Trading fast and slow: Colocation and liquidityJonathan BrogaardBjörn Hagströmer Lars NordénRyan Riordan

Market Microstructure: Confronting Many Viewpoints #3

December 8th, 2014

Market

Colocated traders

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)

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

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

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)

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

Remaining agenda

Data

Descriptive statistics on colocated traders

Who upgrades

Liquidity effects

Mechanism

Data

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 group N Fast vs. Slow Event study

No colocation 80 NonColo NonColo

Basic colocation 13

ColoSlowColo

Premium colocation 1G 11

Premium colocation 10G 12 10GColo

Data

PostSept 17 –

Oct 12

PreAug 20 – Sept 14

AUG SEP OCT2012

Sep 17: Nasdaq OMX introduces Premium Colocation 10G

Proprietary data from NASDAQ OMX StockholmData on trading entity level and colocation statusStocks 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 liquidityRobustness wrt index futures and consolidated order book

Descriptive statistics: What fast traders do

What fast traders do: Total volumes

17%

2%

27%55%

Limit orders

NonColo BasicColoPremiumColo 10GColo

56%

4%

19%

22%

Trades

NonColo BasicColoPremiumColo 10GColo

What fast traders do: Quotes and trades

NonColo

BasicColo

PremiumColo

10GColo

6.05 8.17

28.98

57.39

Order to Trade Ratio

NonColo

BasicColo

PremiumColo

10GColo

0.60% 0.20%

10.70%11.70%

BBO Presence

BBO Presence: % of time which trading entities have orders posted at the best bid and offer in the limit order book

Active Trading Passive Trading0.00

1.00

2.00

3.00

4.00

NonColo Colo

Spre

ad (b

ps)

What fast traders do: Trading performanceVolume-weighted average effective spread across all trades

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. FastPanel A: Trading activity NonColo ColoNumber of trading entities 80 36Share of all limit orders 17.0% 83.0%Share of all cancellations 14.2% 85.8%Share of all trades 55.8% 44.2%Share of all SEK trading volume 58.9% 41.1%Active trades per stock-day 1944.3 1685.1

Passive trades per stock-day

2103.4 1526.0

Panel B: Trading behaviorOrder-to-Trade Ratio 6.05 41.26Liquidity Supply Ratio 52.0% 47.5%BBO Presence 0.6% 8.2%Inventory Crosses Zero 1.0 6.7

Segments of colocationBasicColo PremiumColo 10GColo

13 11 121.5% 26.9% 54.6%1.2% 24.0% 60.6%3.6% 18.9% 21.7%3.7% 17.5% 19.8%79.2 805.1 800.9

181.7 566.5 777.8

8.17 28.98 57.3969.6% 41.3% 49.3%0.2% 10.7% 11.7%0.4 6.9 9.4

Who uses Colocation? High Frequency Traders?

HFT is always Algorithmic Trading (AT) – but AT is not always HFTTypical 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?)

Are colocated traders different than other HFT classifications?

Number of accounts Trades

Hagströmer and Nordén (2013) HFT Definition

NonColo & NonHFT 53 46.4%NonColo & HFT 27 9.4%Colo & NonHFT 20 23.1%Colo & HFT 16 21.1%

Kirilenko et al. (2011) HFT Definition

NonColo & NonHFT 78 51.4%Colo & NonHFT 31 15.1%HFT* 7 33.6%

*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.

Who upgrades?

Who upgrades?Probit on all colocated trading entities: Explanatory variables measured before the upgrade

Upgrade mostly associated with market-making characteristics, not news-trading

- More likely to post quotes at the best bid and offer- Higher Order-to-trade ratios- Active trades are uninformed (no additional price impact)- Higher % of trades supply liquidity- Provide liquidity when it is more expensive

But it is not a perfect bifurcation- Still use a lot of active trades- Inventory management does not appear different

Liquidity effects

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 0.761 8.980 4.517 4.206 3.86 0.38 4.267

Post 0.822*** 9.757*** 4.405** 4.126*** 3.86 0.28*** 4.152***

Liquidity improves in the equity market before and after the upgradeUp Next: What is a good control for time series variation?

