IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2403.12161.html
   My bibliography  Save this paper

Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond

Author

Listed:
  • Arijit Das
  • Tanmoy Nandi
  • Prasanta Saha
  • Suman Das
  • Saronyo Mukherjee
  • Sudip Kumar Naskar
  • Diganta Saha

Abstract

Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are: a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.

Suggested Citation

  • Arijit Das & Tanmoy Nandi & Prasanta Saha & Suman Das & Saronyo Mukherjee & Sudip Kumar Naskar & Diganta Saha, 2024. "Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond," Papers 2403.12161, arXiv.org.
    • Handle: RePEc:arx:papers:2403.12161
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2403.12161
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ashwini Saini & Anoop Sharma, 2022. "Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market," Annals of Data Science, Springer, vol. 9(4), pages 791-799, August.
    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.

      More about this item

      NEP fields

      This paper has been announced in the following NEP Reports:

      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:arx:papers:2403.12161. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

      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.