Transcript Exchange Surveillance Index

Exchange Trading Rules
Douglas Cumming
Schulich School of Business
York University, Toronto, Canada
Sofia Johan
Tilburg Law and Economics Center (TILEC)
Tilburg University, The Netherlands
Research Question

Vague versus detailed rules

Context: equity stock exchanges

Do detailed trading rules facilitate
trading?
• Enhance investor confidence that the
risk of manipulation has been mitigated
Prior Work

Aitken and Siow (2003 Hewlett-Packard
Handbook of World Stock, Derivative &
Commodity Exchanges)
• Rank markets based on efficient and integrity

La Porta et al. (2006 Journal of Finance)
• Public enforcement of securities laws does little to help
the development of stock markets
• Private enforcement and disclosure rules help stock
markets

Cumming and Johan (2008 American Law and
Economics Review)
• First look at international differences in surveillance
• Cross-market surveillance and information sharing helps
development of stock markets
New Contributions

First look at exchange trading rules across
countries and time
Relate trading rules to trading velocity

Trading Rules

• Insider trading
• Market Manipulation




Price Manipulation
Volume Manipulation
Spoofing
False Dissemination
• Broker agency conduct
What Are Business Trading Rules?

Found on the stock exchange webpage

Like a contract between all stock
exchange trading participants


Some exchanges have vague rules
(“thou shalt not manipulate the market”)
Other exchanges precisely set out exactly
what they mean by manipulation in the
rules…
Insider Trading Rules










Frontrunning
Client Precedence
Trading Ahead of Research Reports
Separations of Research and Trading
Broker Ownership Limit
Restrictions on Affiliation
Restrictions on Communications
Investment Company Securities
Influencing or Rewarding Employees of Others
Anti-Intimidation/ Coordination
Market Manipulation Rules

Price Manipulation

Volume Manipulation

Spoofing

False Dissemination
Price Manipulation







Marking the Open
Marking the Close
Misleading End of Month/Quarter/Year
Trades
Intraday Ramping/ Gouging
Market Setting
Pre-Arranged Trades
Domination and Control
Volume Manipulation

Churning

Wash Trades
Spoofing

Giving up Priority

Switch

Layering of Bids/Asks
False Dissemination


Dissemination of False and
Misleading Information
Parking or Warehousing
Broker Agency Conduct





Trade Through
Improper Execution
Restrictions on Member Use of
Exchange Name
Restrictions on Sales Materials and
Telemarketing
Fair Dealing with Customers
New Indices in this Paper
Across Countries and Time

Insider Trading

Market Manipulation
•
•
•
•

Price Manipulation
Volume Manipulation
Spoofing
False Dissemination
Broker Agency Conflict
Trading Velocity

Accounts for market size… makes different
exchanges comparable
Monthly Domestic
Share
Turnover
Month - end Domestic Market Capitaliza tion
Table 3. Descriptive Statistics
This table presents descriptive statistics for the full sample of country-month observations in the data. The data span the
months February 2006 - October 2008, and the exchanges listed in Table 2.
Mean
Median
Standard
Deviation
Minimum
Maximum
Number of
Observations
Velocity
0.951
0.709
0.751
0.034
4.093
1363
Insider Trading Index
2.202
2
2.096
0
10
1363
Market Manipulation Index
5.266
4
4.036
0
13
1363
Price Manipulation Index
2.627
2
2.268
0
7
1363
Volume Manipulation Index
0.691
1
0.710
0
2
1363
Spoofing Index
1.242
1
1.019
0
3
1363
False Disclosure Index
0.707
1
0.583
0
2
1363
Broker Agency Index
0.836
0
1.240
0
5
1363
Investor Protection Index
2.344
2.337
0.826
0.686
3.775
1363
Log (1+MSCI)
-0.001
0.009
0.057
-0.371
0.138
1363
Log (GDP)
9.500
10.164
1.332
6.565
11.304
1363
Comparison Tests

