Competition between trading platforms and market liquidity:

effects of 'lit' and 'dark' trading

based on

The Impact of Dark Trading and Visible Fragmentation

on Market Quality

By

Hans Degryse(KU Leuven, Tilburg University and CEPR)

Frank de Jong(Tilburg University)

and

Vincent van Kervel(VU Amsterdam)

Seminar “DG MARKT's conference cycle on financial market issues”

January 22, 2013

Motivation

• Longstanding question in financial markets: how should trading be organized?

– Is fragmentation of trading over several trading venues beneficial or

harmful for liquidity?

• “visible” fragmentation: fragmentation across transparent ‘lit’ books (limit

order books)

• “dark” trading: not pre-trade transparent (crossing networks, dark pools, over-

the-counter markets)

– If so, for which type of traders?

• Traders with frictionless access to all markets?

• Traders with “restricted access” due to frictions or best-execution policies2

Motivation

• Recent changes in European financial regulation (MiFID in November 2007)

offer unique experiment to address these longstanding questions

– Allow for entry of new trading venues, both “visible” and “dark”

• We address regulatory issues of fair markets and retail investor protection.

Best-execution policies differ across US and EU: consider the impact for three

definitions of liquidity

• “consolidated liquidity”: smart order routers who connect to all venues

• “best-market liquidity”: investors that can pick the most liquid market but do not split across

venues

• “local liquidity”: incumbent (traditional) market only

3

Preview of Results

• “Visible” fragmentation

– improves consolidated liquidity up to 30% relative to a concentrated market

• Effect is strongest for liquidity close to the midpoint

– reduces local liquidity by 6 to 10% relative to a concentrated market

– Best-market liquidity is largely unaffected

• Dark trading lowers liquidity: one standard deviation increase in dark trading lowers the consolidated liquidity by 9%

4

Dark trading, visible fragmentation and liquidity

• Theory:– Consolidation and liquidity:

• Avoids duplication of fixed setup costs• Network externalities (e.g., Pagano QJE 1989): ‘liquidity begets liquidity’

– Why (visible) fragmentation? Impact on liquidity• Upside:

– Fosters innovation (e.g. Stoll, JEP2006)– Serves heterogeneous clientele improving liquidity (e.g., Hendershott and Mendelsohn (JF 2000);

Degryse, Van Achter and Wuyts, JFE2009))– Competition lowers order processing costs (e.g., Biais, Bisière and Spatt, ManSc2010)– Competition deepens markets (Foucault and Menkveld (JF2008))

• Downside: – Informed traders can lever up their trades (Dennert REStud 1993))

– Dark trading in competition with visible trading venues• Attracts new traders to markets improving risk sharing: e.g., Hendershott and

Mendelsohn (JF 2000); Degryse, Van Achter and Wuyts, (JFE2009)

• Dark pool may ‘cream-skim’ uninformed traders and negatively affect liquidity in visible market: execution probability of informed traders is low on dark pools (Zhu (2011))

• Dark pool may deteriorate price discovery as informed traders faces two types of costs: price impact on the exchange and execution probability impact on the crossing network (Ye (2012))

• impact of dark trading depends on liquidity (Buti, Rindi and Werner (2012)) 5

Dark trading, visible fragmentation and liquidity

• Empirics:

– Visible fragmentation• Entry of new lit venues improves liquidity at NASDAQ (Weston JF2000)

• Competition between LSE and Euronext in 2004 for Dutch shares improves liquidity of incumbent venue (Foucault and Menkveld, JF 2008)

– Overall fragmentation:• In the US, more fragmented stocks have lower transaction costs and higher

execution speeds (O‘Hara and Ye, JFE2011)

– Dark trading:• more dark trading reduces liquidity in US markets (Weaver (2011), Nimalendran and

Ray (2012))

• Dark trading improves liquidity in the cross-section but not in the time series (Buti, Rindi, and Werner (2012))

– MiFID: studies look at

• introduction of one system only (e.g. Chlistalla and Lutat, 2009; Hengelbrock and Theissen, 2010))

• datasets with limited information only (best quotes) (Gresse, 2012)

– No study splits impact of visible fragmentation and dark trading, and considerstheir impact jointly => this is our contribution

6

Some institutional background

• Market in Financial Instruments Directive (MiFID): Implemented in November

2007. Three key ingredients

1) New entry allowed: three types of trading venues

• Regulated Markets

• Multilateral Trading Facilities

• Systematic Internalisers

2) Best-execution obligation:

• US : price (trade-through prohibition) <-> EU: many dimensions

=> in EU brokers implement best-execution often by selecting ‘incumbent’

exchange only

3) Uniform transparency requirements with some waivers

(reference price , negotiated price, large-in-scale, order management facility)

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Markets are fragmented

•

8

Dataset

9

Source: http://fragmentation.fidessa.com

Dataset

• Thomson Reuters Tick History database

• AEX Large and Mid cap firms, 52 stocks 2006 -2009

– All trading venues with visible order books

• e.g. Euronext, Deutsche Boerse, Chi-X, Turquoise, Bats, Nasdaq OMX, Virt-X

• Quotes: 10 best price levels at each side of the order book on each trading venue

