Efficiency gains from the
integration of exchanges:
Lessons from Euronext’s
“natural experiment”
Dr. A. Jorge Padilla
LECG Europe
www.lecgcp.com
Leuven, 7 November 2006
2
The theory
The integration of exchanges produces a number of significant
efficiency gains:
 Cost savings
– Eliminates the duplication of costly infrastructure …
– … which may lead to a reduction in trading fees
– … and brokerage fees
 Direct user benefits
–
–
–
–
Savings on operating and capital costs
Trading more diversified portfolios
Increased cross-border trading …
… leading to increases in liquidity, as reflected by lower bid-ask
spreads, greater volume and lower volatility
3
Euronext’s natural experiment
 Integration between the French, Belgian, Dutch and Portuguese stock
exchanges to form Euronext (September 2000 – 2003)
 “Before and after” analysis on costs and user benefits …

… controlling for confounding factors (i.e., time-variant effects that
have nothing to do with integration)
Chronology of integration of cash trading business
Cash
Trading
integration
May
2001
October
2001
Brussels Amsterdam
Paris
Brussels
Paris
November
2003
Lisbon
Amsterdam
Brussels
Paris
4
Euronext’s natural experiment
 This experiment makes it possible to:
–
–
–
–
Evaluate the cost savings achieved through the integration process;
Investigate the pass-through of those savings;
Identify other sources of direct user benefits, and
Test the impact of integration on liquidity and, hence, on the implicit
trading costs faced by the users of the exchange.
5
Cost savings
 Significant reduction in operating costs:
– Overall, the total annual costs of Euronext’s continental operations fell by
137 million euros (25%) between 2001 and 2004.
– IT cost savings: Euronext’s total continental IT costs fell by 29% between 2001
and 2004.
– Headcount reductions: Euronext reduced the staffing levels of its continental
operations by 24% between 2001 and 2004.
Evolution of continental IT costs
following Euronext integration
Euronext continental staff numbers
2001-2004
Developm ent CAPEX
Internet IT costs
1400
m€
1600
1338
1218
1110
1200
1012
160
140
127
128
120
1000
100
800
80
600
Office automation IT costs
143
IT running costs
103
60
400
40
200
20
0
2001
2002
2003
2004
0
2001
2002
2003
2004
6
Trading fees
 The evidence shows that the average
trading fee charged in Paris fell by about
30% (in real terms) in the period from
December 1999 to December 2004.
1.6
1.4
1.2
1
De
c1
Fe 99
b2 9
A p 00
r2 0
J u 000
n
2
Au 000
g2
Oc 00
t2 0
De 000
c2
M 00
ar 0
M 200
ay 1
20
Ju 01
l2
S e 00
p 1
2
No 00
v2 1
J a 00
n 1
2
Ma 002
r
M 200
ay 2
2
Au 002
g
Oc 200
t2 2
De 002
c
Fe 200
b 2
2
A p 00
r2 3
Ju 003
n
2
A u 00
g2 3
No 00
v 3
J a 200
n 3
2
M 00
4
ar
2
Ma 00
y2 4
0
J u 04
l2
Se 004
p2
No 00
v2 4
00
4
Brussels and Amsterdam.
– From January 2002 to December 2004,
the average trading fee in Brussels fell
by 30%.
– From January 2001 to December 2004,
the average trading fee in Amsterdam
fell approximately 45%.
Euros
 Average trading fees also fell in
Paris
December 1 99 9-Dece mber 2 004
So urce:Euronext
Our econometric results show that those fee reductions were to a
large extent the result of the creation of Euronext
7
Direct user benefits
 Improved access:
– Integration has allowed Euronext members directly to access all the different
Euronext markets
– The process of integration has expanded the set of securities accessible
to a Euronext member.
– Investors now benefit from greater inter-broker competition.
Brussels
40%
36%
35%
Share of cross-border
trade undertaken by
Euronext members
(% of total trades of
members at each
location)
33%
Amsterdam
30%
24%
25%
Paris
20%
15%
14%
15%
10%
20%
18%
9%
8%
5%
0%
2002
2003
2004
2002
2003
2004
2002
2003
2004
8
Direct user benefits
 Members have benefited also from reduced internal operating
costs.
 Increased liquidity
– Lower bid-ask spreads;
– Greater volume;
– Lower volatility.
9
Panel data estimation
 We aim to estimate the impact of integration on liquidity. In order to do
so, we have estimated a panel data model that relates liquidity
measures with Euronext integration dummies.
 Liquidity is measured by:
-
Volume: the higher the liquidity, the higher the volume.
Bid-ask spread: the higher the liquidity, the lower the spread.
Volatility: the higher the liquidity, the lower the volatility.
 Therefore, we have tested whether Euronext integration had a positive
impact on volumes and a negative impact on bid-ask spreads, and
volatilities.
