IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v2y2023i1p11-169d1098889.html
   My bibliography  Save this article

An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments

Author

Listed:
  • Man-Fai Leung

    (School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK)

  • Lewis Chan

    (School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong 999077, China)

  • Wai-Chak Hung

    (School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong 999077, China)

  • Siu-Fung Tsoi

    (School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong 999077, China)

  • Chun-Hin Lam

    (School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong 999077, China)

  • Yiu-Hang Cheng

    (School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong 999077, China)

Abstract

The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visualization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals.

Suggested Citation

  • Man-Fai Leung & Lewis Chan & Wai-Chak Hung & Siu-Fung Tsoi & Chun-Hin Lam & Yiu-Hang Cheng, 2023. "An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments," FinTech, MDPI, vol. 2(1), pages 1-17, March.
    • Handle: RePEc:gam:jfinte:v:2:y:2023:i:1:p:11-169:d:1098889
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/2/1/11/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2674-1032/2/1/11/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    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. Gerild QORDIA & Dolantina HYKA, 2024. "The benefits of using IPA in relation to RPA for the cryptocurrency sector, in making decisions on their sale and purchase in the stock market," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 8(2), pages 31-38, February.

    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. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    3. Xiaotong Sun & Charalampos Stasinakis & Georigios Sermpinis, 2022. "Decentralization illusion in Decentralized Finance: Evidence from tokenized voting in MakerDAO polls," Papers 2203.16612, arXiv.org, revised Mar 2023.
    4. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.
    5. A. V. Biju & Aparna Merin Mathew & P. P. Nithi Krishna & M. P. Akhil, 2022. "Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis," Digital Finance, Springer, vol. 4(4), pages 275-290, December.
    6. Voraprapa Nakavachara & Roongkiat Ratanabanchuen & Kanis Saengchote & Thitiphong Amonthumniyom & Pongsathon Parinyavuttichai & Polpatt Vinaibodee, 2023. "Do People Gamble or Invest in the Cryptocurrency Market? Transactional-Level Evidence from Thailand," PIER Discussion Papers 206, Puey Ungphakorn Institute for Economic Research, revised Feb 2024.
    7. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    8. Koch, Sophia & Dimpfl, Thomas, 2023. "Attention and retail investor herding in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 51(C).
    9. Andreas Hackethal & Tobin Hanspal & Dominique M Lammer & Kevin Rink, 2022. "The Characteristics and Portfolio Behavior of Bitcoin Investors: Evidence from Indirect Cryptocurrency Investments [The investor in structured retail products: advice driven or gambling oriented]," Review of Finance, European Finance Association, vol. 26(4), pages 855-898.
    10. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    11. Jacques Vella Critien & Albert Gatt & Joshua Ellul, 2022. "Bitcoin price change and trend prediction through twitter sentiment and data volume," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    12. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    13. Tristan Lim, 2022. "Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning," Papers 2211.01346, arXiv.org, revised Jan 2023.
    14. Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.
    15. Huynh, Nhan & Phan, Hoa, 2023. "Emotions in the crypto market: Do photos really speak?," Finance Research Letters, Elsevier, vol. 55(PB).
    16. Bouri, Elie & Christou, Christina & Gupta, Rangan, 2022. "Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models," Finance Research Letters, Elsevier, vol. 49(C).
    17. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    18. N. L. Balasudarsun & Bikramaditya Ghosh & Sathish Mahendran, 2022. "Impact of Negative Tweets on Diverse Assets during Stressful Events: An Investigation through Time-Varying Connectedness," JRFM, MDPI, vol. 15(6), pages 1-12, June.
    19. Cole, Benjamin M. & Dyhrberg, Anne H. & Foley, Sean & Svec, Jiri, 2022. "Can Bitcoin be Trusted? Quantifying the economic value of blockchain transactions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    20. Hadhri, Sinda, 2023. "Do cryptocurrencies feel the music?," International Review of Financial Analysis, Elsevier, vol. 89(C).

    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:gam:jfinte:v:2:y:2023:i:1:p:11-169:d:1098889. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.