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Open AccessArticle

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

1
Haas School of Business, University of California, Berkeley, CA 94720, USA
2
Reserve Bank of India, Mumbai, Maharashtra 400001, India
3
Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(2), 23; https://doi.org/10.3390/jrfm13020023
Received: 21 December 2019 / Revised: 26 January 2020 / Accepted: 27 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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. View Full-Text
Keywords: Bitcoin; trading strategy; artificial intelligence; cryptocurrency; neural networks; time series analysis; deep learning; predictive model; risk management Bitcoin; trading strategy; artificial intelligence; cryptocurrency; neural networks; time series analysis; deep learning; predictive model; risk management
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Dutta, A.; Kumar, S.; Basu, M. A Gated Recurrent Unit Approach to Bitcoin Price Prediction. J. Risk Financial Manag. 2020, 13, 23.

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