Skip to main content
SpringerLink
Log in

STOCK: A User-Friendly Stock Prediction and Sentiment Analysis System

(Demonstration Paper)

  • Conference paper
  • First Online:
Research Challenges in Information Science (RCIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 415))

Included in the following conference series:

  • 1251 Accesses

Abstract

Determining a future value of a company in order to find a good target for investing is a critical and complex task for stock marketers. And it is even more complicated for non-experts.

STOCK is a modular, scalable, and extensible framework that enables users to gain insight in the stock market by user-friendly combination of three sources of information: (1) An easy access to the companies’ current position and evolution of prices. (2) Prediction models and their customisation according to users’ needs and interests, regardless of their knowledge in the field. (3) Results of sentiment analysis of related news that may influence the respective changes in prices.

Supported by the SVV project no. 260588.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/Rinqt/stock

  2. 2.

    https://github.com/codingupastorm/vadersharp

References

  1. Hungary, W.: Wallet Investor (2020). https://walletinvestor.com/

  2. Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: ICWSM 2014. The AAAI Press (2014)

    Google Scholar 

  3. MarketWatch, I.: MarketWatch (2020). https://www.marketwatch.com/

  4. Ryan, T.P.: Modern Regression Methods. Wiley (2008)

    Google Scholar 

  5. Sherstinsky, A.: Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Physica D: Nonlinear Phenomena 404 (2020)

    Google Scholar 

  6. Wallmine.com: Wallmine (2020). https://wallmine.com/

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Ilda Balliu, Harun Ćerim, Mahran Emeiri, Kaan Yöş & Irena Holubová

Authors
  1. Ilda Balliu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harun Ćerim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahran Emeiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaan Yöş
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Irena Holubová
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irena Holubová .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Samira Cherfi

  2. Fondazione Bruno Kessler, Trento, Italy

    Anna Perini

  3. University Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Balliu, I., Ćerim, H., Emeiri, M., Yöş, K., Holubová, I. (2021). STOCK: A User-Friendly Stock Prediction and Sentiment Analysis System. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-75018-3_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75017-6

  • Online ISBN: 978-3-030-75018-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation