The Impact of Co-Location of Securities Exchanges and Traders Computer Servers on Market Liquidity

Transcription

1 The Impact of Co-Location of Securities Exchanges and Traders Computer Servers on Market Liquidity Alessandro Frino European Capital Markets CRC Vito Mollica Macquarie Graduate School of Management Robert I. Webb University of Virginia April 9, 2013 Abstract This study examines the impact of allowing traders to co-locate their servers near exchange servers on the liquidity of futures contracts traded on the Australian Securities Exchange. It provides evidence of an increase in proxies for HFT activity following the introduction of colocation including raw message traffic, order-to-trade ratios and the volume associated with message traffic to trading volume. There is strong evidence of a decrease in bid ask spreads and an increase in market depth after the introduction of co-location. We conclude that the introduction of co-location enhances liquidity. We conjecture that colocation improves the efficiency with which liquidity providers (including market maker HFTs) are able to make markets 1

2 The Impact of the Co-Location of Securities Exchanges and Traders Computer Servers on Market Liquidity 1. Introduction A number of studies document that algorithmic trading, in general, and high-frequency trading (HFT), in particular, are positively related to liquidity (e.g., Hendershott and Riordan [2009], Hendershott, Jones, and Menkveld [2011], Brogaard, [2010], Saar and Hasbrouck [2003], Hasbrouck and Saar [2012]). Not surprisingly, exchanges have modified their market structure to attract more algorithmic trading. As Hendershott, Jones and Menkveld [2011] point out, one way of doing so is by permitting algorithmic traders to co-locate their servers in the market s data center. Co-location reduces latency (the time it takes to make and execute trading decisions) and therefore enables co-located market-maker high-frequency traders to more rapidly adjust their quotes as market conditions change. It also levels the playing field among competing HFT market makers who are also co-located by ensuring that none has a latency advantage over the other from an exchange perspective. Consequently, the introduction of co-location facilities is expected to increase liquidity by encouraging HFT and increasing competition among HFT market makers. Other things equal, the introduction of co-location by an exchange should lead to greater trading volume by algorithmic traders. However, it is not immediately apparent how, if at all, the increased trading volume from algorithmic traders will affect liquidity and other dimensions of market quality. As Hendershott, Jones and Menkveld [2011] identify: But it is not at all an obvious a priori that AT [algorithmic trading] and liquidity should be positively related. If algorithms are cheaper and/or better at supplying liquidity, then AT may result in more competition in liquidity provision, thereby lowering the cost of immediacy. However, the effects could go the other way if algorithms are used mainly to demand liquidity In fact, AT could actually lead to an unproductive arms race, where liquidity suppliers and liquidity demanders both invest in better algorithms to try to take advantage of the other side, with measured liquidity the unintended victim. 2

3 The decision by the Australian Securities Exchange (herein ASX) to allow futures traders to co-locate their servers starting February 20, 2012 provides a natural experiment by which to assess how much liquidity is affected by the introduction of co-location, as well as the impact of algorithmic trading on liquidity. 1 In this paper, we test the proposition that HFT increased following the introduction of co-location on the ASX by examining whether various proxies for the amount of HFT activity increase. We also directly test whether the introduction of colocation is associated with an improvement in liquidity. This paper also extends previous research by examining futures markets where the mix of traders are different to equities markets studied in previous research examining high frequency traders. Futures markets include a substantial portion of arbitrageurs attempting to exploit price discrepancies between near and deferred contracts as well as futures and spot markets. We examine data for the four most actively traded futures contracts on the ASX before and after the introduction of co-location in order to determine the effects on liquidity of the decision to allow co-location near the exchange server. Specifically, we examine the Share Price Index (SPI); 90 day Bank Accepted Bills; 3 year Treasury bond; and, 10 year Treasury bond futures contracts. We document that trading volume, the order-to-trade ratio, message traffic, and depth increased for the three interest rate futures contracts following co-location. In contrast, trading volume, the order-to-trade ratio and message traffic fell while depth increased for the Share Price Index (SPI) contract following co-location. We argue that the puzzling behaviour of the SPI is largely explained by the introduction of a cost recovery charge (i.e., a tax) on cash market equity message traffic that raised the cost and lowered the benefits of stock index arbitrage. Consistent with Chaboud et al [2011], we do not observe any change in volatility after the introduction of co-location. This is contrary to Boehmer et al [2012], who report evidence of increased short run volatility after the introduction of co-location and Saar and Hasbrouck [2003] and Hasbrouck and Saar [2012] who report a decrease in volatility when algorithmic trading increases. The economic significance of the increase in market liquidity after co-location is assessed. 1 The CME Group introduced co-location services on January 29, Craig Mohan, Managing Director, CME Group Co-Location and Data Center Services reported that the CME Group had over 100 customers trading this way on [CME Group] markets, as well, as 13 telecom providers by late March

