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Machine Learning-Based Approach for Predicting the Altcoins Price Direction Change from a High-Frequency Data of Seven Years Based on Socio-Economic Factors, Bitcoin Prices, Twitter and News Sentiments

Author

Listed:
  • Anamika Gupta

    (S.S. College of Business Studies, University of Delhi)

  • Gaurav Pandey

    (IIT Jodhpur
    Analyttica Datalab)

  • Rajan Gupta

    (Analyttica Datalab
    Autonomous University of Tamaulipas)

  • Smaran Das

    (S.S. College of Business Studies, University of Delhi)

  • Ajmera Prakash

    (S.S. College of Business Studies, University of Delhi)

  • Kartik Garg

    (S.S. College of Business Studies, University of Delhi)

  • Shreyan Sarkar

    (S.S. College of Business Studies, University of Delhi)

Abstract

Altcoins are alternative types of coins under cryptocurrency, apart from traditional Bitcoins, for which predicting the price movement presents a multifaceted challenge deeply rooted in the volatile nature of the cryptocurrency market. This study compares and analyzes different Machine Learning (ML) and Deep Learning (DL) models for price movement prediction through diverse data sources like Bitcoin prices, social media sentiments, and news sentiments, apart from different socio-economic factors specific to USA geography due to its maturity on use of Altcoins, with temporal scope spanning from 2016 to 2022 collating over 77 M tweets and news items. Ethereum, Binance, XRP, Cardano, Monero, Tron, Stellar, and Litecoin, were considered for experimentation across widely used algorithms like Gradient Boosting, Naive Bayes, Decision Trees, Neural Networks, and the like, with different day-length lags ranging up to 4 days. Highly relevant features were selected using Random Forest selection method and highly correlated features have been removed before the modeling. Accuracy for price movement prediction models varied from 71.03% for Ethereum to 66.14% for Stellar, which were better by 15–20% as compared to percentage benchmarking done by literature to be ranging around 50 s and 60 s. For the model validation, sensitivity analysis involving day-wise lag analysis, and different data splits (based on size and months) were considered, which was stable for the high performing models. Further, an interesting result was observed during the study. In order of priority, Bitcoin prices, social media sentiments, and news sentiments significantly impact altcoin price movement. This implies that by studying the Bitcoin price movement and market sentiments, investors can make wise decisions towards altcoin investments. This study holds significance for researchers and practitioners to understand the impact in the trading market of cryptocurrency and help an investor diversify their portfolio. The findings will be helpful for Algo Trading Platforms, Financial Advisors, Trading Experts, Industry Experts, Researchers, and Scholars.

Suggested Citation

  • Anamika Gupta & Gaurav Pandey & Rajan Gupta & Smaran Das & Ajmera Prakash & Kartik Garg & Shreyan Sarkar, 2024. "Machine Learning-Based Approach for Predicting the Altcoins Price Direction Change from a High-Frequency Data of Seven Years Based on Socio-Economic Factors, Bitcoin Prices, Twitter and News Sentiment," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2981-3026, November.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-023-10538-5
    DOI: 10.1007/s10614-023-10538-5
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    References listed on IDEAS

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    1. Imran Yousaf & Shoaib Ali, 2020. "Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    2. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    3. José Almeida & Tiago Cruz Gonçalves, 2022. "A Systematic Literature Review of Volatility and Risk Management on Cryptocurrency Investment: A Methodological Point of View," Risks, MDPI, vol. 10(5), pages 1-18, May.
    4. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    5. Fernando E. Alvarez & David Argente & Diana Van Patten, 2022. "Are Cryptocurrencies Currencies? Bitcoin as Legal Tender in El Salvador," NBER Working Papers 29968, National Bureau of Economic Research, Inc.
    6. Bouri, Elie & Lei, Xiaojie & Jalkh, Naji & Xu, Yahua & Zhang, Hongwei, 2021. "Spillovers in higher moments and jumps across US stock and strategic commodity markets," Resources Policy, Elsevier, vol. 72(C).
    7. 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.
    8. Syed Jawad Hussain Shahzad & Elie Bouri & Sang Hoon Kang & Tareq Saeed, 2021. "Regime specific spillover across cryptocurrencies and the role of COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    9. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
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