., 2002. Order flow and exchange rate dynamics. Journal of Political Economy 110, 170 180. Glosten, L., and L. Harris, 1988. Estimating the components of the bid-ask spread. Journal of Financial Economics 21: 123-142. Glosten, L., Milgrom, P., 1985. Bid, ask, and transaction prices in a specialist market with heterogeneously informed agents. Journal of Financial Economics 14, 71. Green, R.C., Hollifield, B., Schurhoff, N., 2007. Financial intermediation and the costs of trading in an opaque market. Review of Financial Studies 20, 275 314. Heimer, R., Simon, D. 2014. Facebook Finance: How Social Interaction Propagates Active Investing, presented at American Finance Association Annual Meetings, June. Heimer, R., 2014. Can leverage constraints make overconfident investors better off? presented at Eastern Finance Association Annual Meetings, March. Huang, Roger D. and Hans R. Stoll, 1997, The components of the bid-ask spread: A general approach, Review of Financial Studies 10, 995-1034. Ito, T., Hashimoto, Y., 2006. Intraday seasonality in activities of the foreign exchange markets: evidence from the Electronic Broking System. Journal of Japanese International Economics 20, 637 664. 26

Leach, J. Chris and Ananth N. Madhavan, 1993, Price experimentation and security market structure, Review of Financial Studies 6, 375-404. Lyons, R.K., 1995. Tests of microstructural hypotheses in the foreign exchange market. Journal of Financial Economics 39, 321 351. Menkhoff, L., Osler, C., Schmeling, M., 2010. Limit-order submission strategies under asymmetric information. Journal of Banking and Finance 24, 2665 2677. Naik, N.Y., Neuberger, A., Viswanathan, S., 1999. Trade disclosure regulation in markets with negotiated trades. Review of Financial Studies 12, 873 900. Osler, Carol, Alexander Mende, and Lukas Menkhoff, 2011. Price discovery in currency markets. Journal of International Money and Finance 30, 1696 1718. Payne, R., 2003. Informed trade in spot foreign exchange markets: an empirical investigation. Journal of International Economics 61, 307 329. Peterson, M.A., Sirri, E.R., 2003. Order preferencing and market quality on U.S. equity exchanges. Review of Financial Studies 16, 385 415. Ramadorai, T., 2008. What determines transaction costs in foreign exchange markets. International Journal of Finance and Economics 13, 14 25. Stefan Reitz, Markus A. Schmidt, and Mark P. Taylor, 2012. Financial Intermediation and the Role of Price Discrimination in a Two-Tier Market, Kiel Working Papers 1794, Kiel Institute for the World Economy. Rime, D., Sarno, L., Sojli, E., 2009. Exchange-rate forecasting, order flow, and macroeconomic information. Journal of International Economics 80, 72 88. 27

