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).

$ 40 35 30 25 20 15 10 5 0 8/9/2006

Daily Stock Price (CNS Inc.)

36.72

28.5% increase

28.56

GlaxoSmithKline would acquire CNS for $37.50 per share

10/9/2006

Acquisition News Release

11/9/2006

2000 1800 1600 1400 1200 1000 800 600 400 200 0 9/11/2006

Daily Option Volume (CNS Inc.) SEC claims that there was illegal insider trading on these four trading days.

9/27 10/2 call put 10/9

News

1600 1400 1200 1000 800 600 400 200 0

Aggregated Call Option Volume (CNS Inc.) Sep 27 - Oct 2, 2006

Stock Price: $28 17.5

20 22.5

25 Strike Price ($) 30 35

900 800 700 600 500 400 300 200 100 0

Aggregated Call Option Volume (CNS Inc.) Sep 27 - Oct 2, 2006

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

10

days News?

• • Insider?

Background Signal

Use moving windows

: take

100

background data and

10

trading days as 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 <

+0.15

2. Expiration Date Filter Criterion

Expiration date – Current date <

6

months

Detecting Strategy (2)

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

Automation

• Automatic processing script (PERL) • Optimize detection criteria • Use several benchmarks to evaluate the effectiveness of detection strategy # of Tickers Year # of events Litigation Database Training Database CNXS, DJ, INVN 3 2002/01-2004/06 2005/01-2007/06 15 Event Database 99 2005/01/01 2007/06/30 474 Testing Database 2007 First Half OptionMetrics Database 3068 2007/01/01 2007/06/30 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 NSTS Return Buy and hold Return Litigation Database +15% +39% ( Acquisition rich ) Training Database +5.7% +28% ( Acquisition rich ) Testing Database

+7.47% +2.82%

Performance Evaluation Benchmark #3: Histogram of S ignal’s Lead Time before the News Announcement Training Database Testing Database

Performance Evaluation Benchmark #4: Prediction Errors

# of events Detected?

Yes No

# of events Detected?

Yes No

# of events Detected?

Yes No

Stock jump more than 5%?

Yes No

4 55 11 Stock jump more than 5%?

Yes No

114 360 828 Stock jump more than 5%?

Yes No

417 18108 1485

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%