1. Detecting Insider Trading
June 2, 2008
Detecting Insider Trading
MS&E444 Final Presentation
Manabu Kishimoto
Xu Tian
Li Xu

2. Overview Motivation & Focus Litigation Case Study (CNS Inc.)
Detecting Strategy
Automation and Optimization
Performance Evaluation
Conclusion

3. 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).

8. 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.

9. Detecting Strategy (1)
100 days
10 days
News?
Insider?
Background
Signal
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 Stock price 2. Expiration Date Filter Criterion Expiration date – Current date < 6 months
< +0.15

10. Detecting Strategy (2) > 75% > 1 Apply the following criteria:
100 days
10 days
News?
Insider?
Background
Signal
Apply the following criteria:
1. Call Ratio Criterion
　 Call volume
Call volume + Put volume
2. Total Volume Criterion Signal daily average volume
Background daily average volume
> 75%
> 1

11. 2007 First Half OptionMetrics Database
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/ /06
2005/ /06
2005/01/ /06/30
2007/01/ /06/30
# of events 15
474
1902

12. 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

13. 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

14. 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%

15. Performance Evaluation Benchmark #3: Histogram of Signal’s Lead Time before the News Announcement
Training Database
Testing Database

16. Performance Evaluation Benchmark #4: Prediction Errors
# of events
Stock jump more than 5%?
Yes No
Detected? 4 11 55
Litigation
# of events
Stock jump more than 5%?
Yes No
Detected?
114
360
828
Training
# of events
Stock jump more than 5%?
Yes No
Detected?
417
1485
18108
Testing

17. 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 = % 　　 Our return = %