Table 1: Descriptive statistics. Table shows summary statistics for six counterparty groups. Data include all market-making EUR-USD trades by a top-20 forex dealing bank with non-dealing-bank counterparties during the 68 trading days from 2 January through 20 April, 2012. Total Real Hedge Customer MNC SME Brokers Money Funds Banks N. observations 257,241 1,185 6,624 1,261 3,773 170,668 73,730 Markup (pips: $/ ) Mean Max 0.44 325 2.59 96 0.03 15.7 3.09 130 22.74 104 0.01 53 0.26 325 N. Trades ('000) Trade Size ( Mns) Post-trade return 24 hours (pips) Post-trade 5 min return (pips) Direct Trading Share (%) Single-Bank Platform Share (%) Multi-Bank Platform Share (%) API-Brokers Share (%) API-Not-Brokers Share (%) (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) Mean Max (Standard Dev.) (3.96) 10.40 41.11 (14.05) 0.37 170.0 (1.09) -0.72 155.7 (5.81) -0.64 23.0 (0.53) 1.87 (12.46) 29.61 (44.43) 27.15 (42.71) 39.13 (48.05) 2.24 (14.52) (11.59) 0.05 0.16 (0.06) 1.74.1 (6.21) 0.63 137.2 (27.04) -0.04 20.6 (2.10) 13.80 (32.04) 32.83 (44.68) 53.18 (47.95) 0.00 0.0 (0.0) 0.19 4.84 (0.93) (0.48) 0.76 1.681 (0.57) 1.11 20.0 (1.29) -0.88 36.4 5.75-0.35 5.1 (0.41) 1.97 (12.32) 1.17 (10.07) 47.00 (47.68) 0.00 0.0 (0.0) 49.86 (48.15) (10.80) 0.09 219 (0.08) 2.38 38.1 (4.79) 0.52 115.4 (20.10) -0.15 11.3 (1.84) 24.57 (36.35) 17.68 (34.03) 57.75 (47.00) 0.00 0.0 (0.0) 0.00 0.0 (0.0) (18.56) 0.07 0.39 (0.12) 0.20 25.0 (0.73) -0.70 155.8 (30.51) -0.01 23.0 (3.06) 68.56 (46.25) 29.61 (45.52) 0.91 (9.23) 0.00 0.0 (0.0) 0.93 (9.59) (48.75) 15.18 41.11 (15.13) 0.19 24.0 (0.34) -0.50 136.2 (2.00) -0.08 6.2 (0.18) 0.065 (1.78) 13.95 (33.68) 27.00 (43.02) 58.98 (47.98) 0.00 0.0 (0.0) (2.51) 1.08 4.84 (1.49) 0.68 170.0 (1.53) -1.25 125.1 (6.26) -0.05 11.5 (0.52) 2.04 (11.24) 68.57 (43.97) 26.11 (41.22) 0.00 0.0 (0.0) 3.29 (9.59) 28

Table 2. Determinants of Forex Customer Markups. Table reports results from the following regression: k k Markup t = α + βsize t + γntrds ct + δmdnsize ct + πinfo ct + µ Ven ct + ε t. k Markup t is the dealing bank s price on trade t relative to the prevailing interbank price at that second. Size t is the trade t s absolute amount in euros; we allow separate coefficients for four size categories; NTrds ct is the number of trades between the bank and customer c; MdnSize ct is customer c s median trade size; Info ct is customer c s average signed return over the 5 minutes following their trades. Ven k ct is the share of customer c s trades that took place over a given type of trading venues. All regressions include a constant, time-of-day dummies, and the interbank spread. Data include all customer trades through a top-20 foreign exchange dealing bank during the first 68 trading days of 2012. Restricted sample excludes trades with brokers and other banks. Parentheses show robust standard errors clustered by trading day. *, ** and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Restricted Full Trade Size -9.808*** -0.996*** < 1 mn (1.090) (0.0686) -2.689*** -0.437*** [ 1, 5) mn (0.232) (0.0398) -0.831*** -0.281*** [ 5, 25) mn (0.109) (0.0310) 25 mn Trading Activity NTrds ( 000) MdnSize ( mn) Info 5-min returns Venue Sharess Direct Trading Single-bank Multi-bank 0.151 (0.113) -1.108*** (0.395) -0.757*** (0.108) 0.0249 (0.0827) 0.223*** (0.00487) 0.0317*** (0.00502) -0.0182*** -0.0877*** (0.0213) -0.00815*** (0.0004) -0.320*** (0.0551) 0.113* (0.0656) 0.207*** (0.0044) 9.51e-05 (0.0006) -0.0041*** (0.0005) (0.00237) API Brokers -0.0047*** (0.0006) Adj. R 2 0.554 0.430 N. Obs 12,692 255,386 29