Effective spread

Price impact Realized spread

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 futuresPre 0.034 0.477 1.406 1.461 1.19 0.31 1.461Post 0.039*** 0.492 1.392 1.477 1.04 0.52 1.477

Liquidity improves in the futures market before and after the upgradeUp Next: Full difference-difference analysis

Liquidity improvement ln(ELiqit) - ln(FLiqit) = a + bPostt + gXit + qi + eit

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 -0.007 0.000 -0.011* -0.011*** -0.010 -0.006  (-0.783) (-1.596) (0.011) (-1.718) (-2.629) (-0.970) (-0.768)Volatility 14.359 10.000 0.319 36.584 38.087 33.073 20.629  (0.618) (1.043) (0.019) (1.483) (1.323) (1.125) (0.760)Stock FEs Yes Yes Yes Yes Yes Yes YesN 1200 1200 1200 1200 1200 1198 1200

0.021 0.027 0.007 0.025 0.029 0.091 0.020

Even in the full diff-in-diff specification, liquidity improvesUp Next: Is this due to migration of order flow from other exchanges?

Liquidity improvement: Consolidated order book ln(ELiqit) - ln(FLiqit) = a + bPostt + gXit + qi + eit

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 BookPost -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) -Volatility 24.735 - 6.434 37.434 40.045 34.923 -  (0.555) - (0.238) (1.222) (1.180) (1.089) -Stock FEs Yes - Yes Yes Yes Yes -N 1200 - 1200 1200 1200 1200 -

0.025 - 0.007 0.034 0.036 0.148 -               

Mechanism

Inventory ManagementOne 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  Pre 13.316 0.129Post 9.807*** 0.130

     SlowCol

o  Pre 4.830 0.074Post 4.191*** 0.070

Inventory held longer by all traders; BBO Presence changes are small

Up Next: Full difference-difference analysis

Inventory Management

 Inventory

Crosses ZeroBBO

PresencePost -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 FEs Yes Yes 

N 23483 23390 0.062 0.062

 

Full difference in difference analysis

In the full diff-in-diff specification, 10GColo are more stable market makersUp Next: Does inventory influence liquidity?

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.

Inventory and Spreads  (1) (2) (3) (4)

Aggregate Invt-1 0.001*** -0.193***   (3.503) (-5.710)

High Aggregate Invt-

1   0.194***        (5.727)    

Mean Abs(Invt-1) 0.247*** 0.027 (2.591) (0.344)

High Mean Abs(Invt-

1)     0.166***      (6.046)

Returnt-1 16.752 17.642 17.123 17.827   (1.058) (1.073) (1.064) (1.080)

Turnover 0.049 0.050 0.049 0.049   (1.009) (1.032) (1.001) (1.000)

Volatility 0.001 0.001 0.001 0.001   (0.166) (0.161) (0.167) (0.163)

 Stock Fes Yes Yes Yes Yes

 N 603423 603423 603423 603423

0.243 0.244 0.243 0.244  

10G Colos inventory influences spreads, especially when inv. is largeUp Next: Emphasize inventory constrained times

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.

How 10GColo liquidity supply depends on inventory1 minute snapshots: Inventory level and quote presenceLeaning against the wind (Menkveld and Hendershott, 2013)

1 2 3 4 5 6 7 8 9 100.2

0.25

0.3

0.35

0.4

Reduce Expand

Inventory deciles

Pres

ence

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

How 10GColo liquidity supply depends on inventory1 minute snapshots: Inventory level and quote presenceLeaning against the wind (Menkveld and Hendershott, 2013)

1 2 3 4 5 6 7 8 9 100.2

0.25

0.3

0.35

0.4

Reduce Expand Reduce post Expand Post

Inventory deciles

Pres

ence

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

Inventory Management when Constrained

 

Quote Asymmetry with

constant constraint

Quote Asymmetry with

changing constraint

Inventory Constraint

Level

10GColo    Pre 0.182 0.182 8.837Post 0.090*** 0.117*** 8.990***       SlowColo    Pre 0.059 0.059 8.988Post 0.039*** 0.038*** 9.051

Quote Asymmetry, defined as the difference between Reduce and Expand presence.

