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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
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    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).
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    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, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 8(2), pages 31-38, February.

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