A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Aniruddha Dutta, Saket Kumar and Meheli Basu
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Aniruddha Dutta: Haas School of Business, University of California, Berkeley, CA 94720, USA
Saket Kumar: Haas School of Business, University of California, Berkeley, CA 94720, USA
Meheli Basu: Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA

JRFM, 2020, vol. 13, issue 2, 1-16

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.

Keywords: Bitcoin; trading strategy; artificial intelligence; cryptocurrency; neural networks; time series analysis; deep learning; predictive model; risk management (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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