Focus on 10th decile: close to inventory constraint

Both types of Colos decrease their asymmetric quoting in the post period Up Next: Full difference-difference analysis

Inventory Management when Constrained

 

Quote Asymmetry with constant

constraint

  Quote Asymmetry with changing

constraint

  Inventory Constraint

Level  OLS WLS   OLS WLS   OLS

Post -0.022*** -0.015***   -0.023*** -0.019***   0.062*(-8.435) (-14.379)   (-11.706) (-23.585)   (1.829)

10GColo 0.119*** 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 -0.001   -  (-1.771) (-0.494)   (-1.212) (-1.184)   -

Volatility 0.000 0.000   0.000 0.000   -  (-1.020) (-0.317)   (-0.181) (-0.001)   -

Stock FEs Yes Yes   Yes Yes   YesN 9580 9580   10062 8759   1468

0.099 0.040   0.113 0.138   0.230

Full difference in difference analysis

10G Colos asymmetric quoting decreases more after the upgrade

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 withImproved market liquidity

Overall and for NonColosIs not a shift of liquidity across markets

Results suggest the improvement in liquidity is driven by fast traders’ improved inventory management

Conclusions

More Summary Stats

Who upgrades?Probit on all colocated trading entities: Explanatory variables measured before the upgradeUpgrade associated with market-making characteristics, not news-trading

Probit (1 = 10G) t-stat Marginal Effect

Number of Active Trades (1000s) 0.020 (2.23) 0.008Number of Passive Trades (1000s) -0.046 (-2.95) -0.018Liquidity Supply Ratio 7.237 (2.31) 3.012BBO presence 16.11 (2.28) 6.425Active Price Impact (bps) -0.139 (-0.50) -0.055Passive Price Impact (bps) 1.894 (2.09) 0.756Active Effective Spread (bps) 1.266 (1.51) 0.505Passive Effective Spread (bps) 1.244 (2.18) 0.496Order-to-trade ratio 0.007 (2.37) 0.003Inventory Crosses Zero 0.074 (0.972) 0.029# of trading entities (N) 29

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).

Trade - 1 if trade τ (with τ=1,…,N) is by a 10GColo entity, and 0 if by a SlowColo

- lagged returns from the index futures market multiplied by the direction of trade indicator D

Post - 1 for observations after the event and 0 otherwise.

Xτ - Lagged Volatility (the average squared one-second return), - Lagged Volume (expressed in 0.1 MSEK) - Depth at BBO (expressed in 0.1 MSEK) - Quoted Spread (basis points). - Size, the 0.1 MSEK value of the trade

Information Processing: Probit Analysis

Active Trading

Probit (1 = 10G)

Marginal Effects

Post -0.074** -0.030News 212.907*** 84.774News × Post -85.509 -34.047Lagged Volatility 5.894 2.347Lagged Volume 0.052*** 0.021Depth at BBO 0.092*** 0.037Quoted Spread 0.001 0.0005Size -0.543*** -0.216

 Stock Fixed Effects Yes

N 1,100,026Psuedo R^2 0.025

Passive Trading

Probit (1 = 10G)

Marginal Effects

0.0580*** 0.023-99.271** -39.600

-144.599*** -57.6824.048*** 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 tradesUp Next: How is inventory management changing?

Decomposing the spread into adverse selection costs and inventory costs

• Using the Sadka (2006) price impact model = direction of trade = unexpected direction of trade = signed trade volume = unexpected signed trade volume

• Applying the Kim & Murphy (2013) trade aggregation approach• Scaling to observed effective spread

Adverse Selection costs

Inventory costs