A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin

Zi Ye, Yinxu Wu, Hui Chen, Yi Pan and Qingshan Jiang
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Zi Ye: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yinxu Wu: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Hui Chen: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yi Pan: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Qingshan Jiang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Mathematics, 2022, vol. 10, issue 8, 1-21

Abstract: Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.

Keywords: cryptocurrencies; forecasting model; financial technology; ensemble learning; Bitcoin price prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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