About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore

Research Article

A Comparative Study of Bitcoin and Gold Investments

Download9 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/eai.22-5-2026.2365149,
        author={Yi  Liu},
        title={A Comparative Study of Bitcoin and Gold Investments},
        proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore},
        publisher={EAI},
        proceedings_a={ICIAAI},
        year={2026},
        month={8},
        keywords={Gold Bitcoin LSTM},
        doi={10.4108/eai.22-5-2026.2365149}
    }
    
  • Yi Liu
    Year: 2026
    A Comparative Study of Bitcoin and Gold Investments
    ICIAAI
    EAI
    DOI: 10.4108/eai.22-5-2026.2365149
Yi Liu1,*
  • 1: Data Science, Zhejiang University of Finance & Economics, Zhejiang, China
*Contact email: nishiyao@ldy.edu.rs

Abstract

The investment industry has changed a great deal, with commodity and virtual currency investments presenting sharp returns, therefore, requiring comparative analysis to make sound investment decisions. In this paper, we will examine gold and Bitcoin on four fronts, namely the way their prices change over time, the relationship of the two, how far their values impact the market, and the extent to which we can predict their prices of them based on LSTM neural networks. Whereas gold shows low volatility and a low but constant increase in prices, Bitcoin shows high overall increases but with high volatility. There is a high positive correlation between the two products in terms of price (0.83), and closeness to independence in returns (0.11), and gold prices are easier to predict on LSTM models than Bitcoin. These results will be useful in diversifying portfolios and making investment decisions.

Keywords
Gold, Bitcoin, LSTM
Published
2026-08-31
Publisher
EAI
http://dx.doi.org/10.4108/eai.22-5-2026.2365149
Copyright © 2026–2026 EAI
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center
  • Cookie Preferences

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL