Abstract

Bitcoin stands as an immensely volatile cryptocurrency gaining increasing popularity. It signifies a pivotal shift in the perception of currency as the world’s most valuable and costly cryptocurrency. This study aims to forecast the daily return on Bitcoin. Historical data of Bitcoin prices from 24/5/2020-23/5/2023 was collected and forecasted the Bitcoin return for a short 8 days (24/5/2023-31/5/2023). The objective was to validate the practicality of the traditional univariate Autoregressive Integrative Moving Average (ARIMA) model in predicting Bitcoin prices. We successfully projected the closing prices of Bitcoin for the initial seven days of May 2023. Bitcoin’s value fluctuates similarly to a stock, but different in its features. The preprocessing stages were stationary tests using an Augmented Dickey-Fuller Unit Root Test, Jarque-Bera Test, and Lagrange Multiplier Serial Correlation Test utilizing EViews software with series line graph, Q-Q plot, histogram. The selection of potential model was selected through the utilization of the correlogram test looking at the ACF and PACF graph. This study used the ARIMA model and has chosen ARMA (1, 0) as the forecasting model based on the readings on Akaike Information Criterion (AIC), Schwarz Criterion (SIC), and Hannan-Quinn Criterion (HQC), Mean Absolute Error, was run as an accuracy measurement. Our study shows 2 findings, where (1) the forecasted daily return between 24/5/2023-31/5/2023 shows constant return of 0.1%, leading to an annual return of 36.5%; (2) the forecast of using ARMA (1, 0) model is weak in its accuracy.

Keywords: bitcoin, cryptocurrency, prediction, auto regressive integrated moving average, ARIMA

 

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