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

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.

Authors

Publication Details

Journal
International Journal of Research and Innovation in Applied Science
Published
2025-01-01
DOI
https://doi.org/10.51584/ijrias.2025.10040078
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Adegbola Balogun, A. Gbolahan Idowu, O. Idris Rahman
International Journal of Research and Innovation in Applied Science
Blockchain Technology Applications and Security
article

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

Adegbola Balogun, A. Gbolahan Idowu, O. Idris Rahman
article en

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.

International Journal of Research and Innovation in Applied ScienceVol. X(IV)
Openalex Percentile: Top 85%
Blockchain Technology Applications and Security
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