IDEAS home Printed from https://ideas.repec.org/p/fip/fednsr/862.html
   My bibliography  Save this paper

Insider networks

Author

Abstract

How do insiders respond to regulatory oversight? History suggests that they form sophisticated networks to share information and circumvent regulation. We develop a theory of the formation and regulation of information transmission networks. We show that agents with sufficiently complex networks bypass any given regulatory environment. In response, regulators employ broad regulatory boundaries to combat gaming, giving rise to regulatory ambiguity. Tighter regulation induces agents to migrate transmission activity from existing social networks to a core-periphery insider network. A small group of agents endogenously arise as intermediaries for the bulk of information. We provide centrality measures that identify intermediaries.

Suggested Citation

  • Selman Erol & Michael Junho Lee, 2018. "Insider networks," Staff Reports 862, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:862
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr862.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://www.newyorkfed.org/research/staff_reports/sr862.html
    File Function: Summary
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. , & , & ,, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    2. Peter M. DeMarzo & Michael J. Fishman & Kathleen M. Hagerty, 1998. "The Optimal Enforcement of Insider Trading Regulations," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 602-632, June.
    3. Bloch, Francis & Dutta, Bhaskar, 2009. "Communication networks with endogenous link strength," Games and Economic Behavior, Elsevier, vol. 66(1), pages 39-56, May.
    4. Ahern, Kenneth R., 2017. "Information networks: Evidence from illegal insider trading tips," Journal of Financial Economics, Elsevier, vol. 125(1), pages 26-47.
    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. Pierce, Andrew T., 2024. "Capital-market effects of tipper-tippee insider trading law: Evidence from the Newman ruling," Journal of Accounting and Economics, Elsevier, vol. 77(2).
    2. Patrick Augustin & Menachem Brenner & Marti G. Subrahmanyam, 2019. "Informed Options Trading Prior to Takeover Announcements: Insider Trading?," Management Science, INFORMS, vol. 65(12), pages 5697-5720, December.
    3. Marcin Kacperczyk & Emiliano S. Pagnotta, 2024. "Legal Risk and Insider Trading," Journal of Finance, American Finance Association, vol. 79(1), pages 305-355, February.
    4. Billand, Pascal & Bravard, Christophe & Sarangi, Sudipta, 2012. "Existence of Nash networks and partner heterogeneity," Mathematical Social Sciences, Elsevier, vol. 64(2), pages 152-158.
    5. Acharya, Viral V. & Johnson, Timothy C., 2010. "More insiders, more insider trading: Evidence from private-equity buyouts," Journal of Financial Economics, Elsevier, vol. 98(3), pages 500-523, December.
    6. Alexandre Madelaine, 2024. "Negative Externalities of Regulation: Identity‐relevant Information in Mandatory Short‐selling Disclosures," Abacus, Accounting Foundation, University of Sydney, vol. 60(4), pages 892-934, December.
    7. Goergen, Marc & Renneboog, Luc & Zhao, Yang, 2019. "Insider trading and networked directors," Journal of Corporate Finance, Elsevier, vol. 56(C), pages 152-175.
    8. Ping Sun & Elena Parilina, 2024. "Networks with nonordered partitioning of players: stability and efficiency with neighborhood-influenced cost topology," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 99(3), pages 271-305, June.
    9. Tinic, Murat & Sensoy, Ahmet & Demir, Muge & Nguyen, Duc Khuong, 2020. "Broker Network Connectivity and the Cross-Section of Expected Stock Returns," MPRA Paper 104719, University Library of Munich, Germany.
    10. Tsang, Kwok Ping & Yang, Zichao, 2022. "Do connections pay off in the bitcoin market?," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 1-18.
    11. Cabrales, Antonio & Calvó-Armengol, Antoni & Zenou, Yves, 2011. "Social interactions and spillovers," Games and Economic Behavior, Elsevier, vol. 72(2), pages 339-360, June.
    12. Gil S. Epstein & Odelia Heizler-Cohen, 2016. "The Formation of Immigrant Networks in the Short and the Long Run," Economies, MDPI, vol. 4(3), pages 1-16, July.
    13. Harmsen - van Hout, Marjolein J.W. & Herings, P. Jean-Jacques & Dellaert, Benedict G.C., 2013. "Communication network formation with link specificity and value transferability," European Journal of Operational Research, Elsevier, vol. 229(1), pages 199-211.
    14. Bich, Philippe & Teteryatnikova, Mariya, 2023. "On perfect pairwise stable networks," Journal of Economic Theory, Elsevier, vol. 207(C).
    15. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    16. Bittner, Christian & Fecht, Falko & Pala, Melissa & Saidi, Farzad, 2022. "Information transmission between banks and the market for corporate control," Discussion Papers 29/2022, Deutsche Bundesbank.
    17. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    18. Ross Levine & Chen Lin & Lai Wei, 2017. "Insider Trading and Innovation," Journal of Law and Economics, University of Chicago Press, vol. 60(4), pages 749-800.
    19. Grabisch, Michel & Rusinowska, Agnieszka, 2011. "Influence functions, followers and command games," Games and Economic Behavior, Elsevier, vol. 72(1), pages 123-138, May.
    20. Baumann, Leonie, 2021. "A model of weighted network formation," Theoretical Economics, Econometric Society, vol. 16(1), January.

    More about this item

    Keywords

    network formation; endogenous intermediation; regulatory ambiguity; insider trading;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:fip:fednsr:862. 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: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

    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.