IDEAS home Printed from https://ideas.repec.org/a/mmb/journl/articl_v4_9_15.html

Structuring elements of the intellectual capital

Author

Listed:
  • Gordienko M. S.

    (Plekhanov Russian University of Economics)

Abstract

On the basis of the study, which aimed to structuring empirical data about the in-tellectual capital of business entities, formed classification features and presents the author's classification of the elements of intellectual capital.

Suggested Citation

  • Gordienko M. S., 2015. "Structuring elements of the intellectual capital," Annals of marketing-mba, Department of Marketing, Marketing MBA (RSconsult), vol. 4, December.
  • Handle: RePEc:mmb:journl:articl_v4_9_15
    as

    Download full text from publisher

    File URL: http://www.marketing-mba.ru/article/v4_15/Gordienko.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Грызунова Н. В., 2012. "Управление Акционерным Капиталом: Проблемы Собственности И Конфликт Интересов," Бизнес в законе. Экономико-юридический журнал, CyberLeninka;Издательский дом Юр-ВАК, issue 5, pages 134-138.
    2. Archil Gulisashvili & Frederi Viens & Xin Zhang, 2015. "Extreme-Strike Asymptotics for General Gaussian Stochastic Volatility Models," Papers 1502.05442, arXiv.org, revised Feb 2017.
    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. Archil Gulisashvili & Frederi Viens & Xin Zhang, 2015. "Small-time asymptotics for Gaussian self-similar stochastic volatility models," Papers 1505.05256, arXiv.org, revised Mar 2016.
    2. Archil Gulisashvili, 2018. "Gaussian stochastic volatility models: Scaling regimes, large deviations, and moment explosions," Papers 1808.00421, arXiv.org, revised Jun 2019.
    3. Ankush Agarwal & Stefano de Marco & Emmanuel Gobet & Gang Liu, 2017. "Rare event simulation related to financial risks: efficient estimation and sensitivity analysis," Working Papers hal-01219616, HAL.
    4. Archil Gulisashvili, 2020. "Time-inhomogeneous Gaussian stochastic volatility models: Large deviations and super roughness," Papers 2002.05143, arXiv.org, revised Dec 2020.
    5. Archil Gulisashvili, 2020. "Large deviation principles for stochastic volatility models with reflection and three faces of the Stein and Stein model," Papers 2006.15431, arXiv.org.
    6. Gulisashvili, Archil, 2020. "Gaussian stochastic volatility models: Scaling regimes, large deviations, and moment explosions," Stochastic Processes and their Applications, Elsevier, vol. 130(6), pages 3648-3686.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    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:mmb:journl:articl_v4_9_15. 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: Sidorchuk, Roman (email available below). General contact details of provider: https://edirc.repec.org/data/rsconru.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.