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Predictive Analytics Approaches to Modeling Bitcoin Prices During Periods of Financial Uncertainty

Author

Listed:
  • Ahmad F. Vakil

    (Department of Business Analytics and Information Systems, The Tobin College of Business, St. John’s University, Queens, NY 11439, USA)

  • Manuel Russon

    (Department of Business Analytics and Information Systems, The Tobin College of Business, St. John’s University, Queens, NY 11439, USA)

  • Victor Lu

    (Department of Business Analytics and Information Systems, The Tobin College of Business, St. John’s University, Queens, NY 11439, USA)

Abstract

The decentralized nature of cryptocurrencies makes them an appealing choice for investors around the world in times of uncertainty. Recent global health concerns, political conflicts, and subsequent economic issues have contributed to uncertainty in financial markets worldwide. In the first part of this study, we examine the recent changes in the price of Bitcoin and explore the relationship between various financial factors and the price of Bitcoin. Previous studies have been inconclusive in establishing a linear relationship between Bitcoin and various market indexes, including the S&P 500, Dow Jones Industrial Average, Nasdaq 100, Russell 2000, and Nikkei 225, as well as the prices of commodities such as Gold and Oil. In this study, to address the issue of uncertainty due to the volatility of the stock market and its effect on the Bitcoin price, we examine the relationship between the popular measure of the stock market’s expectation of volatility based on the S&P 500 index (VIX) and Bitcoin. Some non-linear analytical methods are employed to examine the impact of the aforementioned indices, commodities, and financial uncertainty measures on Bitcoin prices. This study utilizes different time frames, including weekly and monthly data from 1 January 2016 to 1 March 2026. In the second part of this study, Principal Component Analysis is utilized. Since the suggested analytical models are based on correlated financial factors, Principal Component Analysis will be used to address this issue while maintaining the high explanatory power of our suggested models.

Suggested Citation

  • Ahmad F. Vakil & Manuel Russon & Victor Lu, 2026. "Predictive Analytics Approaches to Modeling Bitcoin Prices During Periods of Financial Uncertainty," FinTech, MDPI, vol. 5(3), pages 1-18, July.
  • Handle: RePEc:gam:jfinte:v:5:y:2026:i:3:p:66-:d:2000844
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