 In this analysis, we assumed that Euronext integration took place in
the following dates:
-
21-May-2001: Brussels and Paris trading
29-Oct-2001: Amsterdam, Brussels and Paris trading
7-Nov-2003: Lisbon, Amsterdam, Brussels and Paris trading
10
Methodology: specification
yit   Integratio n it   Control it  i   it
• Liquidity
(volume,
bid-ask
spread and
volatility) of
security i in
period t, or
• A dummy variable that takes the value of 1 if the security i is traded in
an integrated market in period t and 0 otherwise.
• Alternatively, we define three different dummies in order to
differentiate the impact of each integrated market:
1.“Integration Brussels” takes the value of 1 if if the security i is
traded in the (at-least) integrated market Paris – Brussels in period
t and 0 otherwise.
2.“Integration Amsterdam” takes the value of 1 if if the security i is
traded in the (at-least) integrated market Paris – Brussels –
Amsterdam in period t and 0 otherwise.
3.“Integration Lisbon” takes the value of 1 if if the security i is traded
in the fully integrated market (Paris, Brussels, Amsterdam and
Lisbon) in period t and 0 otherwise.
11
Specification (continued)
yit   Integratio n it   Control it  i   it
• Monthly dummies.
• Dummies related to relevant economic events
(similar to the ones used in the first stage).
• A deterministic time trend.
• Other controls (depend on data availability):
- In the volume regression: the volume of an
index traded in non-integrated markets (FTSE
100 and DAX).
- In the volatility regression: the volatility of the
index of the own market to net out covariance
risk.
• Fixed effects to control
for differences across
securities.
• This control is specially
important when using
data at the security
level. Panel data
models allow to
include a fixed effect
per security, therefore,
netting out differences
across securities.
12
Direct user benefits
 Lower bid-ask spreads
– The bid-ask spreads of the securities included in the main Paris index fell as
a result of the creation of Euronext: approx 40%.
– The analysis also shows that integration led to a reduction of the bid-ask
spreads of the securities in the main indices of Brussels (25%-30%) and
Amsterdam (approx. 10%)
Weighted Average Spread
3rd January 2000-28th February 2005
03
Ja
18 n 0
M 0
ar
01 00
Ju
15 n 0
A 0
ug
29 00
O
c
12 t 00
Ja
28 n 0
M 1
a
11 r 0
1
Ju
n
25
0
Au 1
08 g 0
No 1
v
22 01
Ja
n
07 0 2
A
p
21 r 0
Ju 2
n
04
02
Se
18 p 0
No 2
01 v 0
Fe 2
b
17 03
A
pr
01 03
Ju
14 l 03
Se
28 p 0
N 3
o
11 v 0
Fe 3
b
26 04
Ap
r
10 04
Ju
23 l 0
Se 4
07 p 0
D 4
e
20 c 0
Fe 4
b
05
0
.1
.2
.3
CAC 40
Source:Euronext
13
Liquidity effects
Bid-ask spreads (Bloomberg)
Ln Bid-Ask Spread
 Our main findings, using Bloomberg
data, are:
(1)
(2)
(3)
Paris
-0.515***
-0.488***
-0.406***
[0.000]
[0.000]
[0.000]
Brussels
-0.302***
-0.300***
-0.235***
[0.004]
Amsterdam
– In general, Euronext integration
had a negative, and statistically
significant, impact on bid-ask
spread.
– Our results show that Brussels,
Amsterdam and Lisbon
integration had a similar impact
on the bid-ask spreads, as
measured by Bloomberg.
Lisbon
[0.000]
[0.000]
-0.162**
-0.115
-0.043
[0.035]
[0.125]
[0.576]
0.046
0.250***
0.270***
[0.418]
[0.000]
[0.000]
0.434***
Ln Historical 20 days Volatility DAX
[0.000]
0.393***
Ln Historical 20 days Volatility
FTSE100
Constant
[0.000]
-6.801***
-5.991***
-5.906***
[0.000]
[0.000]
[0.000]
Security dummies
Yes
Yes
Yes
Monthly dummies
Yes
Yes
Yes
Economic events dummies
Yes
Yes
Yes
111,338
105,673
108,132
Number of Observations
R-squared
0.41
0.45
0.44
Notes:
(1) Robust p values in brackets, clustered by security to allow for
heteroskedasticity and autocorrelation within securities.
(2) * significant at 10%; ** significant at 5%; *** significant at 1%
(3) The Ln Bid-Ask Spread is the natural logarithm of the difference
between the daily closing ask price and the daily closing bid price. In
our analysis Bid-Ask Spread is measured as a percentage, and is
calculated as follows:
Bid  Ask Spread 
( PA  PB )
( PA  PB ) / 2
(4) The sample is composed by 104 large caps. In particular, we
include securities that compose the main index of the Paris, Brussels,
Amsterdam and Lisbon stock exchanges: CAC 40, BEL 20, AEX and PSI
respectively.
