IDEAS home Printed from https://ideas.repec.org/a/eme/jfcpps/13590791311287355.html
   My bibliography  Save this article

ANN‐GA based model for stock market surveillance

Author

Listed:
  • Murugesan Punniyamoorthy
  • Jose Joy Thoppan

Abstract

Purpose - This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation. Design/methodology/approach - Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF). Findings - Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation in stock prices has been established. Research limitations/implications - The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based. Practical implications - The market regulators can use these techniques to ensure that sufficient deterrents are in place to identify a manipulator in their market. This helps them carry out their primary function, namely, investor protection. These models will help effective monitoring for abnormal market activities and detect market manipulation. Social implications - Implementing this model at a regulator or SRO helps in strengthening the integrity and safety of the market. This strengthens investor confidence and hence participation, as the investors are made aware that the regulators implementing market manipulation detection techniques ensure that the markets they monitor are secure and protects investor interest. Originality/value - This is the first time a hybrid model has been used to detect market manipulation.

Suggested Citation

  • Murugesan Punniyamoorthy & Jose Joy Thoppan, 2013. "ANN‐GA based model for stock market surveillance," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 20(1), pages 52-66, January.
    • Handle: RePEc:eme:jfcpps:13590791311287355
    DOI: 10.1108/13590791311287355
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/13590791311287355/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/13590791311287355/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/13590791311287355?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Craig Pirrong, 2004. "Detecting Manipulation in Futures Markets: The Ferruzzi Soybean Episode," American Law and Economics Review, American Law and Economics Association, vol. 6(1), pages 28-71.
    2. Robert A. Jarrow, 2008. "Market Manipulation, Bubbles, Corners, and Short Squeezes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 6, pages 105-130, World Scientific Publishing Co. Pte. Ltd..
    3. Allen, Franklin & Gorton, Gary, 1992. "Stock price manipulation, market microstructure and asymmetric information," European Economic Review, Elsevier, vol. 36(2-3), pages 624-630, April.
    4. Allen, Franklin & Gale, Douglas, 1992. "Stock-Price Manipulation," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 503-529.
    5. Franklin Allen & Lubomir Litov & Jianping Mei, 2006. "Large Investors, Price Manipulation, and Limits to Arbitrage: An Anatomy of Market Corners," Review of Finance, European Finance Association, vol. 10(4), pages 645-693, December.
    6. Albert S. Kyle & S. Viswanathan, 2008. "How to Define Illegal Price Manipulation," American Economic Review, American Economic Association, vol. 98(2), pages 274-279, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pirrong, Craig, 2017. "The economics of commodity market manipulation: A survey," Journal of Commodity Markets, Elsevier, vol. 5(C), pages 1-17.
    2. Cumming, Douglas & Ji, Shan & Peter, Rejo & Tarsalewska, Monika, 2020. "Market manipulation and innovation," Journal of Banking & Finance, Elsevier, vol. 120(C).
    3. Ke Liu & Kin Lai & Jerome Yen & Qing Zhu, 2015. "A Model of Stock Manipulation Ramping Tricks," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 135-150, January.
    4. Takayama, Shino, 2021. "Price manipulation, dynamic informed trading, and the uniqueness of equilibrium in sequential trading," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    5. Yu Huang & Yao Cheng, 2015. "Stock manipulation and its effects: pump and dump versus stabilization," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 791-815, May.
    6. Allen, Franklin & Haas, Marlene D. & Nowak, Eric & Tengulov, Angel, 2021. "Market efficiency and limits to arbitrage: Evidence from the Volkswagen short squeeze," Journal of Financial Economics, Elsevier, vol. 142(1), pages 166-194.
    7. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2015. "Exchange trading rules, surveillance and suspected insider trading," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 311-330.
    8. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2013. "Exchange trading rules, surveillance and insider trading," CFS Working Paper Series 2013/15, Center for Financial Studies (CFS).
    9. Tālis J. Putniņš, 2012. "Market Manipulation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 26(5), pages 952-967, December.
    10. M. Punniyamoorthy & Jose Joy Thoppan, 2012. "Detection of stock price manipulation using quadratic discriminant analysis," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 5(4), pages 369-388.
    11. Cumming, Douglas & Johan, Sofia & Li, Dan, 2011. "Exchange trading rules and stock market liquidity," Journal of Financial Economics, Elsevier, vol. 99(3), pages 651-671, March.
    12. Rasim Ozcan, 2012. "An Analysis of Manipulation Strategies in Stock Markets," Istanbul Stock Exchange Review, Research and Business Development Department, Borsa Istanbul, vol. 13(49), pages 19-37.
    13. Sheridan Titman & Chishen Wei. Wei & Bin Zhao, 2021. "Corporate Actions and the Manipulation of Retail Investors in China: An Analysis of Stock Splits," NBER Working Papers 29212, National Bureau of Economic Research, Inc.
    14. Cumming, Douglas & Dannhauser, Robert & Johan, Sofia, 2015. "Financial market misconduct and agency conflicts: A synthesis and future directions," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 150-168.
    15. Xihan Xiong & Zhipeng Wang & Tianxiang Cui & William Knottenbelt & Michael Huth, 2023. "Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications," Papers 2311.17715, arXiv.org, revised Mar 2024.
    16. Giancarlo Corsetti & Paolo Pesenti & Nouriel Roubini, 2002. "The Role of Large Players in Currency Crises," NBER Chapters, in: Preventing Currency Crises in Emerging Markets, pages 197-268, National Bureau of Economic Research, Inc.
    17. Peck, James, 2014. "A battle of informed traders and the market game foundations for rational expectations equilibrium," Games and Economic Behavior, Elsevier, vol. 88(C), pages 153-173.
    18. Matthew Pritsker, 2005. "Large investors: implications for equilibrium asset, returns, shock absorption, and liquidity," Finance and Economics Discussion Series 2005-36, Board of Governors of the Federal Reserve System (U.S.).
    19. Chakraborty, Archishman & Yilmaz, Bilge, 2004. "Manipulation in market order models," Journal of Financial Markets, Elsevier, vol. 7(2), pages 187-206, February.
    20. Enrique Mart'inez-Miranda & Peter McBurney & Matthew J. Howard, 2015. "Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective," Papers 1511.00740, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:jfcpps:13590791311287355. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.