A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Aniruddha Dutta, Saket Kumar, Meheli Basu

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

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 forecasting 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 the gated recurring unit (GRU) model with recurrent dropout performs 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.

Original languageEnglish (US)
Article number23
JournalJournal of Risk and Financial Management
Volume13
Issue number2
DOIs
StatePublished - Feb 2020
Externally publishedYes

Keywords

  • Bitcoin
  • artificial intelligence
  • cryptocurrency
  • deep learning
  • neural networks
  • predictive model
  • risk management
  • time series analysis
  • trading strategy

ASJC Scopus subject areas

  • Accounting
  • Business, Management and Accounting (miscellaneous)
  • Finance
  • Economics and Econometrics

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