The Rise and Fall of Bitcoin: Predicting Market Direction Using Machine Learning Models

Abstract

Bitcoin's dominance in the cryptocurrency market has only increased in recent years. However, it experiences rapid spikes and declines that creates difficulty in predicting its future behavior. Much research has been done to find efficient models that predict with high accuracy, but with limited results. The goal of this study was to determine if higher accuracy can be achieved by focusing on a broader perspective of numeric ranges as opposed to specific time series price predictions. The predictions were concentrated on reporting the expected market direction for the following hour. In using one hour interval trading data and creating discrete classes of levels of hourly changes, five different Machine Learning models were trained and tested. Except for one model, cross validation accuracy ranging from 96-100% was achieved.