Trading fast and slow:
Colocation and liquidity
Jonathan Brogaard
Björn Hagströmer
Lars Nordén
Ryan Riordan
Market Microstructure: Confronting Many Viewpoints #3
December 8th, 2014
Market
Colocated traders
Key points of the paper
1.
Fast traders have
–
–
–
–
2.
Introduction of 10G colocation at NASDAQ OMX Stockholm
-
3.
Higher order-to-trade ratios
Higher market making presence
Better liquidity timing (better effective spreads)
Better ability to trade on short-lived information
Who is buying the fastest connectivity? (mostly market-makers)
What happens to market liquidity? (it improves)
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
Trader group
N
Fast vs. Slow
Event study
No colocation
80
NonColo
NonColo
Basic colocation
13
SlowColo
Premium colocation 1G
11
Premium colocation 10G
12
Colo
10GColo
We identify trader groups based on the colocation services they
“consume”, i.e. self selection
Allows investigation of traders from different speed segments
Data
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)
2012
AUG
SEP
OCT
Pre
Post
Aug 20 –
Sept 14
Sept 17 –
Oct 12
Sep 17: Nasdaq OMX introduces Premium Colocation 10G
Thomson Reuters Tick History (TRTH / SIRCA)
Event study on liquidity
Robustness wrt index futures and consolidated order book
Descriptive statistics: What fast traders do
What fast traders do: Total volumes
Limit orders
17%
55%
27%
Trades
2%
22%
19%
56%
4%
NonColo
BasicColo
NonColo
BasicColo
PremiumColo
10GColo
PremiumColo
10GColo
What fast traders do: Quotes and trades
57.39
10.70%
11.70%
28.98
6.05
8.17
Order to Trade Ratio
0.60%
0.20%
BBO Presence
BBO Presence: % of time which trading entities have orders posted at the
best bid and offer in the limit order book
What fast traders do: Trading performance
Volume-weighted average effective spread across all trades
NonColo
Colo
Spread (bps)
4.00
3.00
2.00
1.00
0.00
Active Trading
Passive Trading
What fast traders do
Panel A: Trading activity
Number of trading entities
Share of all limit orders
Share of all cancellations
Share of all trades
Share of all SEK trading
volume
Active trades per stock-day
Passive trades per stockday
Panel B: Trading behavior
Order-to-Trade Ratio
Liquidity Supply Ratio
BBO Presence
Inventory Crosses Zero
Slow vs. Fast
NonColo
Colo
80
36
17.0%
83.0%
14.2%
85.8%
55.8%
44.2%
58.9%
1944.3
2103.4
41.1%
1685.1
1526.0
6.05
52.0%
0.6%
1.0
41.26
47.5%
8.2%
6.7
Segments of colocation
BasicColo
PremiumColo
10GColo
13
11
12
1.5%
26.9%
54.6%
1.2%
24.0%
60.6%
3.6%
18.9%
21.7%
3.7%
17.5%
19.8%
79.2
805.1
800.9
181.7
566.5
777.8
8.17
28.98
57.39
69.6%
41.3%
49.3%
0.2%
0.4
10.7%
6.9
11.7%
9.4
Inventory Crosses Zero: the number of times a trading entity changes
between having long and short positions in a stock-day
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?)
Are colocated traders different than other HFT
classifications?
Number of
accounts
Trades
Hagströmer and Nordén (2013) HFT Definition
NonColo & NonHFT
NonColo & HFT
Colo & NonHFT
Colo & HFT
53
27
20
16
46.4%
9.4%
23.1%
21.1%
78
31
7
51.4%
15.1%
33.6%
Kirilenko et al. (2011) HFT Definition
NonColo & NonHFT
Colo & NonHFT
HFT*
*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: 𝑈𝑝𝑔𝑟𝑎𝑑𝑒𝑖 = 𝛼 + θΩ𝑖 + 𝜖𝑖
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 Depth at Quoted
BBO
0.5%
Spread
(MSEK) (MSEK)
(bps)
NASDAQ OMX
0.761
Pre
Post
8.980
0.822*** 9.757***
NonColo
Realized
Effective
Spread
Spread
(bps)
(bps)
Effective
Spread
(bps)
Price
Impact
(bps)
4.517
4.206
3.86
0.38
4.267
4.405**
4.126***
3.86
0.28***
4.152***
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
Effective spread = 𝑞𝑡 𝑝𝑡 − 𝑚𝑡 = 𝑞𝑡 𝑚𝑡+∆ − 𝑚𝑡 + 𝑞𝑡 𝑝𝑡 − 𝑚𝑡+∆
Price impact
Realized spread
Liquidity improves in the equity market before and after the upgrade
Up Next: What is a good control for time series variation?
