Forecasting the price of Bitcoin using deep learning

【Author】 Liu, Mingxi; Li, Guowen; Li, Jianping; Zhu, Xiaoqian; Yao, Yinhong

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】After constructing a feature system with 40 determinants that affect the price of Bitcoin considering aspects of the cryptocurrency market, public attention, and the macroeconomic environment, a deep learning method named stacked denoising autoencoders (SDAE) is utilized to predict the price of Bitcoin. The results show that compared with the most popular machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR) methods, the SDAE model performs better in both directional and level prediction, measured using commonly used indicators, i.e., mean absolute percentage error (MAPE), root mean squared error (RMSE), and directional accuracy (DA).

【Keywords】Bitcoin price prediction; Stacked denoising autoencoders; Feature learning; Deep extraction

【发表时间】2021 MAY

【收录时间】2022-01-02

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