4 1. Institutional Detail On February 20, 2012, the ASX introduced a co-location facility for trading of all futures contracts. The facility allows futures brokers to co-locate their computer servers in the same room as the computer server which operates the futures trading system of the exchange, known as ASX Trade24. 2 The co-location facility, named the Australian Liquidity Centre, is located in a dedicated building in a Sydney suburb approximately 5 kilometres from the Central Business District of Sydney (where the head office of the ASX is located). According to ASX sources, on the first day of operation 5 futures brokers had co-located their servers in the co-location facility and approximately 20% of bid and ask orders in futures contracts were submitted through co-located servers. By September 2012 this had grown to approximately 50% of orders submitted. The ASX co-location facility, which provides cabinet space for futures brokers or their clients includes a standard power supply (of 2 kw per month) and a fibre optic cable that connects each cabinet to ASX Trade24. These cabinets are installed in the same room as the computer server that runs ASX Trade24. The length of the fibre optic cable is equal (65 metres) for each cabinet regardless of where it is physically located in the building. An order created by the server of a futures broker or their clients must travel from the cabinet of the broker down the fibre optic line to the computer server of the broker that manages the order known as the AOEI (Automated Order Entry Interface) or Gateway and then onto the matching engine (ASX Trade24). The introduction of the co-location service significantly reduced the amount of time it took for an order to travel from the computer server of a broker (previously located in the broker s office) down a dedicated line to the trading system of the exchange and back. This time lapse is known as latency. ASX estimates of this latency are approximately 2 milliseconds. While 2 The Australian Securities Exchange originated out of the merger of the Australian Stock Exchange and the Sydney Futures Exchange. The trading system of the Exchange was previously called SYCOM and is described in Frino, et.al [2007]. 4

5 the latency created by the 65 metre fibre optic line is less than 2 microseconds, the bulk of the latency originates from the Gateway and the matching engine of ASX Trade24. 3 Since the cable connecting each cabinet to the server of the Exchange is equal in length regardless of where the cabinet is located in the co-location facility, all co-located servers have equal latency. Before the introduction of the ASX co-location facility, the ASX operated its ASX Trade 24 trading system on a server housed at its headquarters in the central business district of Sydney. Each broker had a gateway in their office, and this was connected to the server which operated ASX Trade 24 via a dedicated line rented from a commercial telecommunications infrastructure supplier such as Telstra or Optus. Large brokers were located around the city of Sydney of up to approximately 5 kilometres from the ASX, and hence a message sent from their server to the exchange would have had to travel through a number of kilometres of line and through a number of networks or switches. One broker we spoke to suggested that the latency that this created could have exceeded 4-5 milliseconds, implying that the latency benefit from colocation was 2-3 milliseconds. Furthermore, the amount of latency was affected by the proximity of the brokers office to the ASX (which was costly to reduce as it required rental of office space close to the Exchange), the quality of the infrastructure in the building in which a broker was located and the quality of the infrastructure provided by the telecommunications infrastructure supplier of a broker. The cost of co-locating a server is approximately $5,000 per month. The cost of hiring a cabinet and the power supply is approximately $2,500 per month. The AOEI or gateway costs approximately $2,500 per month each and allows the entry of 12 messages per second. 4 Most brokers would have two or more AOEI s. The marginal cost to the broker from allowing one of their clients (eg. a HFT firm) to set up a cabinet is also $2,500 per month. The Australian Securities Exchange charges an exchange fee of 90 cents per side for trades in 3 Year Treasury bond, 10 year Treasury Bond 90 Day Bank Accepted Bill and SPI 200 Index 3 A millisecond is one thousandth of a second while a microsecond is one millionth of a second 4 A message includes: a new bid or ask order, cancellation or amendment of an existing order. 5