Control group – OMX 30 futures
Depth at Depth at
BBO
0.5%
(MSEK) (MSEK)
OMXS30 index futures
0.034
0.477
Pre
0.039***
0.492
Post
Quoted
Spread
(bps)
Effective
Spread
(bps)
Price
Impact
(bps)
Realized
Spread
(bps)
NonColo
Effective
Spread
(bps)
1.406
1.392
1.461
1.477
1.19
1.04
0.31
0.52
1.461
1.477
Liquidity improves in the futures market before and after the upgrade
Up Next: Full difference-difference analysis
Liquidity improvement
ln(ELiqit) - ln(FLiqit) = a + bPostt + gXit + qi + eit
Panel B: Difference-in-Difference Analysis
Quoted
Spread
(bps)
Effective
Spread
(bps)
Price
Impact
(bps)
Realized
Spread
(bps)
NonColo
Effective
Spread
(bps)
0.055***
(3.121)
-0.007
(-1.596)
10.000
(1.043)
-0.017**
(-2.396)
0.000
(0.011)
0.319
(0.019)
-0.025***
(-6.286)
-0.011*
(-1.718)
36.584
(1.483)
0.078***
(14.850)
-0.011***
(-2.629)
38.087
(1.323)
-0.139***
(-9.121)
-0.010
(-0.970)
33.073
(1.125)
-0.033***
(-20.513)
-0.006
(-0.768)
20.629
(0.760)
Yes
1200
0.027
Yes
1200
0.007
Yes
1200
0.025
Yes
1200
0.029
Yes
1198
0.091
Yes
1200
0.020
Depth at Depth at
BBO
0.5%
(MSEK) (MSEK)
NASDAQ OMX
Post
-0.058**
(-2.363)
Turnover -0.008
(-0.783)
Volatility 14.359
(0.618)
Stock
FEs
Yes
N
1200
0.021
Adj. R2
Even in the full diff-in-diff specification, liquidity improves
Up 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
Quoted
Spread
(bps)
Effective
Spread
(bps)
Price
Impact
(bps)
Realized
Spread
(bps)
NonColo
Effective
Spread
(bps)
-
-0.016**
(-2.313)
-0.002
(-0.281)
6.434
(0.238)
-0.033***
(-8.823)
-0.009
(-1.238)
37.434
(1.222)
0.091***
(5.323)
-0.009*
(-1.684)
40.045
(1.180)
-0.166***
(-4.223)
-0.009
(-0.968)
34.923
(1.089)
-
-
Yes
1200
0.007
Yes
1200
0.034
Yes
1200
0.036
Yes
1200
0.148
-
Depth at Depth at
BBO
0.5%
(MSEK) (MSEK)
Consolidated Order Book
Post
-0.064**
(-2.424)
Turnover -0.010
(-0.688)
Volatility 24.735
(0.555)
Stock
FEs
Yes
N
1200
2
0.025
Adj. R
Mechanism
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
Pre
Post
13.316
9.807***
0.129
0.130
SlowColo
Pre
Post
4.830
4.191***
0.074
0.070
Inventory held longer by all traders; BBO Presence changes are small
Up Next: Full difference-difference analysis
Inventory Management
Full difference in difference analysis
𝑦𝑖𝑡𝑒 = 𝛽1 𝑃𝑜𝑠𝑡𝑡 + 𝛽2 10𝐺𝐶𝑜𝑙𝑜𝑒 + 𝛽3 𝑃𝑜𝑠𝑡𝑡 10𝐺𝐶𝑜𝑙𝑜𝑒 + 𝛾𝑋𝑖𝑡 + 𝜃𝑖 + 𝜖𝑖𝑡 ,
Inventory
Crosses Zero
-0.034
(-0.232)
8.767***
(27.553)
-2.978***
(-15.334)
-0.728**
(-2.472)
0.385***
(6.543)
BBO Presence
Stock FEs
Yes
Yes
N
Adj. R2
23483
0.062
23390
0.062
Post
10GColo
Post*10GColo
Turnover
Volatility
-0.004***
(-2.576)
0.052***
(12.975)
0.004***
(3.152)
0.001
(0.523)
-0.000***
(-2.939)
In the full diff-in-diff specification, 10GColo are more stable market makers
Up 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
Aggregate Invt-1
(1)
0.001***
(3.503)
High Aggregate Invt-1
(2)
-0.193***
(-5.710)
0.194***
(5.727)