• Trades

– Dark trading: ‘Dark pools, OTC, internalization’ :trading volume for a particular stock on a particularday

10

Time line AEX Large and Midcap

stocks•

11

Liquidity Measure: Depth (X)

•

12

Liquidity Measure: Depth (20)

•

13

Liquidity Measure: Depth (40)

•

14

Consolidated liquidity: consolidating order books (1)

•

15

•

16

Consolidated liquidity: consolidating order books (2)

•

17

Consolidated liquidity: consolidating order books (3)

18

Best-Market Liquidity: order book with highest liquidity

illustration

Local liquidity: incumbent’s order book (Euronext)

•

19

Visible Fragmentation

(trading venues with transparent limit order books (‘lit’ books))

• Measure visible fragmentation through 1- HHI

with i=stock, t=day, and v= trading venue

20

Dark Trading

• Darkit

= percentage of “dark trading” in total

trading of stock i on day t.

• Dark trading = “OTC, dark pools, broker-

crossing networks”

21

Descriptives – visible fragmentation

•

22

Descriptives – dark trading

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Empirical model and identification strategy

• Daily observations, T=1022, N=52 stocks

– Control for factors known to impact market quality: Log of Share Price, Market Cap, Trading Volume, Stock volatility and Algorithmic trading

– Identification strategy:• Quarter*firm dummies => within firm, within quarter variation

of visible fragmentation and dark trading– Colliard and Foucault (RFS 2012) and Degryse, Van Achter and

Wuyts (2012): entry of new trading venues often goes together withother fee changes or post–trading fee changes

• Instrumental Variables as in O’Hara and Ye (JFE2011):– IV with as instruments (i) Ratio limit orders to trades at visible

competitors, (ii) Average daily order size visible competitors, (iii) Average daily Dark order size , and their respective squares 24

Results for visible fragmentation: consolidated liquidity

• Consolidated liquidity gains most by fragmentation for depth close to the

midpoint (impact for ln depth (10)>(20)>(30)>(40))

• Depth(10) may increase with about 30% relative to concentrated market

• Depth(40) may increase with about 12% relative to concentrated market

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-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Ln Depth(10) Ln Depth(20)Ln Depth(30) Ln Depth(40)

Consolidated

Results for visible fragmentation: local liquidity

• Local liquidity in general is harmed by greater fragmentation

• Depth may drop by about 6 to 10% at VisFrag=0.4 relative to

concentrated market

• Investor with best-execution policy restricted to local market

may be harmed26

-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Depth(10) Depth(20)Depth(30) Depth(40)

Local

Results for visible fragmentation: best-market liquidity

• Best-market liquidity gains by fragmentation for depth close to the

midpoint (depth(10) improves by about 10% relative to concentrated

market)

• Best-market liquidity deeper in the order book deteriorates

27

-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Depth(10) Depth(20)Depth(30) Depth(40)

Best-market

Results for visible fragmentation: comparison

• SORT traders connecting to all visible venues benefit

• Frictions in accessing markets are important– Exchange fees, post-trading fees, collateral management

28

-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Ln Depth(10) Ln Depth(20)Ln Depth(30) Ln Depth(40)

Consolidated

-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Depth(10) Depth(20)Depth(30) Depth(40)

Local

-0.2

-0.1

0.0

0.1

0.2

0.3

Ln D

epth

0 .2 .4 .6VisFrag

Depth(10) Depth(20)Depth(30) Depth(40)

Best-market

Dark trading

• Dark Trading:

– Reduces depth: one standard deviation increase of

dark trading reduces depth around 9%

• for all depth levels

• for consolidated, best-market and local liquidity

– Increases effective spread and reduces realized

spread => suggests dark trading cream skims

uninformed order flow

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Robustness / other liquidity measures

• Qualitatively robust to:

– Firm, month, day dummies

– Instrumental Variables with as instruments (i) Ratio limit orders to trades at visible competitors, (ii) Average daily order size visible competitors, (iii) Average daily Dark order size , and theirrespective squares

• Visable Fragmentation and other liquidity measures:

– Effective spread:• VisFrag of 0.4 relative to concentrated market reduces effective spread

with 2.4 basispoints (bp) relative to a median spread of 12.5 bp

– Realized spread:• VisFrag of 0.4 reduces realized spread with 1.8 bp relative to a median

spread of 0.2 bp

– Quoted spread:• VisFrag of 0.4 reduces quoted spread with 2 bp relative to an average

spread of 12 bp30

Conclusions and policy implications

• Entry of new trading venues induced fragmented markets –visible and dark

• Visible fragmentation generates differential effects on liquidity

– Improves “consolidated” liquidity

– Deteriorates “local” liquidity

– leaves “best-market” liquidity unaffected

• Dark trading in general reduces depth and increases adverse selection on visible trading venues

• Open issues:

– does visible fragmentation impact on trading strategies of retail investors? Limit orders versus market orders? Role of high-frequency traders

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