A Gated Recurrent Unit Approach to Bitcoin Price Prediction

TL;DR

This paper explores the use of a Gated Recurrent Unit (GRU) neural network with recurrent dropout for predicting Bitcoin prices, demonstrating improved accuracy and potential for profitable trading strategies.

Contribution

It introduces a novel application of GRU with recurrent dropout for Bitcoin price prediction, outperforming existing models in accuracy.

Findings

GRU with recurrent dropout outperforms other models in RMSE

Simple trading strategies based on GRU predictions can be profitable

The proposed framework effectively incorporates exogenous and endogenous factors

Abstract

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.