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Gold and Bitcoin Optimal Portfolio Research and Analysis Based on Machine-Learning Methods

Author

Listed:
  • Jingjing Li

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China)

  • Xinge Rao

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China)

  • Xianyi Li

    (International Business School Suzhou at XJTLU, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Sihai Guan

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China)

Abstract

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.

Suggested Citation

  • Jingjing Li & Xinge Rao & Xianyi Li & Sihai Guan, 2022. "Gold and Bitcoin Optimal Portfolio Research and Analysis Based on Machine-Learning Methods," Sustainability, MDPI, vol. 14(21), pages 1-12, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14659-:d:965944
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    References listed on IDEAS

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    1. Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
    2. Ziaul Haque Munim & Mohammad Hassan Shakil & Ilan Alon, 2019. "Next-Day Bitcoin Price Forecast," JRFM, MDPI, vol. 12(2), pages 1-15, June.
    3. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    4. Prosper Lamothe-Fernández & David Alaminos & Prosper Lamothe-López & Manuel A. Fernández-Gámez, 2020. "Deep Learning Methods for Modeling Bitcoin Price," Mathematics, MDPI, vol. 8(8), pages 1-13, July.
    5. Jiang Yu & Yue Shang & Xiafei Li & Dehua Shen, 2021. "Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-20, September.
    6. Adam S. Hayes, 2019. "Bitcoin price and its marginal cost of production: support for a fundamental value," Applied Economics Letters, Taylor & Francis Journals, vol. 26(7), pages 554-560, April.
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