1. Trading fast and slow: Colocation and liquidity Jonathan Brogaard Björn Hagströmer Lars Nordén Ryan Riordan Market Microstructure: Confronting Many Viewpoints #3 December 8 th, 2014

3. Market Colocated traders

4. Key points of the paper 1.Fast traders have –Higher order-to-trade ratios –Higher market making presence –Better liquidity timing (better effective spreads) –Better ability to trade on short-lived information 2.Introduction of 10G colocation at NASDAQ OMX Stockholm -Who is buying the fastest connectivity? (mostly market-makers) -What happens to market liquidity? (it improves) 3.What is driving the liquidity improvement? –Market-makers avoiding being adversely selected –Inventory management (relaxed inventory constraint)

5. Adverse Selection Hypothesis Fast traders have a short-term informational advantage Fast traders trade actively on news  adversely select traders who do not have time to revise stale quotes (Biais, Foucault & Moinas, 2014; Cartea & Penalva, 2012; Foucault, Hombert & Rosu, 2013; Martinez and Rosu, 2013) News traders get faster  Adverse selection costs increase Fast liquidity providers use speed to avoid being picked off (Jovanovic & Menkveld, 2012; Hoffman, 2014; Aït-Sahalia and Saglam, 2014) Market makers get faster  Adverse selection costs decrease

6. Inventory Hypothesis  Aït-Sahalia & Saglam, 2014: The inventory constraint of market makers depends on the accuracy of the signal on future trade flows Faster market makers have better control of their inventory, as they can cancel quotes quickly when inventory builds up Market makers get faster  Inventory costs decrease

7. Current empirical evidence on trading speed Empirical studies on colocation events find improved liquidity but increased volatility (Boehmer, Fong & Wu, 2012; Frino, Mollica & Webb, 2013) Studies of AT/HFT show: Informed (Brogaard et al. 2013, Hendershott and Riordan 2009) Supply liquidity (Menkveld 2013, Malinova et al. 2013) … Empirical studies on trading system upgrades find mixed results Positive effects: Boehmer, Fong, and Wu (2014); Frino, Mollica, and Webb (2014); Riordan & Storkenmaier (2012) Negative effects: Hendershott & Moulton (2011); Gai, Yao & Ye (2013); Menkveld & Zoican (2013)

8. How is this paper different than other papers? Previous papers classify traders by Exchange-defined HFT flag (Hagströmer and Norden, 2013; Brogaard et al., 2013) Trading behaviour (Kirilenko et al., 2011; Hasbrouck and Saar, 2013; Malinova et al., 2013) We identify groups based on the exchange services (colocation) they “consume”, i.e. self selection We study the behaviour & impact of these colocated/fast traders (basic, 1G, and 10G) that results from being fast

9. Remaining agenda Data Descriptive statistics on colocated traders Who upgrades Liquidity effects Mechanism

10. Data

11. Colocation history and trader classification Feb 8, 2010: INET introduced  Basic colocation Mar 14, 2011: Premium Colocation 1G introduced as add-on to Basic Sep 17, 2012: Premium Colocation 10G introduced We identify trader groups based on the colocation services they “consume”, i.e. self selection Allows investigation of traders from different speed segments Trader groupNFast vs. SlowEvent study No colocation80NonColo Basic colocation13 Colo SlowColo Premium colocation 1G11 Premium colocation 10G1210GColo

12. Data Post Sept 17 – Oct 12 Pre Aug 20 – Sept 14 AUG SEP OCT 2012 Sep 17: Nasdaq OMX introduces Premium Colocation 10G Proprietary data from NASDAQ OMX Stockholm Data on trading entity level and colocation status Stocks in the OMX S30 index (30 largest stocks in Sweden) NASDAQ OMX order books (no MTFs) Thomson Reuters Tick History (TRTH / SIRCA) Event study on liquidity Robustness wrt index futures and consolidated order book

13. Descriptive statistics: What fast traders do

14. What fast traders do: Total volumes

15. What fast traders do: Quotes and trades BBO Presence: % of time which trading entities have orders posted at the best bid and offer in the limit order book

16. What fast traders do: Trading performance Volume-weighted average effective spread across all trades

17. What fast traders do Inventory Crosses Zero: the number of times a trading entity changes between having long and short positions in a stock-day Slow vs. Fast Panel A: Trading activityNonColoColo Number of trading entities8036 Share of all limit orders17.0%83.0% Share of all cancellations14.2%85.8% Share of all trades55.8%44.2% Share of all SEK trading volume58.9%41.1% Active trades per stock-day 1944.31685.1 Passive trades per stock- day 2103.41526.0 Panel B: Trading behavior Order-to-Trade Ratio 6.0541.26 Liquidity Supply Ratio 52.0%47.5% BBO Presence 0.6%8.2% Inventory Crosses Zero 1.06.7 Segments of colocation BasicColoPremiumColo10GColo 131112 1.5%26.9%54.6% 1.2%24.0%60.6% 3.6%18.9%21.7% 3.7%17.5%19.8% 79.2805.1800.9 181.7566.5777.8 8.1728.9857.39 69.6%41.3%49.3% 0.2%10.7%11.7% 0.46.99.4

18. Who uses Colocation? High Frequency Traders? HFT is always Algorithmic Trading (AT) – but AT is not always HFT Typical properties of HFT: –Fast turnover –Low Intraday inventory –End the day neutral –High Volume (SEC, 34-61358, Concept Release on Equity Market Structure) HFT is a mixture of the use of technology and trading strategies (do they differ?)

19. Are colocated traders different than other HFT classifications? Number of accounts Trades Hagströmer and Nordén (2013) HFT Definition NonColo & NonHFT5346.4% NonColo & HFT279.4% Colo & NonHFT2023.1% Colo & HFT1621.1% Kirilenko et al. (2011) HFT Definition NonColo & NonHFT7851.4% Colo & NonHFT3115.1% HFT*733.6% *Due to the small number of firms in this HFT category, we are unable to disclose their distribution across NonColo and Colo accounts. This is to comply with the NASDAQ OMX policies on participant confidentiality.

20. Who upgrades?

22. Liquidity effects

23. What happens to liquidity? Depth at BBO - the average MSEK volume posted at the BBO Depth at 0.5% - the MSEK trade volume required to change the price at all and by 0.5% Quoted spread –half the difference between the best offer and best bid price scaled by the spread midpoint Effective spread – the difference between the trade price and the spread midpoint prevailing prior to trade NonColo Effective Spread - the same measure conditional on a NonColo trader being involved in the trade Depth at BBO (MSEK) Depth at 0.5% (MSEK) Quoted Spread (bps) Effective Spread (bps) Price Impact (bps) Realized Spread (bps) NonColo Effective Spread (bps) NASDAQ OMX Pre 0.7618.9804.5174.2063.860.384.267 Post 0.822***9.757***4.405**4.126***3.860.28***4.152*** Liquidity improves in the equity market before and after the upgrade Up Next: What is a good control for time series variation? Price impactRealized spread

24. Control group – OMX 30 futures Depth at BBO (MSEK) Depth at 0.5% (MSEK) Quoted Spread (bps) Effective Spread (bps) Price Impact (bps) Realized Spread (bps) NonColo Effective Spread (bps) OMXS30 index futures Pre 0.0340.4771.4061.461 1.190.31 1.461 Post 0.039***0.4921.3921.477 1.040.52 1.477 Liquidity improves in the futures market before and after the upgrade Up Next: Full difference-difference analysis

25. Liquidity improvement ln(ELiq it ) - ln(FLiq it ) =  +  Post t +  X it +  i +  it Panel B: Difference-in-Difference Analysis Depth at BBO (MSEK) Depth at 0.5% (MSEK) Quoted Spread (bps) Effective Spread (bps) Price Impact (bps) Realized Spread (bps) NonColo Effective Spread (bps) NASDAQ OMX Post -0.058**0.055***-0.017**-0.025***0.078***-0.139***-0.033*** (-2.363)(3.121)(-2.396)(-6.286)(14.850)(-9.121)(-20.513) Turnover-0.008-0.0070.000-0.011*-0.011***-0.010-0.006 (-0.783)(-1.596)(0.011)(-1.718)(-2.629)(-0.970)(-0.768) Volatility14.35910.0000.31936.58438.08733.07320.629 (0.618)(1.043)(0.019)(1.483)(1.323)(1.125)(0.760) Stock FEsYes N1200 11981200 0.0210.0270.0070.0250.0290.0910.020 Even in the full diff-in-diff specification, liquidity improves Up Next: Is this due to migration of order flow from other exchanges?

