IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i2d10.1007_s10660-019-09389-w.html
   My bibliography  Save this article

Research and analysis of deep learning algorithms for investment decision support model in electronic commerce

Author

Listed:
  • Zhizhong Lei

    (Liaoning University)

Abstract

In order to improve the accuracy of e-commerce decision-making, this paper proposes an investment decision-making support model in e-commerce based on deep learning calculation to support the company. Investment decision-making system is not only an important means of enterprise investment and financing, but also an important way for investors to make profits. It also plays an important role in macroeconomic regulation, resource allocation and other aspects. This paper takes investment data related to Internet and e-commerce business as the research object, studies the theory and method of investment decision-making quality evaluation at home and abroad, and puts forward a prediction model of company decision-making quality evaluation based on deep learning algorithm, aiming at providing decision support for investors. Then a neural network investment quality evaluation model is constructed, including model structure, parameters and algorithm design. The experimental data are input into training, and the data processing process and prediction results are displayed. Experiments show that the evaluation indexes of prediction model is mainly used to judge the quality of investment of Internet or commercial enterprises. Based on this deep learning model, various index data of enterprises are analyzed, which can assist investors in decision-making.

Suggested Citation

  • Zhizhong Lei, 2020. "Research and analysis of deep learning algorithms for investment decision support model in electronic commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 275-295, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09389-w
    DOI: 10.1007/s10660-019-09389-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-019-09389-w
    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/s10660-019-09389-w?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. Guanlian Xiao & Willem van Jaarsveld & Ming Dong & Joris van de Klundert, 2018. "Models, algorithms and performance analysis for adaptive operating room scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1389-1413, February.
    2. Ismahene Aouadni & Abdelwaheb Rebai, 2017. "Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction," Annals of Operations Research, Springer, vol. 256(1), pages 3-20, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    2. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.

    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. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
    2. Sean Harris & David Claudio, 2022. "Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review," SN Operations Research Forum, Springer, vol. 3(1), pages 1-42, March.
    3. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Panos M. Pardalos, 2023. "Scheduling operating rooms of multiple hospitals considering transportation and deterioration in mass-casualty incidents," Annals of Operations Research, Springer, vol. 321(1), pages 717-753, February.
    4. Sebastián Rodríguez & Pablo Cabrera-Barona, 2024. "A machine learning-based assessment of subjective quality of life," Journal of Computational Social Science, Springer, vol. 7(1), pages 451-467, April.
    5. Yasemin Merzifonluoglu & Joseph Geunes, 2021. "The Risk-Averse Static Stochastic Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 931-948, July.
    6. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.

    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:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09389-w. 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: 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.