6 futures. 5 Arbitrage transactions involving SPI 200 index futures also involve transactions in stocks. Since 30 June 2010, transactions in stocks have incurred an exchange fee of 0.15 basis points per side. On August 1, 2010, the Australian government transferred the role of market supervision from the ASX to the Australian Securities and Investments Commission (herein ASIC). This decision was part of broader shift of market supervision from market operators to a streamlined single supervision and enforcement agency and the introduction of competing exchanges in Australia. A consequence of this regime shift was the introduction by the regulatory body, ASIC, to charge a fee or collect revenue from entities which it regulated so as to fund market supervision. For the period 1 January 2012 to 30 June 2013, ASIC introduced an additional fee for messages and transaction in stocks executed on ASX. 6 The objective of the additional fees were to recover $2.487 million per quarter from brokers proportionate to their share in transactions and $0.215 million per quarter proportionate to their share in message traffic which was incurred by ASIC in supervising the market. Clearly, ASICs fee per trade in stocks and fee per message in stocks could change each quarter with the volume of transactions and message traffic. The magnitude of these fees has not been disclosed publically, but is estimated it to be in the order of 3 cents per trade side for the fee component levied on the basis of the number of transaction Data & Methodology Intraday trade and quote data used in this study is sourced from Thomson Reuters Tick History (TRTH) and is managed and distributed by the Securities Industry Research Centre of Asia Pacific. Time stamped to the nearest microsecond, the data include price and volume details for each trade, and order book information for up to ten price levels. The data include qualifiers which identify open bid and ask quotes, and daily high and low prices. Our analysis considers the four most actively traded futures contracts traded on the ASX derivative 5 See ASX (2012A). 6 ASIC (2012) 7 The Annual Report of the ASX for the year ended 30 June 2012 reported that there were 165 million trades in stocks for the year, or approximately million per quarter. Given that the total fee to be recovered by ASIC each quarter was $2.487 million, then the approximate size of the fee was 3 cents per trade side. 6

7 market, including: 10 and 3 year Treasury Bonds, 90-day Bank Accepted Bills (BABS) and the Share Price Index (SPI) futures. The sample period extends August 20, 2011 to August 20, 2012, coinciding with a six month event window centred on the introduction of colocation facilities. The sample is restricted to trades in the nearest to maturity contract, during daytime trading hours. 8 To gauge the change in the extent of algorithmic trading in futures markets we adopt several proxies consistent with Hendershott et al [2011] as our data does not permit explicit identification of algorithmic traders. We consider three related measures: (1) Messages Traffic (2) Order-to-trade Ratio and (3) Algo Trade. Boehmer et al [2012] and Hendershott et al [2011] highlight message traffic can include new order submissions, modifications and or order cancelations. Order book or market depth data sourced from Thomson Reuters Tick History summaries such information. Consequently message traffic calculated for each day is the accumulation of changes in the order book for each microsecond of the trading day. We examine the order book for up to ten price levels. Order-to-trade Ratio for contract i on day t is calculated as follows: Hendershott et al [2011] suggest normalising message traffic by trading volume, consist with their approach we examine, Algo Trade calculated as follows for contract i on day t: To test the impact of the introduction of co-location on ASX24 9 we estimate the following regression for each futures contract: 8 Our results are robust to the inclusion or exclusion of dates close to expiration. Frino and McKenzie, 2002 find trades occurring within 10 days of expiration of the near contract are likely to form part of rollover strategies associated with increased activity, declining spreads, and lower market impact costs in the period before contract maturity. 9 ASX24 is the trading platform used by the ASX to trade derivative contracts. ASX Match is the system used to trading equity products. 7

