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Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model

E3S Web of Conferences, 2020, Vol.214, p.2040 [Peer Reviewed Journal]

2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2267-1242 ;ISSN: 2555-0403 ;EISSN: 2267-1242 ;DOI: 10.1051/e3sconf/202021402040

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  • Title:
    Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model
  • Author: Wang, Feiyu
  • Ahn, Y. ; Wu, F.
  • Subjects: Artificial neural networks ; Learning algorithms ; Machine learning ; Neural networks ; Regression analysis ; Stock exchanges ; Support vector machines
  • Is Part Of: E3S Web of Conferences, 2020, Vol.214, p.2040
  • Description: The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy.
  • Publisher: Les Ulis: EDP Sciences
  • Language: English
  • Identifier: ISSN: 2267-1242
    ISSN: 2555-0403
    EISSN: 2267-1242
    DOI: 10.1051/e3sconf/202021402040
  • Source: EDP Open
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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