期刊
FINANCE RESEARCH LETTERS
卷 40, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.frl.2020.101755
关键词
Bitcoin price prediction; Stacked denoising autoencoders; Feature learning; Deep extraction
资金
- National Natural Science Foundation of China [71971207, 71901204, 71425002, 71601178]
- Youth Innovation Promotion Association of Chinese Academy of Sciences [2017200]
After constructing a feature system with 40 determinants affecting the price of Bitcoin, a deep learning method called SDAE was used for prediction. The SDAE model outperformed traditional methods such as BPNN and SVR in both directional and level prediction accuracy.
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).
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