(5) The ask price and the bid price has been provided by Bloomberg.
We have data for the period between 3rd January 2000 and 31st
December 2004, on a daily basis.
14
Liquidity effects
Bid-ask spreads (Euronext)
 Our main findings, using Euronext
data, are:
– In general, Euronext integration
had a negative, and statistically
significant, impact on bid-ask
spread.
– Our results show that Brussels,
Amsterdam and Lisbon
integration had a similar impact
on the bid-ask spreads, as
measured by Bloomberg.
Ln Weighted Average Spread
Integration
(1)
(2)
-0.380***
[0.000]
Phase 1
-0.093***
Phase 2
-0.140***
Phase 3
-0.395***
[0.000]
[0.000]
[0.000]
Constant
-1.905***
-1.850***
[0.000]
[0.000]
Monthly dummies
Yes
Yes
Economic events dummies
Yes
Yes
1,313
1,313
Number of Observations
R-squared
0.45
0.70
Notes:
(1) Robust p values in brackets
(2) * significant at 10%; ** significant at 5%; *** significant at 1%
(3) The Ln Weighted Average Spread is the natural logarithm of the
difference between the best quoted ask price and the best quoted bid
price, weighted by transaction size. In our analysis Weighed Average
Spread is measured as a percentage.
(4) The Weighted Average Spread is only available for the index CAC40
quoted in Paris stock exchange.
(3) The Weighted Average Spread has been provided by Euronext. We
have data for the period between 3rd January 2000 and 28th February
2005, on a daily basis.
15
Direct user benefits
 Greater volume
– Trading volume in Paris, Brussels, and Amsterdam increased as a result of
the creation of Euronext.
– According to our estimations, the creation of Euronext led to an increase in
the traded volume of the main securities listed on the Paris, Brussels and
Amsterdam exchanges of approximately 40%.
Volume (Millions of shares traded)
3rd January 2000-31st December 2004
Brussels
0
0
20
100
40
200
60
300
Amsterdam
Paris
Source:Bloomberg
200
0
03
J
12 an 0
A 0
2 1 pr
J 00
29 ul
O 00
06 ct
Fe 00
17 b
M 01
25 ay
A 0
0 3 ug 1
De 01
13 c
M 01
21 ar 0
J 2
29 un
Se 02
07 p
Ja 02
17 n
A 03
26 pr 0
3
03 Jul
N 03
11 ov 0
2 1 F eb 3
M 0
29 ay 4
A 04
07 ug
De 04
c
04
03
Ja
12 n 0
A 0
21 pr
J 00
29 ul
O 00
06 ct
F 00
17 e b
M 01
2 5 ay
Au 01
03 g
D 01
1 3 ec
M 01
21 ar 0
J 2
29 un
Se 02
07 p 0
J 2
1 7 an
A 03
26 pr 0
3
03 Jul
N 03
11 ov
Fe 0 3
21 b
M 04
2 9 ay
A 0
07 ug 4
De 04
c
04
0
50
100
400
150
600
Lisbon
16
Liquidity effects
Volume
Ln Number of Shares Traded
 Our main findings are:
– Euronext integration had a positive,
and statistically significant, impact
on volume (defined as number of
shares traded).
(1)
(2)
(3)
0.520***
0.468***
0.497***
[0.000]
[0.000]
[0.000]
Brussels
0.529***
0.479***
0.509***
[0.000]
[0.000]
[0.000]
Amsterdam
0.364***
0.341***
0.370***
[0.005]
[0.009]
[0.005]
0.183
0.216
0.264*
[0.151]
[0.079]
Paris
Lisbon
[0.218]
Ln Number of Shares Traded in FTSE100
0.500***
[0.000]
– These results are robust to different
specifications of the panel data
model, in particular when including
the volume of an index traded in
non-integrated markets (FTSE 100
and DAX) as control variables.
Ln Number of Shares Traded in DAX
– Results are also robust when
defining volume in levels, except
that the integration of Brussels is no
longer statistically significant.
R-squared
0.85
0.86
0.86
Notes:
(1) Robust p values in brackets, clustered by security to allow for heteroskedasticity and
autocorrelation within securities.
0.407***
[0.000]
Time trend
Constant
0
-0.000***
-0.000***
[0.327]
[0.001]
[0.000]
15.696***
5.359***
8.534***
[0.000]
[0.000]
[0.000]
Security dummies
Yes
Yes
Yes
Monthly dummies
Yes
Yes
Yes
Economic events dummies
Yes
Yes
Yes
127,286
125,422
126,431
Number of Observations
(2) * significant at 10%; ** significant at 5%; *** significant at 1%
(3) The sample is composed by 104 large caps. In particular, we include securities that
compose the main index of the Paris, Brussels, Amsterdam and Lisbon stock exchanges:
CAC 40, BEL 20, AEX and PSI respectively.