8 where Colo is a dummy variable set to zero prior to the introduction of co-location facilities on February 20, 2012 and set to 1 following its introduction. includes our proxies of the degree of algorithmic trading or a series of market quality and liquidity metrics such as bidask spreads, depth, open interest, trading activity and volatility. Bid-ask spreads for each trade executed on ASX24 are calculated from prevailing best bid and ask quotes. For each trading day we calculate the average bid-ask spread in ticks and percentage bid-ask spread, which standardises the dollar tick spread by the quote midpoint. We introduce a third measure related to bid-ask spreads, Percentage Trades at Minimum Tick, providing a frequency count of trading throughout the day at the minimum tick. The minimum tick for the SPI (1 tick), BABS (0.01 percent) and 10 year Government Bonds (0.005 percent) did not change during the sample period considered. In relation to 3 year Government Bonds, the ASX mandates a minimum tick of 0.01 percent (equivalent to approximately $24), except for trading days between the 8 th of the expiry month and expiry date. During this period the minimum tick for 3 year Treasury bonds is reduced to percent. The change in the minimum price movement for 3 year Treasury notes applies to all contract months, irrespective of expiry. To avoid any confounding effects of this regime change we exclude trading days in the 3 year Government Bond futures from the 8 th of the expiry month to settlement. Our variable Depth, aggregates over the trading day the average available total depth at the best bid and ask quotes. Trade Size, measures the average daily trade parcel, while Transactions counts the number of trades. Volume measures the total size of trade for trading day t, Open Interest measures the open interest at the start of the trading day, while Volatility indicates contract s price volatility on day, proxied by the daily price movement ( ) (see Parkinson, [1980]). 8

9 The first objective of our examination is to identify the relationship between algorithmic trading and co-location. Ex-ante, it is expected the introduction of co-location should increase the quantity of algorithmic trading. However, the implementation of co-location coincides with the introduction of a charge imposed by the regulator on message traffic and trading on January 1, The cost recovery charge intended for equities and derivate markets applied solely to equity products in 2012, however its introduction may indirectly impact equity based futures contract. Arbitrage in the SPI futures contract involves trading in the underlying equity market. If arbitrage opportunities diminish as a result of the charge, this may lead to a reduction in message traffic for equity based futures contracts vis-à-vis interest rate futures contacts. To test this proposition we estimate the following regression for each futures contract i: where, is a proxy for algorithmic trading Order-to-trade Ratio or Algo Trade. Colo is a dummy variable set to zero prior to the introduction of co-location facilities on February 20, 2012 and set to 1 following its introduction. CRC is a dummy variable set to zero prior to the introduction of the Cost Recovery Charge on January 1, 2012 and set to 1 following its introduction. In this paper our principal objective is to test the impact of co-location on market liquidity. To evaluate and measure the effect of co-location we estimate the following regression for our four measures of market liquidity: 9

10 where, is proxied via, dollar tick spread, percentage tick spread, percentage of trades at the minimum tick and total depth at the best prevailing quotes. Consistent with the literature which seeks to explain the determinants of liquidity, we control for price volatility and the level of trading activity. The theory underpinning these variables is well developed in Stoll [1978] and Copeland and Galai [1983]. Price volatility measures the risk to liquidity providers per unit of time. Hence, the higher the price volatility, the greater the compensation sought by liquidity providers. To control for the level of trading activity, in the spirit of Wang, Yau and Baptiste [1997] we introduce open interest as a control for the level of liquidity. Wang, Yau and Baptiste [1997] argue changes in the information collected by derivative traders are reflected in the expected changes in physical positions. Open interest or the total number of unsettled contracts in the futures markets captures such changes. A high degree of open interest would indicate greater trading activity and liquidity in the future. The level of open interest should not be associated with the degree of algorithmic trading, who typically do not hold open positions. 3. Results We begin by examining the change in the proxies for algorithmic trading for interest rate futures following the introduction of colocation, in order to assess whether colocation increased the amount of HFT. Table 1 reports summary statistics for a number of proxies for HFT including the raw number of messages per minute, the order-to-trade ratio and Hendershots proxy for algorithmic trading. The table documents an increase in raw message traffic for all interest rate futures contracts, which is statistically significant at all conventional levels of significance. For example, for BAB futures raw message traffic increases from 7.65 messages per minute to messages per minutes, and the change in this value is significant at all conventional levels of significance. Table 1 also documents changes in message traffic which have been scaled for trading interest including the order-totrade ratio and Hendershot s proxy. On the basis of these variables, Table 1 also provides some evidence that HFT increased following the introduction of colocation. The algorithmic trading proxy increases significantly for BAB futures following the introduction of colocation, while the order-to-trade ratio increases significantly for 3 Year Bond futures and 10 Year bond futures following the introduction of colocation. 10

