IDEAS home Printed from https://ideas.repec.org/a/now/jnlrbe/105.00000060.html
   My bibliography  Save this article

Portfolio Optimization With Investor Utility Preference of Higher-Order Moments: A Behavioral Approach

Author

Listed:
  • Bekiros, Stelios
  • Loukeris, Nikolaos
  • Eleftheriadis, Iordanis

Abstract

We incorporate advanced higher moments of individual or institutional investors in a new approach dealing with the portfolio selection problem, formulated under a multi-criteria optimization framework. The “integrated portfolio intelligence†model extracts hidden patterns out of company fundamental indices and filters out effects such as trader noise or fraud utilizing advanced big data machine learning modeling. One of the main advantages of this novel system aside from providing with computer-efficient algorithmic optimality and predictive out performance is that it detects and extracts hidden trader behavioral patterns and firm investment “styles†from the data sets of large-scale institutional portfolios, which ultimately leads to the aversion and protection of extensive market manipulation and speculation.

Suggested Citation

  • Bekiros, Stelios & Loukeris, Nikolaos & Eleftheriadis, Iordanis, 2017. "Portfolio Optimization With Investor Utility Preference of Higher-Order Moments: A Behavioral Approach," Review of Behavioral Economics, now publishers, vol. 4(2), pages 83-106, September.
    • Handle: RePEc:now:jnlrbe:105.00000060
    DOI: 10.1561/105.00000060
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1561/105.00000060
    Download Restriction: no
    File URL: https://libkey.io/10.1561/105.00000060?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & He, Yun-Xin & Qiu, Tian & Ren, Fei & Shi, Yong-Dong & Zhong, Chen-Yang, 2020. "Multiple learning mechanisms promote cooperation in public goods games with project selection," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

    More about this item

    Keywords

    Utility preference; Support Vector Machines; Genetic Evolution;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:now:jnlrbe:105.00000060. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Lucy Wiseman (email available below). General contact details of provider: http://www.nowpublishers.com/ .

    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.