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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.

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Detecting insider trading

June 2, 2008

Detecting Insider Trading

MS&E444 Final Presentation

Manabu Kishimoto

Xu Tian

Li Xu


  • Motivation & Focus

  • Litigation Case Study (CNS Inc.)

  • Detecting Strategy

  • Automation and Optimization

  • Performance Evaluation

  • Conclusion

Motivation focus
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).

Salient statistical patterns
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
Detecting Strategy (1)

100 days

10 days


  • 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 price2. Expiration Date Filter Criterion Expiration date – Current date < 6 months




< +0.15

Detecting strategy 2
Detecting Strategy (2)

100 days

10 days


  • 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


  • Automatic processing script (PERL)

  • Optimize detection criteria

  • Use several benchmarks to evaluate the effectiveness of detection strategy

Optimize detection criteria
Optimize Detection Criteria

  • Define:

    • Right Detection: stock price rallies≥10%

    • Wrong Detection: stock price sinks≥10%

  • Optimization on Training database

    • Optimize to maximizeRight/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
Performance EvaluationBenchmark #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.

Training Database

Testing Database

Litigation Database

Performance evaluation benchmark 2 percentage r eturn of non leveraged simple trading strategy
Performance EvaluationBenchmark #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

Performance EvaluationBenchmark #3: Histogram of Signal’s Lead Time before the News Announcement

Testing Database

Training Database

Performance evaluation benchmark 4 prediction errors
Performance EvaluationBenchmark #4: Prediction Errors





  • 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%