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Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables

Author

Listed:
  • Ahmed Ibrahim

    (Computer Science Department, Wilfried Laurier University, Waterloo, ON N2L 3C5, Canada)

  • Rasha Kashef

    (Electrical, Computer, and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada)

  • Menglu Li

    (Electrical, Computer, and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada)

  • Esteban Valencia

    (IVEY Business School, Management Science Department, London, ON N6G 0N1, Canada)

  • Eric Huang

    (Advanced Analytics and Research Lab, Toronto, ON M5J 2P1, Canada)

Abstract

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.

Suggested Citation

  • Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:189-:d:401211
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    References listed on IDEAS

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    Cited by:

    1. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    2. Yihang Fu & Mingyu Zhou & Luyao Zhang, 2024. "DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting," Papers 2405.00522, arXiv.org.
    3. Jong-Min Kim & Chanho Cho & Chulhee Jun, 2022. "Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model," JRFM, MDPI, vol. 15(2), pages 1-10, February.
    4. Stephen Chan & Jeffrey Chu & Yuanyuan Zhang & Saralees Nadarajah, 2020. "Blockchain and Cryptocurrencies," JRFM, MDPI, vol. 13(10), pages 1-3, September.
    5. Manlika Ratchagit & Honglei Xu, 2022. "A Two-Delay Combination Model for Stock Price Prediction," Mathematics, MDPI, vol. 10(19), pages 1-21, September.
    6. Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    7. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).

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