Difference in means and medians

Panel A: All countries

Panel B: Subset of Mifid Countries

Panel C: Pre- versus Post-Mifid
Insider Trading Index
Market Manipulation Index
Broker Agency Index
Panel A. All
Countries
Investor Protection
Index
>1
<=1
>5
<=5
>0
=0
>2
<=2
Number of
Observations
883
480
537
826
526
837
891
472
Mean
1.07
0.74
1.17
0.81
0.92
0.97
0.99
0.88
Standard
Deviation
0.84
0.49
0.75
0.72
0.82
0.70
0.81
0.61
Median
0.81
0.60
0.93
0.53
0.68
0.74
0.78
0.63
Difference in
Means
8.963***
8.986***
-1.087
2.946***
Difference in
Medians
p <= 0.000***
p <= 0.000***
p <= 0.171
p <= 0.000***
Insider Trading Index
Panel B. Subset of MiFID
Countries
Market Manipulation
Index
Broker Agency Index
Investor Protection
Index
>1
<=1
>5
<=5
>0
=0
>2
<=2
Number of Observations
312
84
207
189
87
309
198
198
Mean
1.227
1.026
1.186
1.183
1.366
1.133
1.341
1.028
Standard Deviation
0.562
0.505
0.596
0.510
0.316
0.597
0.333
0.678
Median
1.365
0.957
1.357
1.206
1.352
1.292
1.383
0.717
Difference in Means
3.158***
0.062
4.835***
5.826***
Difference in Medians
p <= 0.000***
p <= 0.028 **
p <= 0.067*
p <= 0.000***
Non-MiFID Countries
MiFID Countries
Panel C. Pre-MiFID versus Post-MiFID
Post-MiFID
Pre-MiFID
Post-MiFID
Pre-MiFID
Number of Observations
358
609
144
252
Mean
0.895
0.832
1.267
1.137
Standard Deviation
0.841
0.772
0.581
0.537
Median
0.679
0.563
1.373
1.276
Difference in Means
1.167
2.200***
Difference in Medians
p <= 0.019***
p <= 0.025**
OLS Regressions

Trading Velocity is a function of
•
•
•
•
•
•

Insider Trading Index
Market Manipulation Index
Broker Conflict Index
LLSV (1998, 2006) Indices
GDP per capita (annual lagged)
MSCI Index (monthly lagged)
Robustness
• Country dummy variables & fixed effects
• Difference-in-differences
• Endogeneity considered later
Model (1)
Table 6
Constant
Model (2)
Model (3)
Model (4)
Coeffici
Coeffici
tCoeffici
tCoefficie
tt-statistic
ent
ent
statistic
ent
statistic
nt
statistic
-1.588
Country Dummy
Variables
Country Fixed
Effects
AR(1) Model
-1.850*
23.938
***
0.054
2.026*
*
1.763
1.836
2.150**
Yes
Yes
No
Yes
No
No
No
Yes
No
Yes
No
Yes
Yes
Yes
0.052
4.018***
0.121
3.038*
**
Treat
3.455
17.620**
*
0.908
8.867*
**
Insider Trading
Index
0.037
4.005*
**
0.017
1.866*
Market
Manipulation
Index
-0.080
Coefficie
t-statistic
nt
Yes
After*Treat
Log (1+MSCI)
Model (5)
4.464***
-0.159
-1.244
-0.081
4.526*
**
-0.025
-1.352
0.005
3.356***
-0.083
4.624***
Number of
Observations
1363
1363
1363
1363
1363
Adjusted R2
0.88
0.88
0.88
0.87
0.88
Model (6)
Table 6
Constant
Model (8)
Model (9)
Coeffici
tCoeffici
tCoeffici
tCoefficie
tent
statistic
ent
statistic
ent
statistic
nt
statistic
2.174*
*
1.847
Country Dummy
Variables
Country Fixed
Effects
AR(1) Model
Price Index
Model (7)
2.203*
*
1.858
2.155*
*
1.852
1.824
1.995**
Coefficie
t-statistic
nt
1.958
2.058**
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
0.008
3.490*
**
Volume Index
0.036
2.059*
*
Spoofing Index
0.021
3.073*
**
False Disclosure
Index
2.811**
*
0.052
Broker Agency Index
Log (1+MSCI)
Model (10)
-0.083
4.585*
**
-0.087
4.855*
**
-0.084
4.683*
**
-0.084
4.663**
*
-0.019
-0.413
-0.088
4.862***
Number of
Observations
1363
1363
1363
1363
1363
Adjusted R2
0.88
0.88
0.88
0.88
0.88
Model (11)
Table 6
Constant
Country Dummy
Variables
Country Fixed
Effects
9.738**
*
-1.904
Model (14)
-1.850
Log (1+MSCI)
-0.040
Log (GDP)
Number of
Observations
0.261
9.532**
*
-1.975
-1.944
-9.337***
Model (15)
Coefficie
nt
t-statistic
-1.987
10.299***
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
19.856*
**
0.177
9.298**
*
0.172
17.589*
**
0.143
10.468*
**
0.144
10.582**
*
0.005
1.330
0.008
2.115**
0.007
1.889*
0.065
2.949**
*
0.035
1.455
0.072
2.977***
Broker Agency Index
Investor Protection
Index
Efficiency of the
Judiciary
Adjusted R2
Model (13)
Coeffici
tCoeffici
tCoeffici
tCoefficie
tent
statistic
ent
statistic
ent
statistic
nt
statistic
AR(1) Model
Insider Trading Index
Market Manipulation
Index
Model (12)
-0.733
20.362*
**
-0.029
0.254
-0.522
18.265*
**
-0.032
0.266
-0.594
18.404*
**
-0.039
0.248
-0.724
15.816**
*
0.169
18.392***
0.183
7.720***
-0.099
-9.536***
-0.047
-0.907
0.306
21.492***
1363
1363
1363
1363
1363
0.26
0.27
0.28
0.29
0.32
IV Estimates

Rules  Velocity

Velocity  Rules

Use instruments very similar to that
in La Porta et al. (2006):
• English Legal Origin
• Repudiation
• Efficiency of the Judiciary
First Stage IV Estimates
Table 7
(16) Market
Manipulation
Index
(17) Insider
Trading Index
Second Stage IV Estimates
(18) Investor
Protection Index
Coeffici
ent
tstatistic
Coeffici
ent
tstatistic
Constant
-12.871
3.977**
*
-0.682
-0.350
1.510
2.207*
*
Log GDP per Capita
1.011
1.371
0.012
0.041
-0.021
-0.221
English Legal Origin
0.581
0.546
0.610
0.924
0.986
3.993*
**
Repudiation Index
1.395
2.617**
*
0.040
0.177
-0.013
-0.140
Efficiency of the
Judiciary
-0.173
-0.553
0.319
1.850*
0.095
1.149
(19) Velocity
Coeffici
tCoeffici
ent
statistic
ent
(20) Velocity
tCoeffici
tstatisti
ent
statistic
c
0.536
1.817*
2.276
1.667*
-0.094
1.874*
-0.079
-1.522
Market Manipulation
Index (fitted values for
Models 19-22)
0.167
6.147*
**
0.246
3.210**
*
Log (1+MSCI)
3.522
0.430
-1.274
-0.160
-0.236
-1.311
Log (GDP)
Number of
Observations
42
42
42
42
42
Adjusted R2
0.45
0.08
0.41
0.41
0.31
Second Stage IV Estimates
Model (21)
Constant
Efficiency of the
Judiciary
Insider Trading Index
(fitted values for
Models 19-22)
Market Manipulation
Index (fitted values
for Models 19-22)
Model (22)
OLS Estimates without Instruments
Model (23)
Model (24)
Coeffici
ent
tstatistic
Coeffici
ent
tstatistic
Coeffici
ent
tstatistic
2.198
1.749*
2.599
1.797*
-1.156
-1.734*
-1.572
0.062
0.374
-0.032
-0.487
-0.064
-1.312
-0.119
-0.351
-0.843
0.259
3.304**
*
Investor Protection
Index (fitted values
for Models 19-22)
0.247
3.373**
-0.210
-0.843
0.047
1.715*
Model (25)
Coeffici
tCoeffici
tent
statistic
ent
statistic
1.968**
2.405**
0.143
1.865*
0.014
0.438
-1.234
-1.794*
-0.086
-1.770*
0.050
1.812*
0.098
0.608
Log (1+MSCI)
0.111
0.013
0.111
0.013
8.322
0.867
8.377
0.935
7.097
0.663
Log (GDP)
-0.265
-1.425
-0.262
-1.421
0.224
2.402**
0.298
2.713**
*
0.227
2.420**
Number of
Observations
42
42
42
42
42
Adjusted R2
0.31
0.39
0.18
0.27
0.17
Summary of Key Result

