<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.</p>
</abstract>

This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.

Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

Electronic & Computer Engineering Department

Annals of Science and Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were...

Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

Data publikacji: 07 gru 2021

Zakres stron: 47 - 56

Otrzymano: 03 wrz 2021

Przyjęty: 09 lis 2021

DOI: https://doi.org/10.2478/ast-2021-0009

Słowa kluczowe
ARIMA Model, Bitcoin Forecast, Short-term Prediction, Time Series

© 2021 Olufunke G. Darley et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

Data publikacji: 07 gru 2021

Zakres stron: 47 - 56

Otrzymano: 03 wrz 2021

Przyjęty: 09 lis 2021

DOI: https://doi.org/10.2478/ast-2021-0009

Słowa kluczoweARIMA Model, Bitcoin Forecast, Short-term Prediction, Time Series

© 2021 Olufunke G. Darley et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
ARIMA Model, Bitcoin Forecast, Short-term Prediction, Time Series