A Comparative Study of Bitcoin’s Price Prediction Using Regression Models
Ibrahim Adamu
*
Department of Statistics, Federal University of Technology, Owerri, Imo State Nigeria.
Chukwudi Justin Ogbonna
Department of Statistics, Federal University of Technology, Owerri, Imo State Nigeria.
Success Ogechi Ubah
Department of Statistics, Federal University of Technology, Owerri, Imo State Nigeria.
Alumbugu Auta Irinews
Department of Statistics, Federal Polytechnic Nasarawa, Nasarawa State, Nigeria.
Aminu Muhammad
Department of Mathematical Sciences, Federal University Gusau, Zamfara State, Nigeria.
Shuaibu Ahmed
Department of Public Administration, Federal Polytechnic Ayede, Oyo State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
The rising popularity and increasing financial acceptance of cryptocurrency are having a profound impact on global scale. Unlike the current fiat currencies, bitcoins offer a unique possibility to predict their price. Despite the fact that many individuals are investing in cryptocurrencies, little is known about their dynamic properties and predictability, which puts money at risk. The aim of this paper is to evaluate and compare different regression algorithms in order to forecast the price of most popular cryptocurrency – Bitcons. Secondary bitcoin historical data from Kaggle which features an updated daily record of 24 variables over a seven-year period ARE considered. Since the bitcoin data is so volatile, we implemented an effective pre-processing of data in order to have a better prediction result. The different models applied include – Linear Regression, Ridge Regression, LASSO Regression and Elastic Net Regression model. However, elastic net performed better with an RMSE of 0.0228 without showing signs of overfitting.
Keywords: Cryptocurrency, linear regression, ridge regression, LASSO regression and elastic net regression