June 2, 2008
Detecting Insider Trading
MS&E444 Final Presentation
Manabu Kishimoto
Xu Tian
Li Xu
Overview
•
•
•
•
•
•
Motivation & Focus
Litigation Case Study (CNS Inc.)
Detecting Strategy
Automation and Optimization
Performance Evaluation
Conclusion
Motivation & Focus
• If we can detect insider trading before the
news release, we can generate excess returns.
• In our project, we focus on the option market
because
– It gives leveraged return for insiders;
– It is more thinly traded than the stock market;
– It is more informative than the stock market.
• We also focus on good news (e.g. Acquisition).
Daily Stock Price (CNS Inc.)
$
40
36.72
35
28.5% increase
30
28.56
25
20
15
10
GlaxoSmithKline
would acquire CNS
for $37.50 per share
5
0
8/9/2006
10/9/2006
Acquisition News Release
11/9/2006
Daily Option Volume (CNS Inc.)
2000
1800
call
1600
put
1400
1200
SEC claims that there was
illegal insider trading on
these four trading days.
1000
800
600
400
200
0
9/11/2006
9/27
10/2
10/9
News
Aggregated Call Option Volume (CNS Inc.)
Sep 27 - Oct 2, 2006
1600
1400
Stock Price:
$28
Volume
1200
1000
800
600
400
200
0
17.5
20
22.5
25
Strike Price ($)
30
35
Volume
Aggregated Call Option Volume (CNS Inc.)
Sep 27 - Oct 2, 2006
900
800
700
600
500
400
300
200
100
0
3 weeks
(Oct 21)
7 weeks
(Nov 18)
11 weeks
(Dec 16)
Time to Expiration
24 weeks
(Mar 17, 2007)
Salient Statistical Patterns
1. Call-put imbalance is large;
2. Total option volume is high;
3. Insiders prefer slightly in-the-money
or out-of-the-money option;
4. Near-term option is preferred.
Detecting Strategy (1)
100 days
Background
•
•
10 days
Signal
News?
Insider?
Use moving windows: take 100 trading days as
background data and 10 days as the signal
Filter the data: focus on the data which satisfy the
following two conditions:
1. Strike Price Filter Criterion
Stock price – Strike price
< +0.15
Stock price
2. Expiration Date Filter Criterion
Expiration date – Current date < 6 months
Detecting Strategy (2)
100 days
Background
•
10 days
Signal
News?
Insider?
Apply the following criteria:
1. Call Ratio Criterion
Call volume
Call volume + Put volume > 75%
2. Total Volume Criterion
Signal daily average volume
Background daily average volume
>1
Automation
• Automatic processing script (PERL)
• Optimize detection criteria
• Use several benchmarks to evaluate the effectiveness
of detection strategy
Litigation Database
Training Database
Testing Database
CNXS, DJ, INVN
Event Database
2007 First Half
OptionMetrics
Database
# of Tickers
3
99
3068
Year
2002/01-2004/06
2005/01-2007/06
2005/01/012007/06/30
2007/01/012007/06/30
# of events
15
474
1902
Optimize Detection Criteria
• Define:
– Right Detection: stock price rallies ≥ 10%
– Wrong Detection: stock price sinks ≥ 10%
• Optimization on Training database
– Optimize to maximize Right/Total Ratio
– Optimize the criteria to maximize Right/Wrong
Ratio
• Change only one parameter at a time
Performance Evaluation
Benchmark #1: Histogram of Stock Return
• If we buy 1 share of stock when the signal suggests insider
events, and sell it after holding it for 10 days, we obtained the
histogram of the percentage return for all tickers in the database.
Litigation Database
Training Database
Testing Database
Performance Evaluation
Benchmark #2: Percentage Return of
Non-leveraged Simple Trading Strategy
• Non-leveraged Simple Trading Strategy (NSTS):
– Allocate $1 for every ticker in the database
– Check whether there is possible insider trading just before the market closes
Yes: Use all balance allocated to buy shares of stocks and sell it after 10 days.
No: Do nothing.
– Calculate annualized percentage returns for all the funds allocated at the end
of the period
– Compare the return with the Buy-and-Hold strategy
Litigation Database
Training Database
Testing Database
NSTS Return
+15%
+5.7%
+7.47%
Buy and hold
Return
+39%
(Acquisition rich)
+28%
(Acquisition rich)
+2.82%
Performance Evaluation
Benchmark #3: Histogram of Signal’s Lead
Time before the News Announcement
Training Database
Testing Database
Testing
Training
Litigation
Performance Evaluation
Benchmark #4: Prediction Errors
# of events
Detected?
Yes
No
Yes
4
11
No
55
# of events
Detected?
Stock jump more than 5%?
Yes
No
Yes
114
360
No
828
# of events
Detected?
Stock jump more than 5%?
Stock jump more than 5%?
Yes
No
Yes
417
1485
No
18108
Conclusion
• There are salient statistical patterns of insider trading
in the option market.
1. Call-put imbalance is large;
2. Total option volume is high;
3. Slightly in-the-money or out-of-the-money is preferred;
4. Near-term option is preferred.
• By detecting insider trading before the news release,
excess returns can be generated.
- Based on 2007 data, Market return = + 2.82%
Our return = + 7.47%