IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v348y2025i3d10.1007_s10479-022-05136-x.html
   My bibliography  Save this article

Big data applications with theoretical models and social media in financial management

Author

Listed:
  • Taiga Saito

    (The University of Tokyo)

  • Shivam Gupta

    (NEOMA Business School)

Abstract

This study presents big data applications with quantitative theoretical models in financial management and investigates possible incorporation of social media factors into the models. Specifically, we examine three models, a revenue management model, an interest rate model with market sentiments, and a high-frequency trading equity market model, and consider possible extensions of those models to include social media. Since social media plays a substantial role in promoting products and services, engaging with customers, and sharing sentiments among market participants, it is important to include social media factors in the stochastic optimization models for financial management. Moreover, we compare the three models from a qualitative and quantitative point of view and provide managerial implications on how these models are synthetically used along with social media in financial management with a concrete case of a hotel REIT. The contribution of this research is that we investigate the possible incorporation of social media factors into the three models whose objectives are revenue management and debt and equity financing, essential areas in financial management, which helps to estimate the effect and the impact of social media quantitatively if internal data necessary for parameter estimation are available, and provide managerial implications for the synthetic use of the three models from a higher viewpoint. The numerical experiment along with the proposition indicates that the model can be used in the revenue management of hotels, and by improving the social media factor, the hotel can work on maximizing its sales.

Suggested Citation

  • Taiga Saito & Shivam Gupta, 2025. "Big data applications with theoretical models and social media in financial management," Annals of Operations Research, Springer, vol. 348(3), pages 1489-1511, May.
  • Handle: RePEc:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-022-05136-x
    DOI: 10.1007/s10479-022-05136-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-05136-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-022-05136-x?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.

    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:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-022-05136-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.