11 [INSERT TABLE 1] Despite the relatively strong evidence for interest rate futures which supports the proposition that HFT activity increased following the introduction of colocation, the evidence for SPI futures appears to contradict this proposition. Specifically, message traffic, the order-to-trade ratio and the Hendershott measure for HFT all decline from the pre to the post colocation period, and this decline is statistically significant at all conventional levels of significance. This is contrary to the notion that the introduction of colocation increases HFT activity. However, this is likely to be attributable to the introduction of the message traffic charge for equity markets during the post-colocation period, and which therefore confounds our analysis of the introduction of colocation. We return to this issue below. Table 1 also enables us to provide preliminary evidence on the impact of colocation on liquidity. Table 1 documents that the bid ask spread in ticks and as a percentage of the price, declines across all futures contracts (including the SPI) from the pre to the post colocation period. This decline is statistically significant 3 and 10 Year Bond futures as well as SPI futures. The table also documents that the portion of trades executed at the minimum tick increased significantly for all contracts except the BAB. We now turn to an assessment of market depth: another dimension of liquidity. Table 1 also documents that depth available at the best bid and ask increased significantly following the introduction of colocation, across all futures contracts. In aggregate, the descriptive statistics reported in Table 1 therefore provide prima facie evidence that the liquidity of futures contracts improved following the introduction of colocation. However, these results can only be regarded as preliminary, as the table documents that various determinants of liquidity which are unlikely to be related to colocation were also changing significantly from the pre to the post colocation period, including price volatility and open interest. Hence, this confirms the importance of controlling for these variables in order to properly assess the impact of colocation on liquidity. Table 2 re-examines the impact on the proxies for HFT activity, after controlling for market variables likely to have an impact on the proxies including price volatility and proxies for trading interest (open interest). The table enables us to draw similar conclusions to those drawn using raw proxies for HFT, and supports the proposition that HFT trading increased following the introduction of colocation. 11

12 [INSERT TABLE 2] Table 3 reports results of the regression analysis which enables us to assess the impact of colocation on liquidity after controlling for changes in the known determinants of liquidity from the pre to the post colocation period such as trading activity and price volatility. Consistent with previous research, the table documents a positive relationship between price volatility and the various measures of bid-ask spreads, and a negative relationship between our proxy for trading interest (open interest) and bid-ask spreads. Most importantly, an examination of the dummy variable for colocation implies that there was a decrease in bid-ask spreads across all four futures contracts examined. Panel A of Table 3 documents a significant decreases in bid ask spreads in ticks for all futures contract, except BABS which do not change significantly. The coefficient on the colocation dummy for the regressions based on the bidask spread as a portion of price is negative and statistically significant for all futures contracts. Finally, Panel C of Table 3 which reports the regression results based on the percentage of trades at the minimum tick suggests that colocation resulted in a significant decreases in bid ask spreads for trades in 10 and 3 year Treasury bond futures and SPI futures. Hence, on the balance, the results reported in this table confirm that the introduction of colocation was associated with an improvement in liquidity as measured by bid-ask spreads. [INSERT TABLE 3] Table 3 also reports the results of regression analysis based on depth. In relation to depth, Table 3 confirms that the introduction of colocation was associated with an increase in market depth across all futures contracts. These increases are significant at all conventional levels of significance. We conclude that the introduction of colocation facilities was associated with an increase in liquidity as measured by market depth. One of the interesting conclusions which can be drawn from the results above is that an increase in HFT was not essential to an improvement in liquidity following the introduction of colocation. For example, while the changes in the proxies for HFT activity for 10 Year 12

