Bitcoin Price Projections Using Geometric Brownian Motion: An Empirical Study of 2020–2024

A. Gbolahan Idowu, O. Idris Rahman and A. Iyinoluwa Balogun
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A. Gbolahan Idowu: Department of Mathematics, Lagos State University, Lagos, Nigeria
O. Idris Rahman: Department of Mathematics, Lagos State University, Lagos, Nigeria
A. Iyinoluwa Balogun: Department of Mathematics, Lagos State University, Lagos, Nigeria

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 4, 927-935

Abstract: This study employs the Geometric Brownian Motion (GBM) model to forecast Bitcoin prices from 2020 to 2024. Given Bitcoin’s notorious volatility and unpredictability, accurate forecasting models are essential for investors, traders, and policymakers. The GBM model, known for its ability to capture stochastic price movements, is applied to historical Bitcoin price data to estimate drift and volatility parameters. The results indicate that the GBM model provides moderate predictive accuracy, with a mean absolute error (MAE) of 10% and a root mean squared percentage error (RMSPE) of 15%. Although the model captures the general trend of Bitcoin prices, it underestimates extreme price movements during periods of high volatility. The study concludes that the GBM model is a useful tool for forecasting Bitcoin prices, although its limitations should be considered in risk management and investment strategies.

Date: 2025
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