26. Liquidity improvement: Consolidated order book ln(ELiq it ) - ln(FLiq it ) =  +  Post t +  X it +  i +  it Panel B: Difference-in-Difference Analysis Depth at BBO (MSEK) Depth at 0.5% (MSEK) Quoted Spread (bps) Effective Spread (bps) Price Impact (bps) Realized Spread (bps) NonColo Effective Spread (bps) Consolidated Order Book Post-0.064**--0.016**-0.033***0.091***-0.166***- (-2.424)-(-2.313)(-8.823)(5.323)(-4.223)- Turnover-0.010--0.002-0.009-0.009*-0.009- (-0.688)-(-0.281)(-1.238)(-1.684)(-0.968)- Volatility24.735-6.43437.43440.04534.923- (0.555)-(0.238)(1.222)(1.180)(1.089)- Stock FEsYes- - N1200- - 0.025-0.0070.0340.0360.148-

27. Mechanism

28. Inventory Management One channel through which speed may influence liquidity is inventory costs. To better understand the effect of trading speed on inventory management consider how 10GColos change their inventory management behavior after upgrading. Focus on Inventory crosses zero and BBO Presence Inventory Crosses Zero BBO Presence 10GColo Pre13.3160.129 Post9.807***0.130 SlowColo Pre4.8300.074 Post4.191***0.070 Inventory held longer by all traders; BBO Presence changes are small Up Next: Full difference-difference analysis

29. Inventory Management Inventory Crosses Zero BBO Presence Post-0.034-0.004*** (-0.232)(-2.576) 10GColo 8.767*** 0.052*** (27.553)(12.975) Post*10GColo-2.978*** 0.004*** (-15.334)(3.152) Turnover-0.728** 0.001 (-2.472)(0.523) Volatility 0.385***-0.000*** (6.543)(-2.939) Stock FEsYes N 23483 23390 0.062 Full difference in difference analysis In the full diff-in-diff specification, 10GColo are more stable market makers Up Next: Does inventory influence liquidity?

30. Inventory and Spreads How is inventory management related to market liquidity? Comerton-Forde et al. (2010) find strong evidence showing a positive link between market-maker inventory and spreads. To show such a link for our dataset and 10GColos we perform an intraday version of their analysis.

31. Inventory and Spreads (1)(2)(3) (4) Aggregate Inv t-1 0.001***-0.193*** (3.503)(-5.710) High Aggregate Inv t-1 0.194*** (5.727) Mean Abs(Inv t-1 ) 0.247*** 0.027 (2.591)(0.344) High Mean Abs(Inv t-1 ) 0.166*** (6.046) Return t-1 16.75217.64217.12317.827 (1.058)(1.073)(1.064)(1.080) Turnover 0.049 0.050 0.049 (1.009)(1.032)(1.001)(1.000) Volatility 0.001 (0.166)(0.161)(0.167)(0.163) Stock FesYes N603423 0.243 0.244 0.243 0.244 10G Colos inventory influences spreads, especially when inv. is large Up Next: Emphasize inventory constrained times

32. Inventory Management when Constrained Aït-Sahalia and Saglam (2014): fast market makers submit two-sided quotes when their inventories are within an upper and lower bound. –When inventory is outside the bounds, in contrast, they only submit quotes on the opposite side of their inventory position. A related strategy for inventory-constrained market makers is to post orders asymmetrically around the current midpoint quote, in order to adjust the execution probabilities (known as leaning against the wind). We formulate a test of the asymmetric quoting effect by studying presence at the best bid and offer prices separately and conditional on the inventory of the individual trading entity. Inventory - the number of shares accumulated in that stock-day up to the time of each minute-by-minute randomized snapshot used in the BBO Presence When a trading entity has a long position, a quote at the best bid implies a chance of expanding the position, while a limit order posted at the best offer price represents a chance of reducing the position.