13 Bond futures are mixed (raw message traffic increases significantly, while the order-to-trade ratio and the Hendershot proxy for algorithmic trade do not change significantly following the introduction of colocation), the change in the proxies for liquidity for 10 Year Bond futures all imply that liquidity improved significantly following the introduction of colocation. Perhaps more stark is the evidence that message traffic decreased significantly while liquidity increased for SPI futures. Hence, colocation may have improved the efficiency with which HFT firms and other market participants are able to make markets and therefore resulted in an improvement in liquidity, without the introduction of any additional HFT activity. The results above also enable us to estimate the overall benefit in terms of transaction costs associated with the introduction of colocation. Specifically, based on estimates of the change in bid-ask spreads (in ticks) reported in Table 3, the size and value (in dollars) of the minimum tick and the annual contract turnover for each of the futures contracts for financial year 2012, we estimate the benefit through reduced bid-ask spreads associated with the introduction of colocation facilities. In aggregate, we estimate that the introduction of colocation was associated with a $12 million improvement in the cost of trading based on the bid ask spread alone across the four futures contracts examined in this paper Further Analysis and Robustness Test In this section we undertake several analyses to probe our conclusions and also to assess their robustness. It was earlier suggested that the apparently conflicting evidence on the impact of the introduction of colocation on HFT was likely to be related to the introduction of a message traffic charge at the beginning of January 2012 in relation to equities markets trades. This is likely to have increased the cost of HFTs which arbitrage equities and futures markets. Figure 1 below documents the ratio of message traffic to trades in the equities which underly 10 We calculate the value of the introduction of colo using the following equation: Value of minimum tick x Contract turnover in 2012 x [Coefficient on Colo Dummy (in ticks) / minimum tick]. For example for 10 Year Bond futures this is equal to 38 x 17,220,000 x / or $ 3.1 million. 13

14 SPI futures. The diagram clearly demonstrates that the order-to-trade ratio declined following the introduction of the cost recovery charge in January 2012, from approximately 10 messages per trade to approximately 8 messages per trade (or indeed 20 percent). This is similar to the decline in message traffic documented for SPI futures contracts in Table 1 which declined from the pre to the post colocation period from approximately 10.9 to 8.8 messages per trade (or by approximately 20 percent). This provides evidence to support our conjecture that the decline in message traffic for the SPI. [INSERT FIGURE 1] While our results reported earlier examines the impact of colocation on liquidity by examining a 6 month observation interval before and after the introduction of colocation, such long periods increase the probability that extraneous variables enter our analysis and confound analysis. In order to test the robustness of our results, we replicate analysis after sampling only 3 months of data before and after the introduction of the colocation facility. The results of this analysis are reported in Table 4. The results documented in Table 4 confirm that our analysis is robust to the sample period used, and support our conclusion that the introduction of colocation is associated with an increase in liquidity. [INSERT TABLE 3] The securities examined in this paper are all futures contracts, which rely on underlying assets such as stocks and bonds. Changes in the liquidity of these underlying assets may have an impact on the liquidity of the futures market. For example, the cost of hedging market maker trades in futures is likely to increase if liquidity in the underlying asset decreases, and this is therefore likely to increase the cost of making markets in futures and therefore decrease the liquidity of futures. In order to test the robustness of our conclusions to changes in the liquidity of underlying assets, we introduce trading volume in the spot market for three of the futures contracts examined in this study as a control variable in examining the association between co-location and liquidity. Specifically we examine daily trading volume in the ASX/S&P 200 index, and the basket of bonds which make up the 3 and 10 year 14