(4) The number of shares traded has been provided by Bloomberg. We have data for the
period between 3rd January 2000 and 31st December 2004, on a daily basis.
Source:Bloomberg
Ja
12 n 0
A 0
2 1 pr
J 00
29 ul
O 00
06 ct
F 00
17 eb
M 01
25 ay
A 0
0 ug 1
3D 0
1
13 e c
M 01
21 ar 0
J 2
29 un
S 02
0 ep
7J 0
2
1 7 an
Ap 03
26 r 0
3
03 Jul
N 03
11 ov 0
2 F eb 3
1M
0
29 ay 4
A 04
07 ug
De 04
c
04
03
Ja
12 n 0
A 0
2 pr
1 J 00
29 ul
O 00
06 ct
F 00
17 e b
M 01
2 5 ay
A 0
03 ug 1
D 01
1 ec
3M 0
1
21 ar 0
J 2
29 un
Se 02
07 p 0
J 2
1 an
7A 0
p 3
26 r 0
3
03 Jul
N 03
11 ov
F 03
21 eb
M 04
2 9 ay
A 0
07 ug 4
D 04
ec
04
03
0
0
.1
.2
.2
.4
.3
0
0
.1
.2
.2
.3
.4
.4
17
Direct user benefits
 Lower volatility
– The volatility of the large-cap securities traded in Paris, Brussels, Amsterdam
and Lisbon fell as a result of the creation of Euronext.
– The reduction in volatility following integration was between 9% and 18% of
the initial levels
Historical 20 days Volatility
Amsterdam
3rd January 2000-31st December 2004
Brussels
Lisbon
Paris
18
Liquidity effects
Volatility
Ln Historical 20 days Volatility
(1)
– In general, Euronext integration
had a negative, and statistically
significant, impact on volatility
(defined as 20-days volatility)
when including the volatility of
the index of the own market as
a control variable.
– Our results show that
Amsterdam and Lisbon
integration had the highest
(negative) impact on volatility,
while Brussels integration had
no statistically significant
impact on volatility.
(3)
(4)
-0.261*** -0.180*** -0.252*** -0.152***
Brussels
-0.207*** -0.216*** -0.197***
Amsterdam
-0.209*** -0.174***
[0.000]
 Our main findings are:
(2)
Paris
[0.000]
Lisbon
[0.000]
[0.000]
[0.000]
-0.089*
[0.000]
[0.083]
-0.122**
-0.028
[0.035]
[0.629]
[0.000]
[0.003]
-0.374***
-0.109**
-0.053
-0.021
[0.000]
[0.030]
[0.301]
[0.678]
0.579***
Ln Historical 20 days Volatility of
the Index
[0.000]
0.627***
Ln Historical 20 days Volatility DAX
Ln Historical 20 days Volatility
FTSE100
Constant
[0.000]
[0.000]
0.614***
[0.000]
-1.780*** -0.472*** -0.456*** -0.220***
[0.000]
[0.000]
[0.000]
[0.000]
Security dummies
Yes
Yes
Yes
Yes
Monthly dummies
Yes
Yes
Yes
Yes
Economic events dummies
Yes
Yes
Yes
Yes
Number of Observations
111,793
111,793
108,065
110,142
R-squared
0.36
0.58
0.54
0.56
Notes:
(1) Robust p values in brackets, clustered by security to allow for heteroskedasticity
and autocorrelation within securities.
(2) * significant at 10%; ** significant at 5%; *** significant at 1%
(3) The Ln Historical 20 days Volatility is the natural logarithm of the annualized
standard deviation for closing stock prices returns observed on a time period of 20
days, and is calculated as follows:
P

Stock return xt  Ln t
Volatility

 Pt 1 
N
1
X ( mean of xt )   xt
N t 1
N
Historical 20 days Volatility  250 *
 x
t 1
t
 X
2
( N  1)
(3) The sample is composed by 104 large caps. In particular, we include securities
that compose the main index of the Paris, Brussels, Amsterdam and Lisbon stock
exchanges: CAC 40, BEL 20, AEX and PSI respectively.
(4) The closing prices has been provided by Bloomberg. We have data for the
period between 3rd January 2000 and 31st December 2004, on a daily basis
19
Conclusions
 The results of the natural experiment show:
– Significant cost savings were achieved as a result of the integration
process;
– Those savings were passed on in part to users;
– Users also enjoyed other benefits: access to more securities, increased
brokerage competition, lower transaction costs and, perhaps, most
importantly increased liquidity.
– The integration of the Amsterdam, Brussels, Lisbon and Paris
exchanges in a single platform resulted in a significant increase in
liquidity.
Jorge Padilla
LECG
[email protected]
www.lecgcp.com
Leuven, 7 November 2006