33. How 10GColo liquidity supply depends on inventory 1 minute snapshots: Inventory level and quote presence Leaning against the wind (Menkveld and Hendershott, 2013) Expand = presence at the best bid (offer) conditional on a long (short) position Reduce = presence at the best offer (bid) conditional on a long (short) position

34. How 10GColo liquidity supply depends on inventory 1 minute snapshots: Inventory level and quote presence Leaning against the wind (Menkveld and Hendershott, 2013) Expand = presence at the best bid (offer) conditional on a long (short) position Reduce = presence at the best offer (bid) conditional on a long (short) position Before After

35. Inventory Management when Constrained Quote Asymmetry with constant constraint Quote Asymmetry with changing constraint Inventory Constraint Level 10GColo Pre 0.182 8.837 Post 0.090***0.117***8.990*** SlowColo Pre 0.059 8.988 Post 0.039***0.038***9.051 Quote Asymmetry, defined as the difference between Reduce and Expand presence. Focus on 10 th decile: close to inventory constraint Both types of Colos decrease their asymmetric quoting in the post period Up Next: Full difference-difference analysis

36. Inventory Management when Constrained Quote Asymmetry with constant constraint Quote Asymmetry with changing constraint Inventory Constraint Level OLSWLS OLSWLS OLS Post-0.022***-0.015*** -0.023***-0.019*** 0.062* (-8.435)(-14.379) (-11.706)(-23.585) (1.829) 10GColo0.119***0.058*** 0.119***0.058*** -0.145*** (6.289)(7.584) (6.311)(7.816) (-5.818) Post*10GColo -0.071***-0.043*** -0.042***-0.046*** 0.091*** (-5.253)(-19.961) (-12.745)(-36.610) (3.717) Turnover-0.001*0.000 -0.001 - (-1.771)(-0.494) (-1.212)(-1.184) - Volatility0.000 - (-1.020)(-0.317) (-0.181)(-0.001) - Stock FEsYes N9580 100628759 1468 0.0990.040 0.1130.138 0.230 Full difference in difference analysis 10G Colos asymmetric quoting decreases more after the upgrade

37. We provide new insightful summary statistics for colocated firms –Higher order-to-trade ratios –Higher market making presence –Better liquidity timing (better effective spreads) –Better ability to trade on short-lived information The colocation upgrade is associated with Improved market liquidity Overall and for NonColos Is not a shift of liquidity across markets Results suggest the improvement in liquidity is driven by fast traders’ improved inventory management Conclusions

39. More Summary Stats

40. Who upgrades? Probit (1 = 10G) t-statMarginal Effect Number of Active Trades (1000s)0.020(2.23)0.008 Number of Passive Trades (1000s)-0.046(-2.95)-0.018 Liquidity Supply Ratio7.237(2.31)3.012 BBO presence16.11(2.28)6.425 Active Price Impact (bps)-0.139(-0.50)-0.055 Passive Price Impact (bps)1.894(2.09)0.756 Active Effective Spread (bps)1.266(1.51)0.505 Passive Effective Spread (bps)1.244(2.18)0.496 Order-to-trade ratio0.007(2.37)0.003 Inventory Crosses Zero0.074(0.972)0.029 # of trading entities (N)29

41. Information Processing To understand how speed influences adverse selection costs we evaluate how 10GColos react to news We specify a probit regression to investigate whether those who upgrade impose more adverse selection costs on other traders in their active trading or do they use their speed to avoid being picked off in their passive trading (or both).

42. Information Processing: Probit Analysis Active Trading Probit (1 = 10G) Marginal Effects Post-0.074**-0.030 News212.907***84.774 News × Post-85.509-34.047 Lagged Volatility5.8942.347 Lagged Volume0.052***0.021 Depth at BBO0.092***0.037 Quoted Spread0.0010.0005 Size-0.543***-0.216 Stock Fixed EffectsYes N1,100,026 Psuedo R^20.025 Passive Trading Probit (1 = 10G) Marginal Effects 0.0580***0.023 -99.271**-39.600 -144.599***-57.682 4.048***1.615 -0.005-0.002 -0.047***-0.019 -0.012***-0.005 -0.473**-0.189 Yes 1,264,206 0.013 Active trading on news unchanged, Passive trading avoids news trades Up Next: How is inventory management changing?

43. Decomposing the spread into adverse selection costs and inventory costs Adverse Selection costs Inventory costs