15 Government Bonds. 11 Table 5 (which replicates and extends the equation underlying Table 3) implies that the conclusions we draw from our analysis are robust to the inclusion of underlying volume in our analysis. 5. Conclusion and Suggestions for Future Research The ASX introduced co-location for ASX futures markets on February 20, We provide evidence of an increase in HFT activity following the introduction of colocation. We also provide strong evidence of a decrease in bid-ask spreads and an increase in market depth following the introduction of colocation, across all futures contracts examined. We conclude that the introduction of colocation resulted in an improvement in liquidity of futures contracts. A number of avenues for future research are possible. First, while we examine traditional measures of liquidity (bid-ask spreads and depth) in assessing the impact of colocation on liquidity, it may be possible to examine other measures including the market impact of institutional trades: data permitting. Second, while we have examined the impact of colocation on the general level of liquidity, it may be useful to examine whether these results hold around important information announcements. Finally, high frequency trading is related to the latency of the trading system and some exchanges have introduced new trading platforms that per se change latency (eg. Singapore Exchange in 2011). It would be useful to examine whether the results of this study also apply to these other microstructure changes that change the attractiveness of futures market to high frequency trading. References ASX, (2012 A), ASX Market Connectivity: Schedule of Fees, 2 April 2012 Version 2.3, ASX, (2012 B), ASX Australian Liquidity Centre : Business Services Guide, ASX CoLo _guide.pdf ASX, (2011), ASX Australian Liquidity Centre : Technical Services Guide Version 2 11 The basket of Government bonds is published by the ASX as each interest rate futures contract is listed. We are unable to source underlying volume for the BABS. 15

16 de.pdf Boehmer, E., Fong, K. Y. L., and Wu, J., International Evidence on Algorithmic Trading (March 14, 2012). Available at SSRN: or Brogaard, J., Hendershott, T., and Riordan, R., High Frequency Trading and Price Discovery (July 30th, 2012). Available at SSRN: or Budimir, M., and Schweickert, U., Latency in Electronic Securities Trading - A Proposal for Systematic Measurement. Journal of Trading, Vol. 4, No. 3, pp Available at SSRN: Chaboud, A., Hjalmarsson, E., Vega, C., and Chiquoine, B., Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market (June 13, 2011). FRB International Finance Discussion Paper No Available at SSRN: or Easley, D., Lopez de Prado, M., and O'Hara, M., The Volume Clock: Insights into the High Frequency Paradigm (March 30, 2012). The Journal of Portfolio Management, (Fall, 2012) Forthcoming ; Johnson School Research Paper Series No Available at SSRN: or Foucault, T., Kadan, O., and Kandel, E., Liquidity Cycles and Make/Take Fees in Electronic Markets (March 14, 2012). Journal of Finance, Forthcoming; EFA 2009 Bergen Meetings Paper. Available at SSRN: or Frino, A., Kruk, J., and Lepone, A., 2007 'Transactions in Futures Markets: Informed or Uninformed?', The Journal of Futures Markets, vol.27:12, pp Frino, A. and McKenzie, M., 2002 'The pricing stock index futures spreads at contract expiration', Journal of Futures Markets, vol.22:5, pp Hasbrouck, J., and Saar, G., Low-Latency Trading (July 27, 2012). AFA 2012 Chicago Meetings Paper; Johnson School Research Paper Series No Available at SSRN: or Hendershott, T., Jones, C. M. and Menkveld, A. J., Does Algorithmic Trading Improve Liquidity? (August 30, 2010). Journal of Finance, Vol. 66, pp. 1-33; WFA 2008 Paper. Available at SSRN: Hoffmann, P., A Dynamic Limit Order Market with Fast and Slow Traders (July 2012). Available at SSRN: or 16

17 Labuszewski, J.W., and Aldinger, L., Twenty Years of CME Globex, June 21, 2012, Menkveld, A.J., High Frequency Trading and the New-Market Makers (February 6, 2012). EFA 2011 Paper; AFA 2012 Paper; EFA 2011 Paper. Available at SSRN: or Robin, P., and J. Greene, The Competitive Landscape for Global Exchanges: What Exchanges Must Do to Meet User Expectations, Cisco Internet Business Solutions Group, July Wang, G.H.K., Yau, J. and Baptiste, T., 1997 Trading Volume and Transaction Costs in Futures Markets Journal of Futures Markets, Vol 17:7; pp

18 Table 1 Liquidity and Market Quality Measures Centred around the introduction of Colocation This table reports summary statistics of market quality pre and post the introduction of co-location facilities on the Australian Securities Exchange. To test the impact of co-location on market quality measures we estimate the following specification for each futures contract i where is a dummy variable set to zero prior to the introduction of co-location facilities on February 20, 2012 and 1 thereafter. Y it is the relevant dependent variable, bid-ask spreads, depth, messages per minute. The analysis is based on daily observation and considers a 12 month event window. */** denote significance at the 95%/99% level. Tick Spread % Tick Spread % Trades at Minimum Tick Depth Messages per Minute Order-totrade Ratio Algo Trade Trade Size Transactions Volume Open Interest Volatility 10 Year Government Bonds Pre Post Difference t-stat Year Government Bonds Pre Post Difference t-stat Day Bank Accepted Bills Pre Post Difference t-stat Share Price Index Pre Post Difference t-stat