Mean Abs(Invt-1)
(3)
(4)
0.247***
(2.591)
16.752
(1.058)
0.049
(1.009)
0.001
(0.166)
17.642
(1.073)
0.050
(1.032)
0.001
(0.161)
17.123
(1.064)
0.049
(1.001)
0.001
(0.167)
0.027
(0.344)
0.166***
(6.046)
17.827
(1.080)
0.049
(1.000)
0.001
(0.163)
Stock Fes
Yes
Yes
Yes
Yes
N
Adj. R2
603423
0.243
603423
0.244
603423
0.243
603423
0.244
High Mean Abs(Invt-1)
Returnt-1
Turnover
Volatility
10G Colos inventory influences spreads, especially when inv. is large
Up 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 inventory
1 minute snapshots: Inventory level and quote presence
Leaning against the wind (Menkveld and Hendershott, 2013)
Reduce = presence at the best offer (bid)
conditional on a long (short) position
0.4
Presence
0.35
0.3
Expand = presence at the best bid (offer)
conditional on a long (short) position
0.25
0.2
1
2
3
4
5
6
Inventory deciles
Reduce
Expand
7
8
9
10
How 10GColo liquidity supply depends on inventory
1 minute snapshots: Inventory level and quote presence
Leaning against the wind (Menkveld and Hendershott, 2013)
Reduce = presence at the best offer (bid)
conditional on a long (short) position
0.4
Before
Presence
0.35
0.3
After
Expand = presence at the best bid (offer)
conditional on a long (short) position
0.25
0.2
1
2
3
4
5
6
7
8
9
10
Inventory deciles
Reduce
Expand
Reduce post
Expand Post
Inventory Management when Constrained
Quote Asymmetry, defined as the difference between Reduce and
Expand presence.
Focus on 10th decile: close to inventory constraint
Quote Asymmetry
with constant
constraint
Quote Asymmetry
with changing
constraint
Inventory
Constraint
Level
10GColo
Pre
Post
0.182
0.090***
0.182
0.117***
8.837
8.990***
SlowColo
Pre
Post
0.059
0.039***
0.059
0.038***
8.988
9.051
Both types of Colos decrease their asymmetric quoting in the post period
Up Next: Full difference-difference analysis
Inventory Management when Constrained
Full difference in difference analysis
𝑦𝑖𝑡𝑒 = 𝛽1 𝑃𝑜𝑠𝑡𝑡 + 𝛽2 10𝐺𝐶𝑜𝑙𝑜𝑒 + 𝛽3 𝑃𝑜𝑠𝑡𝑡 10𝐺𝐶𝑜𝑙𝑜𝑒 + 𝛾𝑋𝑖𝑡 + 𝜃𝑖 + 𝜖𝑖𝑡 ,
Post
10GColo
Post*10GColo
Turnover
Volatility
Stock FEs
N
Adj. R2
Quote Asymmetry with
constant constraint
Quote Asymmetry with
changing constraint
OLS
-0.022***
(-8.435)
0.119***
(6.289)
-0.071***
(-5.253)
-0.001*
(-1.771)
0.000
(-1.020)
Yes
9580
0.099
OLS
-0.023***
(-11.706)
0.119***
(6.311)
-0.042***
(-12.745)
-0.001
(-1.212)
0.000
(-0.181)
Yes
10062
0.113
WLS
-0.015***
(-14.379)
0.058***
(7.584)
-0.043***
(-19.961)
0.000
(-0.494)
0.000
(-0.317)
Yes
9580
0.040
WLS
-0.019***
(-23.585)
0.058***
(7.816)
-0.046***
(-36.610)
-0.001
(-1.184)
0.000
(-0.001)
Yes
8759
0.138
10G Colos asymmetric quoting decreases more after the upgrade
Inventory
Constraint
Level
OLS
0.062*
(1.829)
-0.145***
(-5.818)
0.091***
(3.717)
Yes
1468
0.230
Conclusions
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
More Summary Stats
Who upgrades?
