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Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends

Author

Listed:
  • Mansour Davoudi

    (Shiraz University)

  • Mina Ghavipour

    (Amirkabir University of Technology)

  • Morteza Sargolzaei-Javan

    (Amirkabir University of Technology)

  • Saber Dinparast

    (Urmia University)

Abstract

Cryptocurrencies, recognized for their transformative impact on both emerging economies and the global financial landscape, are increasingly integral to investment strategies due to their widespread adoption and significant market volatility driven by socio-political news. This study analyzes the price trends of four major cryptocurrencies in decentralized storage—Filecoin, Arweave, Storj, and Siacoin—using a novel approach that combines network analysis, textual analysis, and market analysis. By constructing a network of relevant entities, summarizing pertinent news articles, assessing sentiment with the FinBert model, and evaluating financial market data through transformer encoders, our methodology provides a comprehensive analysis of factors influencing cryptocurrency prices. The integration of these analyses enables us to predict the price trends of the examined cryptocurrencies with accuracies of 76% for Filecoin, 83% for Storj, 61% for Arweave, and 74% for Siacoin, highlighting the model's effectiveness in navigating the complexities of the cryptocurrency market.

Suggested Citation

  • Mansour Davoudi & Mina Ghavipour & Morteza Sargolzaei-Javan & Saber Dinparast, 2025. "Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2919-2964, May.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10664-8
    DOI: 10.1007/s10614-024-10664-8
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    References listed on IDEAS

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