19 Table 2 Message Traffic Taxes and Algorithmic Trading This table presents regression analysis of the impact of the introduction of co-location facilities on algorithmic trading behaviour. The following specification is estimated: where AT it is the relevant algorithmic trading proxy reported in Panels A,B and C. Panel A reports results for our first proxy, Message Traffic per Minute. Results for Order-to-trade Ratio measured as the number of messages divided by number of transactions are reported in. Panel C reports results for Algo Trade (volume) measured as (-1xVolume/100)/Message Traffic. Our regression specification controls for daily open interest, volatility and a dummy variable CRC, which is set to zero prior to the introduction of the Cost Recovery Charge imposed by the regulator ASIC on January 1, 2012, and set to 1 following its introduction. The CRC dummy I only included for the SPI contact as the CRC was imposed only for equities markets. Dummy variable, Colo is set to zero prior to the introduction of colocation facilities on February 20, 2012, and set to 1 following introduction of co-location. The event window extends six months around the introduction of co-location. t-statistics are reported in parenthesis Variable 10 Year Government Bonds 3 Year Government Bonds Bank Accepted Bills Share Price Index Messages Intercept Open Interest Volatility Order-to-trade Ratio Intercept Open Interest Volatility Algo Trade (Volume) Intercept Open Interest Volatility

20 Table 3 The impact of Co-location on Market Liquidity This table presents regression analysis of the impact of the introduction of colocation facilities on market liquidity. where, is proxied via, Dollar Tick Spread, Percentage Tick Spread, Percentage of Trades at the Minimum Tick and Depth Included in the estimated specification is a dummy variable, Colo, which is set to zero prior to the introduction of co-location facilities on February 20, 2012, and set to 1 following co-location. The event window extends six months around the introduction of co-location. t-statistics are reported in parenthesis Variable 10 Year Government Bonds 3 Year Government Bonds Bank Accepted Bills Share Price Index Bid-Ask Spread (ticks) Intercept Open Interest Volatility Bid-Ask Spread (percent) Intercept Open Interest Volatility Trades at Minimum Tick (percent) Intercept Open Interest Volatility Depth Intercept Open Interest Volatility

21 21

22 Figure 1 Algorithmic Trading in ASX Equity Securities 22

23 Table 4 Replication of with 3-month event window This table presents results of robustness tests of the impact of colocation on market liquidity. We shorten the event window considered in the estimation of the following specification: where, is proxied via, Dollar Tick Spread, Percentage Tick Spread, Percentage of Trades at the Minimum Tick and Depth. The event window extends three months around the introduction of co-location Variable 10 Year Government Bonds 3 Year Government Bonds Bid-Ask Spread (ticks) Bank Accepted Bills Share Price Index Bid-Ask Spread (percent) Trades at Minimum Tick (percent) Depth

24 Table 5 Inclusion of Underlying Trade Volume as Robustness Test This table presents results of robustness tests of the impact of colocation on market liquidity. Trading volume in the underlying instrument is included as a control variable in the following specification: where, is proxied via, Dollar Tick Spread, Percentage Tick Spread, Percentage of Trades at the Minimum Tick and Depth. The event window extends six months around the introduction of co-location. t- statistics are reported in parenthesis Variable Share Price Index Bid-Ask Spread (ticks) 3 Year Government Bonds 10 Year Government Bonds Bid-Ask Spread (percent) Trades at Minimum Tick (percent) Depth

25 20/08/ /09/ /10/ /11/ /12/ /01/ /02/ /03/ /04/ /05/ /06/ /07/ /08/ Messages per Minute in Futures Contracts 10 Year Government Bonds 3 Year Government Bonds Bank Accepted Bills Share Price Index (scaled by 10) 25