Probit on all colocated trading entities: 𝑈𝑝𝑔𝑟𝑎𝑑𝑒𝑖 = 𝛼 + θΩ𝑖 + 𝜖𝑖
Explanatory variables measured before the upgrade
Upgrade associated with market-making characteristics, not newstrading
Probit
(1 = 10G)
Number of Active Trades (1000s)
Number of Passive Trades (1000s)
Liquidity Supply Ratio
BBO presence
Active Price Impact (bps)
Passive Price Impact (bps)
Active Effective Spread (bps)
Passive Effective Spread (bps)
Order-to-trade ratio
Inventory Crosses Zero
# of trading entities (N)
0.020
-0.046
7.237
16.11
-0.139
1.894
1.266
1.244
0.007
0.074
29
t-stat
Marginal Effect
(2.23)
(-2.95)
(2.31)
(2.28)
(-0.50)
(2.09)
(1.51)
(2.18)
(2.37)
(0.972)
0.008
-0.018
3.012
6.425
-0.055
0.756
0.505
0.496
0.003
0.029
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).
𝑇𝑟𝑎𝑑𝑒𝜏 = 𝛽1 𝑃𝑜𝑠𝑡𝜏 + 𝛽2 𝑁𝑒𝑤𝑠𝜏 + 𝛽3 𝑃𝑜𝑠𝑡𝜏 𝑁𝑒𝑤𝑠𝜏 + 𝛾𝑋𝜏 + 𝜃𝑖 + 𝜖𝜏 ,
Trade - 1 if trade τ (with τ=1,…,N) is by a 10GColo entity, and 0 if by a SlowColo
𝑓𝑢𝑡
𝑁𝑒𝑤𝑠𝜏 = 𝑟𝜏
𝐷𝜏 - 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
Post
News
News × Post
Lagged Volatility
Lagged Volume
Depth at BBO
Quoted Spread
Size
Stock Fixed Effects
N
Psuedo R^2
Probit
(1 = 10G)
-0.074**
212.907***
-85.509
5.894
0.052***
0.092***
0.001
-0.543***
Marginal
Effects
-0.030
84.774
-34.047
2.347
0.021
0.037
0.0005
-0.216
Passive Trading
Probit
(1 = 10G)
0.0580***
-99.271**
-144.599***
4.048***
-0.005
-0.047***
-0.012***
-0.473**
Yes
Yes
1,100,026
0.025
1,264,206
0.013
Active trading on news unchanged, Passive trading avoids news trades
Up Next: How is inventory management changing?
Marginal
Effects
0.023
-39.600
-57.682
1.615
-0.002
-0.019
-0.005
-0.189
Decomposing the spread into adverse selection costs
and inventory costs
• Using the Sadka (2006) price impact model
𝐷𝜏 = direction of trade
𝜀𝛹,𝜏 = unexpected direction of trade
𝐷𝜏 = signed trade volume
𝜀𝜆,𝜏 = unexpected signed trade volume
∆𝑝𝜏 = 𝑧0 𝜀𝛹,𝜏 + 𝑧1 𝜀𝜆,𝜏 + 𝑐0 Δ𝐷𝜏 + 𝑐1 Δ𝐷𝑉𝜏 + 𝑦𝜏
1
𝐸𝑆𝑡 =
𝑇𝑡
𝑇𝑡
𝑗=1
𝐷𝑗 𝑧0 𝜀𝛹,𝑗 + 𝑧1 𝜀𝜆,𝑗 + 𝑐0 𝐷𝑗 + 𝑐1 𝐷𝑉𝑗 /𝑃𝑗
Adverse
Selection costs
Inventory
costs
• Applying the Kim & Murphy (2013) trade aggregation approach
• Scaling to observed effective spread