Table 3. Direct effects of customer-type and trading venue Table reports robustness tests based on the following regression: k k Markup ct = α+βsize t +γntrds ct +δmdnsize ct +πinfo ct + Ven ct k k k µ + κ Ven t + k c k ϑ Cust ct +ε t. Markup t is the dealing bank s price on trade t relative to the prevailing interbank price at that second. Size t is the trade t s absolute amount in euros; we allow separate coefficients for four size categories; NTrds ct is the number of trades between the bank and customer c; MdnSize ct is customer c s median trade size; Info ct is customer c s average signed return over the 5 minutes following their trades. Ven k ct is the share of customer c s trades that took place over a given type of trading venues. Ven k t is a set of dummies for the venue of trade t. Cust ct is a set of dummies for the different customer types. All regressions include a constant, time-of-day dummies, and the interbank spread. Data include all customer trades through a top- 20 foreign exchange dealing bank during the first 68 trading days of 2012. Restricted sample excludes trades with brokers and other banks. Parentheses show robust standard errors clustered by trading day. *, ** and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Customer & Venue Dummies Restricted Full Trade Size < 1 mn -7.182*** -0.420*** [ 1, 5) mn -1.890*** -0.214*** [ 5, 25) mn -0.650*** -0.170*** 25 mn 0.084-0.054*** Trading Activity NTrds ( 000) -0.338-0.003*** MdnSize ( mn) -0.505*** -0.163*** Info ct 5 min returns 0.020 0.096* Venue Shares Direct trading 0.084*** 0.075*** Single-bank -0.041-2.62E-5 Multi-bank -0.015*** -0.001** API Brokers -2.75E-4 Customer-Type Dummies Real money 1.214** 1.399*** MNC 0.152 0.736* SME 8.648*** 14.490*** Broker -0.147*** Bank -0.049 Venue Dummies Direct trade 8.335*** 4.176*** Single-bank 3.283 0.117 Multibank 0.468*** 0.069*** Adj. R 2 0.574 0.542 N Obs. 12,692 255,386 30

Table 4. A closer look at information effects Table reports robustness tests based on the following regression: Markup t = α + βsize t + γntrds ct + δmdnsize ct + πinfo ct + k k π Info Cust ct ct + k k k µ + ε t. Ven ct Markup t is the dealing bank s price on trade t relative to the prevailing interbank price at that second. Size t is the trade t s absolute amount in euros; we allow separate coefficients for four size categories; NTrds ct is the number of trades between the bank and customer c; MdnSize ct is customer c s median trade size; Info ct is customer c s average signed return over the 5 minutes following their trades. Ven k ct is the share of customer c s trades that took place over a given type of trading venues. All regressions include a constant, time-of-day dummies, and the interbank spread. Data include all customer trades through a top-20 foreign exchange dealing bank during the first 68 trading days of 2012. Restricted sample excludes trades with brokers and other banks. Parentheses show robust standard errors clustered by trading day. *, ** and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All trades Risk & GY dummy Human trades 5-min returns 30-min returns 5-min returns 5-min returns Trade Size < 1 mn -1.016*** -1.030*** -0.787*** -6.247* [ 1, 5) mn -0.435*** -0.437*** -0.362*** -2.112*** [ 5, 25) mn -0.281*** -0.278*** -0.246*** -0.652*** 25 mn -0.087*** -0.087*** -0.097*** -0.104*** Trading Activity NTrds ( 000) -0.008*** -0.007*** -0.000 0.120 MdnSize ( mn) -0.320*** -0.321*** -0.281*** -0.291*** Info ct Real-money -0.222-0.078-0.146-0.251 Hedge funds -0.551*** -0.365*** -0.495*** -1.410*** MNC 0.067 0.175* 0.321-1.177** SME 0.109 0.005 0.0796-0.264 Brokers 0.033-0.048** -0.207*** -5.374*** Customer banks 0.295*** -0.052 0.344*** 1.956*** Venue Shares Direct trading 0.209*** 0.208*** 0.175*** 0.141*** Single-bank 0.001 0.001-0.000 0.016 Multi-bank -0.003*** -0.003*** -0.004*** -0.018 API Brokers -0.004*** -0.004*** -0.005*** -0.037* Risk & Country Country risk -0.007 Country-of-origin dummy 4.998*** Adj. R 2 0.430 0.430 0.183 N Obs. 255,386 255,386 3,035 31