Exchange trading rules a very statistically significant and robust
factor

Insider Trading Rules greatest economic significance:
•

Market Manipulation Rules also economically significant
•


Each rule increases velocity by at least 3% depending on the specification
Each rule increases velocity by at least 1% depending on the specification
Economic significance is even greater with the IV estimates.
Example:
• Euronext Paris (138.57%) versus Hong Kong (95.32%)
• Actual difference: 43.25%
• Predicted difference for Insider Trading Rules (simple
regression): 31.2%
• Predicted difference for Market Manipulation Rules (simple
regression): 45.18%
Figure 1. Insider Trading Index
400,00%
350,00%
NASDAQ
Shenzhen
300,00%
Velocity
250,00%
Germany
Italy
200,00%
Korea
Shanghai
Taiwan
150,00%
Tokyo
France
Turkey
NYSE
InsideX
Spain
Линейная (InsideX)
y = 0,156x + 0,6127
R² = 0,1724
London
OSLO
Switzerland
OMX
Australia
100,00%
Hong Kong
Egypt
50,00%
Canada
Ireland
SingaporeIndia NSE
ThailandIndonesia
Greece
Brazil
Austria
Israel
46,81%
Philippines
Chile
Columbia
Peru
Jordon
Mexico
Bombay
Slovenia Sri Lanka
Bermuda Argentina
0,00%
0
Malaysia
2
4
6
Insider Trading Index
8
10
12
Figure 1. Partial Regression Plot of Velocity and Insider Trading Rules
Index
2.5
Residual Velocity
NASDAQ
2
Shenzhen
1.5
1
Italy
Tokyo
Egypt
Germany
Shanghai
0.5
NYSE
Korea
Spain
Turkey London
Paris Switzerland
Jordan
OMX
Bombay 0
-4
-2 Australia
0
2
Indonesia
Columbia
Brazil
Oslo
Philippines
Peru
Sri Lanka
Ireland Thailand
Hong Kong
-0.5
Mexico
Toronto
Chile
Austria
Singapore
Greece
New
Israel
Slovenia Argentina
Zealand
India NSE
Taiwan
-6
4
6
8
Malaysia
-1
Bermuda
-1.5
Residual Insider Trading Rules Index
-2
Figure 1. Partial Regression Plot of Velocity and Insider Trading Rules Index. This figure presents a partial regression plot
of velocity and the Insider Trading Rules Index. The independent variables include the investor protection index (La Porta et al., 2006),
the Efficiency of the Judiciary (La Porta et al., 1998), the log of MSCI and the log of GDP per capita.
The coefficient is equal to 0.152, (robust) t-statistic 2.437 and adjusted R2 is 0.157.
Figure 2. Market Manipulation Index
400,00%
350,00%
NASDAQ
Shenzhen
300,00%
Velocity
250,00%
200,00%
Korea
Shanghai
Taiwan
150,00%
Germany
Italy
NYSE
Market Manipulation Index
Spain
Линейная (Market Manipulation Index)
London
Oslo
OMX France
Switzerland
Tokyo
Turkey
Australia
100,00%
Hong Kong
Canada
Ireland
Singapore
India NSE
Thailand
Egypt Indonesia
Greece
Austria
Brazil
Israel New Zealand
Malaysia
50,00%
JordanBombay
Argentina
Columbia
Sri Lanka
Bulgaria
Bermuda
Philippines
Chile
Peru
0,00%
0
2
4
6
8
Market Manipulation Index
Slovenia
10
12
14
y = 0,0753x + 0,4969
R² = 0,198
Figure 2. Partial Regression Plot of Velocity and Market Manipulation
Trading Rules Index
Residual Velocity
2.5
NASDAQ
2
Shanghai
1.5
1
Germany
Shenzhen
Tokyo
0.5
Turkey
Taiwan
Korea
Egypt
Jordon
-6
-4
Australia
Malaysia
Chile New Zealand
Israel
Brazil
OMX
Peru
-2
Hong Columbia
Kong Philippines
-0.5
Singapore
Argentina
-1
0 Oslo
Spain
India
London NSE
Paris
Switzerland
0
-8
NYSE
Italy
Bombay
Indonesia 2
Sri Lanka
Mexico
4
Thailand
Ireland
Toronto
6
Austria
Greece
Slovenia
Bermuda
-1.5
Residual Market Manipulation Rules Index
-2
Figure 2. Partial Regression Plot of Velocity and Market Manipulation Trading Rules Index. This figure presents a partial
regression plot of velocity and the Market Manipulation Trading Rules Index. The independent variables include the investor protection I
ndex (La Porta et al., 2006), the Efficiency of the Judiciary (La Porta et al., 1998), the log of MSCI and the log of GDP per capita.
The coefficient is equal to 0.050, (robust) t-statistic 1.812 and adjusted R2 is 0.039.
Conclusions





Insider Trading and Market Manipulation Rules
are an important element to encouraging
investors to trade on stock exchanges
Detailed rules facilitate trading
Vague rules in countries with high risk of
repudiation
Not easy to change business rules, but possible
Mifid rules are an important benefit to trading on
European Exchanges