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Detecting insider trading: The theory and validation in Korea Exchange

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  • Park, Young S.
  • Lee, Jaehyun

Abstract

This paper provides an empirical basis for identifying insider transactions by deriving a theoretical model, which incorporates the relationship between insider transactions and time series of stock returns. Thus, this model enables us detecting insider transactions by applying stock return time series. We show that when there is an insider transaction in the market, time series can be derived as an ARMA(1,1) process having closed solution coefficients. For validation of the model, we test publicly released insider transactions and reverse takeover events using the minute-by-minute stock price data. The selected events show higher pass rate of the detection criteria than the current detection system which shows that our model produces smaller Type II error than the existing post transaction-based cumulative abnormal return model.

Suggested Citation

  • Park, Young S. & Lee, Jaehyun, 2010. "Detecting insider trading: The theory and validation in Korea Exchange," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2110-2120, September.
  • Handle: RePEc:eee:jbfina:v:34:y:2010:i:9:p:2110-2120
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    Cited by:

    1. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
    2. Lyudmila A. Glik & Oleg L. Kritski, 2014. "Detecting informed activities in European-style option tradings," Papers 1403.3294, arXiv.org.
    3. Lyudmila A. Glik & Oleg L. Kritski, 2014. "Finding informed traders in futures and their inderlying assets in intraday trading," Papers 1402.6583, arXiv.org.
    4. de Bonviller, Simon & Zuo, Alec & Wheeler, Sarah Ann, 2019. "Is there evidence of insider trading in Australian water markets?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 60(2), April.
    5. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    6. Ryu, Doojin & Yang, Heejin & Yu, Jinyoung, 2022. "Insider trading and information asymmetry: Evidence from the Korea Exchange," Emerging Markets Review, Elsevier, vol. 51(PA).
    7. Mazza, Paolo & Ruh, Benjamin, 2022. "The performance of corporate legal insider trading in the Korean market," International Review of Law and Economics, Elsevier, vol. 71(C).
    8. Cline, Brandon N. & Posylnaya, Valeriya V., 2019. "Illegal insider trading: Commission and SEC detection," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 247-269.

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    Keywords

    Insider trading Surveillance ARMA;

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